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Nikolaou N, Salazar D, RaviPrakash H, Gonçalves M, Mulla R, Burlutskiy N, Markuzon N, Jacob E. A machine learning approach for multimodal data fusion for survival prediction in cancer patients. NPJ Precis Oncol 2025; 9:128. [PMID: 40325104 PMCID: PMC12053085 DOI: 10.1038/s41698-025-00917-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2024] [Accepted: 04/19/2025] [Indexed: 05/07/2025] Open
Abstract
Technological advancements of the past decade have transformed cancer research, improving patient survival predictions through genotyping and multimodal data analysis. However, there is no comprehensive machine-learning pipeline for comparing methods to enhance these predictions. To address this, a versatile pipeline using The Cancer Genome Atlas (TCGA) data was developed, incorporating various data modalities such as transcripts, proteins, metabolites, and clinical factors. This approach manages challenges like high dimensionality, small sample sizes, and data heterogeneity. By applying different feature extraction and fusion strategies, notably late fusion models, the effectiveness of integrating diverse data types was demonstrated. Late fusion models consistently outperformed single-modality approaches in TCGA lung, breast, and pan-cancer datasets, offering higher accuracy and robustness. This research highlights the potential of comprehensive multimodal data integration in precision oncology to improve survival predictions for cancer patients. The study provides a reusable pipeline for the research community, suggesting future work on larger cohorts.
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Affiliation(s)
- Nikolaos Nikolaou
- Oncology Data Science, Oncology R&D, AstraZeneca, Cambridge, UK
- Department of Physics & Astronomy, University College London, London, UK
| | - Domingo Salazar
- Oncology Data Science, Oncology R&D, AstraZeneca, Cambridge, UK
| | | | | | - Rob Mulla
- Oncology Data Science, Oncology R&D, AstraZeneca, Waltham, MA, USA
| | | | - Natasha Markuzon
- Oncology Data Science, Oncology R&D, AstraZeneca, Waltham, MA, USA.
| | - Etai Jacob
- Oncology Data Science, Oncology R&D, AstraZeneca, Waltham, MA, USA.
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Zhi Y, Wu J, Li R, Chang X, Liu S, Lu W, Zheng M, Liu B, Chen J, Zhang X, Huang Y. A combination of hepatic leukemia factor and circulating tumor cells serve as effective biomarkers for lung adenocarcinoma prognosis. PeerJ 2025; 13:e19092. [PMID: 40124619 PMCID: PMC11927566 DOI: 10.7717/peerj.19092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Accepted: 02/11/2025] [Indexed: 03/25/2025] Open
Abstract
Background Lung adenocarcinoma (LUAD) is a highly malignant tumor with the highest mortality rate among all cancers. Early diagnosis and prognosis are important factors in treatment. Hepatic leukemia factor (HLF) is thought to be closely associated with lung cancer metastasis. It is downregulated in lung cancer tissues and negatively correlated with the number of metastasis-activating circulating tumor cells (CTCs) in the peripheral blood of patients. Method and Results In this study, we analyzed data from LUAD samples in TCGA and found that HLF was significantly upregulated in samples with EGFR mutations. Immunohistochemical (IHC) staining of 343 clinical samples also revealed a trend of HLF upregulation in patients with EGFR mutations. EGFR is one of the driver genes in non-small cell lung cancer (NSCLC), and the proportion in LUAD is as high as 50% in the East Asian population. In this study, EGFR mutation was not significantly correlated with the prognosis of LUAD patients and the number of CTC was also not related to EGFR mutation, but was closely related to HLF expression, with more CTCs being captured in the peripheral blood of patients with low expression of HLF (SI ≤ 4). By following up these 343 LUAD patients, high HLF expression (SI > 4) was found to be an independent protective factor for progression-free survival regardless of EGFR status (P < 0.001), whereas high CTC count (> 3) was an independent risk factor for recurrence or death in LUAD patients (P < 0.001). When low HLF and high CTCs coexisted, patients had the shortest median survival time. Patients with low HLF or high CTCs appeared alone had a moderate median survival time. Patients had the longest median survival time when HLF was high and CTCs were low. Conclusion In summary, we believe that HLF expression in cancer tissues and the number of CTCs can be used as effective biomarkers for predicting the prognosis of LUAD, which plays an important role in clinical diagnosis and prognosis judgment.
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Affiliation(s)
- Yaofeng Zhi
- Clinical Experimental Center, Jiangmen Engineering Technology Research Center of Clinical Biobank and Translational Research, Jiangmen Key Laboratory of Precision and Clinical Translation Medicine, Jiangmen Central Hospital, Jiangmen, Guangdong, China
| | - Jinhua Wu
- Department of Clinical Laboratory, Jiangmen Central Hospital, Jiangmen, Guangdong, China
| | - Ronggang Li
- Department of Pathology, Jiangmen Central Hospital, Jiangmen, Guangdong, China
| | - Xuefei Chang
- Clinical Experimental Center, Jiangmen Engineering Technology Research Center of Clinical Biobank and Translational Research, Jiangmen Key Laboratory of Precision and Clinical Translation Medicine, Jiangmen Central Hospital, Jiangmen, Guangdong, China
- Department of Pulmonary and Critical Care Medicine, Jiangmen Central Hospital, Jiangmen, Guangdong, China
| | - Silin Liu
- Clinical Experimental Center, Jiangmen Engineering Technology Research Center of Clinical Biobank and Translational Research, Jiangmen Key Laboratory of Precision and Clinical Translation Medicine, Jiangmen Central Hospital, Jiangmen, Guangdong, China
- Department of Pulmonary and Critical Care Medicine, Jiangmen Central Hospital, Jiangmen, Guangdong, China
| | - Wenjie Lu
- Clinical Experimental Center, Jiangmen Engineering Technology Research Center of Clinical Biobank and Translational Research, Jiangmen Key Laboratory of Precision and Clinical Translation Medicine, Jiangmen Central Hospital, Jiangmen, Guangdong, China
| | - Mingzhu Zheng
- Clinical Experimental Center, Jiangmen Engineering Technology Research Center of Clinical Biobank and Translational Research, Jiangmen Key Laboratory of Precision and Clinical Translation Medicine, Jiangmen Central Hospital, Jiangmen, Guangdong, China
| | - Baoyi Liu
- Clinical Experimental Center, Jiangmen Engineering Technology Research Center of Clinical Biobank and Translational Research, Jiangmen Key Laboratory of Precision and Clinical Translation Medicine, Jiangmen Central Hospital, Jiangmen, Guangdong, China
| | - Jiarong Chen
- Clinical Experimental Center, Jiangmen Engineering Technology Research Center of Clinical Biobank and Translational Research, Jiangmen Key Laboratory of Precision and Clinical Translation Medicine, Jiangmen Central Hospital, Jiangmen, Guangdong, China
- Department of Oncology, Jiangmen Central Hospital, Jiangmen, Guangdong, China
| | - Xin Zhang
- Clinical Experimental Center, Jiangmen Engineering Technology Research Center of Clinical Biobank and Translational Research, Jiangmen Key Laboratory of Precision and Clinical Translation Medicine, Jiangmen Central Hospital, Jiangmen, Guangdong, China
- Dongguan Key Laboratory of Medical Bioactive Molecular Developmental and Translational Research, Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Guangdong Medical University, Dongguan, Guangdong, China
- Collaborative Innovation Center for Antitumor Active Substance Research and Development, Guangdong Medical University, Dongguan, Guangdong, China
| | - Yanming Huang
- Clinical Experimental Center, Jiangmen Engineering Technology Research Center of Clinical Biobank and Translational Research, Jiangmen Key Laboratory of Precision and Clinical Translation Medicine, Jiangmen Central Hospital, Jiangmen, Guangdong, China
- Department of Pulmonary and Critical Care Medicine, Jiangmen Central Hospital, Jiangmen, Guangdong, China
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Polverino F, Mastrangelo A, Guarguaglini G. Contribution of AurkA/TPX2 Overexpression to Chromosomal Imbalances and Cancer. Cells 2024; 13:1397. [PMID: 39195284 PMCID: PMC11353082 DOI: 10.3390/cells13161397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2024] [Revised: 08/14/2024] [Accepted: 08/15/2024] [Indexed: 08/29/2024] Open
Abstract
The AurkA serine/threonine kinase is a key regulator of cell division controlling mitotic entry, centrosome maturation, and chromosome segregation. The microtubule-associated protein TPX2 controls spindle assembly and is the main AurkA regulator, contributing to AurkA activation, localisation, and stabilisation. Since their identification, AurkA and TPX2 have been described as being overexpressed in cancer, with a significant correlation with highly proliferative and aneuploid tumours. Despite the frequent occurrence of AurkA/TPX2 co-overexpression in cancer, the investigation of their involvement in tumorigenesis and cancer therapy resistance mostly arises from studies focusing only on one at the time. Here, we review the existing literature and discuss the mitotic phenotypes described under conditions of AurkA, TPX2, or AurkA/TPX2 overexpression, to build a picture that may help clarify their oncogenic potential through the induction of chromosome instability. We highlight the relevance of the AurkA/TPX2 complex as an oncogenic unit, based on which we discuss recent strategies under development that aim at disrupting the complex as a promising therapeutic perspective.
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Affiliation(s)
| | | | - Giulia Guarguaglini
- Institute of Molecular Biology and Pathology, National Research Council of Italy, c/o Sapienza University of Rome, Via degli Apuli 4, 00185 Rome, Italy; (F.P.); (A.M.)
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Zhang Y, Wang Y, Zhang R, Li Q. The prognostic and clinical value of genes associate with immunity and amino acid Metabolism in Lung Adenocarcinoma. Heliyon 2024; 10:e32341. [PMID: 39183890 PMCID: PMC11341317 DOI: 10.1016/j.heliyon.2024.e32341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 06/02/2024] [Accepted: 06/02/2024] [Indexed: 08/27/2024] Open
Abstract
Background Lung adenocarcinoma (LUAD) is the commonest subtype of primary lung cancer. A comprehensive analysis of the association of immunity with amino acid metabolism in LUAD is critical for understanding the disease. Methods The present study examined LUAD and noncancerous cases from the TCGA database. Differentially expressed genes (DEGs) between LUAD and noncancerous tissues were detected by analyzing processed expression profiles. We cross-referenced the up-regulated DEGs with Immune and Amino Acid Metabolism-related genes (I&AAMGs), resulting in Immune and Amino Acid Metabolism related differentially expressed genes (IAAAMRDEGs). The STRING database was employed to analyze PPI on IAAAMRDEGs, obtaining excavated hub genes, whose biological processes, molecular functions and cellular components were examined with GO/KEGG. Potential mechanisms related to LUAD were investigated by GSEA and GSVA. A prognostic model was built by LASSO-COX analysis, taking into consideration risk scores and prognostic factors to determine biomarkers affecting LUAD occurrence and prognosis. Results Totally 377 genes were detected at the intersection of upregulated DEGs and I&AAMGs. Analysis of PPI on these 377 IAAAMRDEGs yielded 17 hub genes. A LASSO regression analysis was utilized to assess the prognostic values of the 17 hub genes. Validation using the combined dataset confirmed 4 genes, e.g., polo-like kinase (PLK1), Ribonucleotide Reductase Subunit M2 (RRM2), Thyroid Hormone Receptor Interactor 13 (TRIP13), and Hyaluronan-Mediated Motility Receptor (HHMR). The model's accuracy was further assessed by ROC curve analysis and the COX model. In addition, immunohistochemical staining obtained from the HPA database, revealed enhanced PLK1 expression in LUAD samples. Conclusion LUAD pathogenesis is highly associated with immunity and amino acid metabolism. The PLK1, RRM2, TRIP13, and HMMR genes have prognostic values for LUAD. PLK1 upregulation in LUAD might be involved in tumorigenesis by modulating the cell cycle and represents a potential prognostic factor in clinic.
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Affiliation(s)
- Yuxin Zhang
- Beijing University of Chinese Medicine, No.11, North Third Ring East Road, Chaoyang District, Beijing, 100029, China
| | - Yuehui Wang
- Beijing University of Chinese Medicine, No.11, North Third Ring East Road, Chaoyang District, Beijing, 100029, China
| | - Ruoxuan Zhang
- Beijing University of Chinese Medicine, No.11, North Third Ring East Road, Chaoyang District, Beijing, 100029, China
| | - Quanwang Li
- Dongfang Hospital, Beijing University of Chinese Medicine, No. 6 fangxingyuan, Fengtai District, Beijing, 100078, China
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Zhang S, Liu X, Zhou L, Wang K, Shao J, Shi J, Wang X, Mu J, Gao T, Jiang Z, Chen K, Wang C, Wang G. Intelligent prognosis evaluation system for stage I-III resected non-small-cell lung cancer patients on CT images: a multi-center study. EClinicalMedicine 2023; 65:102270. [PMID: 38106558 PMCID: PMC10725055 DOI: 10.1016/j.eclinm.2023.102270] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/27/2023] [Accepted: 09/27/2023] [Indexed: 12/19/2023] Open
Abstract
Background Prognosis is crucial for personalized treatment and surveillance suggestion of the resected non-small-cell lung cancer (NSCLC) patients in stage I-III. Although the tumor-node-metastasis (TNM) staging system is a powerful predictor, it is not perfect enough to accurately distinguish all the patients, especially within the same TNM stage. In this study, we developed an intelligent prognosis evaluation system (IPES) using pre-therapy CT images to assist the traditional TNM staging system for more accurate prognosis prediction of resected NSCLC patients. Methods 20,333 CT images of 6371 patients from June 12, 2009 to March 24, 2022 in West China Hospital of Sichuan University, Mianzhu People's Hospital, Peking University People's Hospital, Chengdu Shangjin Nanfu Hospital and Guangan Peoples' Hospital were included in this retrospective study. We developed the IPES based on self-supervised pre-training and multi-task learning, which aimed to predict an overall survival (OS) risk for each patient. We further evaluated the prognostic accuracy of the IPES and its ability to stratify NSCLC patients with the same TNM stage and with the same EGFR genotype. Findings The IPES was able to predict OS risk for stage I-III resected NSCLC patients in the training set (C-index 0.806; 95% CI: 0.744-0.846), internal validation set (0.783; 95% CI: 0.744-0.825) and external validation set (0.817; 95% CI: 0.786-0.849). In addition, IPES performed well in early-stage (stage I) and EGFR genotype prediction. Furthermore, by adopting IPES-based survival score (IPES-score), resected NSCLC patients in the same stage or with the same EGFR genotype could be divided into low- and high-risk subgroups with good and poor prognosis, respectively (p < 0.05 for all). Interpretation The IPES provided a non-invasive way to obtain prognosis-related information from patients. The identification of IPES for resected NSCLC patients with low and high prognostic risk in the same TNM stage or with the same EGFR genotype suggests that IPES have potential to offer more personalized treatment and surveillance suggestion for NSCLC patients. Funding This study was funded by the National Natural Science Foundation of China (grant 62272055, 92259303, 92059203), New Cornerstone Science Foundation through the XPLORER PRIZE, Young Elite Scientists Sponsorship Program by CAST (2021QNRC001), Clinical Medicine Plus X - Young Scholars Project, Peking University, the Fundamental Research Funds for the Central Universities (K.C.), Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Chinese Academy of Medical Sciences (2021RU002), BUPT Excellent Ph.D. Students Foundation (CX2022104).
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Affiliation(s)
- Siqi Zhang
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Xiaohong Liu
- UCL Cancer Institute, University College London, London, WC1E 6DD, UK
| | - Lixin Zhou
- Thoracic Oncology Institute and Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China
| | - Kai Wang
- College of Future Technology, Peking University and Peking-Tsinghua Center for Life Sciences, Beijing, 100871, China
| | - Jun Shao
- State Key Laboratory of Respiratory Health and Multimorbidity, Department of Pulmonary and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Jianyu Shi
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Xuan Wang
- Thoracic Oncology Institute and Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China
| | - Jiaxing Mu
- Thoracic Oncology Institute and Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China
| | - Tianrun Gao
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Zeyu Jiang
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Kezhong Chen
- Thoracic Oncology Institute and Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China
| | - Chengdi Wang
- State Key Laboratory of Respiratory Health and Multimorbidity, Department of Pulmonary and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Guangyu Wang
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China
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Ellen JG, Jacob E, Nikolaou N, Markuzon N. Autoencoder-based multimodal prediction of non-small cell lung cancer survival. Sci Rep 2023; 13:15761. [PMID: 37737469 PMCID: PMC10517020 DOI: 10.1038/s41598-023-42365-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 09/09/2023] [Indexed: 09/23/2023] Open
Abstract
The ability to accurately predict non-small cell lung cancer (NSCLC) patient survival is crucial for informing physician decision-making, and the increasing availability of multi-omics data offers the promise of enhancing prognosis predictions. We present a multimodal integration approach that leverages microRNA, mRNA, DNA methylation, long non-coding RNA (lncRNA) and clinical data to predict NSCLC survival and identify patient subtypes, utilizing denoising autoencoders for data compression and integration. Survival performance for patients with lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) was compared across modality combinations and data integration methods. Using The Cancer Genome Atlas data, our results demonstrate that survival prediction models combining multiple modalities outperform single modality models. The highest performance was achieved with a combination of only two modalities, lncRNA and clinical, at concordance indices (C-indices) of 0.69 ± 0.03 for LUAD and 0.62 ± 0.03 for LUSC. Models utilizing all five modalities achieved mean C-indices of 0.67 ± 0.04 and 0.63 ± 0.02 for LUAD and LUSC, respectively, while the best individual modality performance reached C-indices of 0.64 ± 0.03 for LUAD and 0.59 ± 0.03 for LUSC. Analysis of biological differences revealed two distinct survival subtypes with over 900 differentially expressed transcripts.
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Affiliation(s)
- Jacob G Ellen
- Institute of Health Informatics, University College London, London, UK.
| | - Etai Jacob
- AstraZeneca, Oncology Data Science, Waltham, MA, USA
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Garinet S, Didelot A, Marisa L, Beinse G, Sroussi M, Le Pimpec-Barthes F, Fabre E, Gibault L, Laurent-Puig P, Mouillet-Richard S, Legras A, Blons H. A novel Chr1-miR-200 driven whole transcriptome signature shapes tumor immune microenvironment and predicts relapse in early-stage lung adenocarcinoma. J Transl Med 2023; 21:324. [PMID: 37189151 DOI: 10.1186/s12967-023-04086-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 03/25/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND In Lung adenocarcinoma (LUAD), targeted therapies and immunotherapies have moved from metastatic to early stage and stratification of the relapse risk becomes mandatory. Here we identified a miR-200 based RNA signature that delineates Epithelial-to-mesenchymal transition (EMT) heterogeneity and predicts survival beyond current classification systems. METHODS A miR-200 signature was identified using RNA sequencing. We scored the miR-200 signature by WISP (Weighted In Silico Pathology), used GSEA to identify pathway enrichments and MCP-counter to characterize immune cell infiltrates. We evaluate the clinical value of this signature in our series of LUAD and using TCGA and 7 published datasets. RESULTS We identified 3 clusters based on supervised classification: I is miR-200-sign-down and enriched in TP53 mutations IIA and IIB are miR-200-sign-up: IIA is enriched in EGFR (p < 0.001), IIB is enriched in KRAS mutation (p < 0.001). WISP stratified patients into miR-200-sign-down (n = 65) and miR-200-sign-up (n = 42). Several biological processes were enriched in MiR-200-sign-down tumors, focal adhesion, actin cytoskeleton, cytokine/receptor interaction, TP53 signaling and cell cycle pathways. Fibroblast, immune cell infiltration and PDL1 expression were also significantly higher suggesting immune exhaustion. This signature stratified patients into high-vs low-risk groups, miR-200-sign-up had higher DFS, median not reached at 60 vs 41 months and within subpopulations with stage I, IA, IB, or II. Results were validated on TCGA data on 7 public datasets. CONCLUSION This EMT and miR-200-related prognostic signature refines prognosis evaluation independently of tumor stage and paves the way towards assessing the predictive value of this LUAD clustering to optimize perioperative treatment.
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Affiliation(s)
- Simon Garinet
- Assistance Publique-Hôpitaux de Paris, Department of Biochemistry, Pharmacogenetics and Molecular Oncology, European Georges Pompidou Hospital, Paris Cancer Institute CARPEM, 20 Rue Leblanc, 75015, Paris, France.
- Centre de Recherche des Cordeliers, INSERM, Team Personalized Medicine, Pharmacogenomics and Therapeutic Optimization (MEPPOT), Université de Paris, Sorbonne Université, Paris, France.
- Department of Genetics and Molecular Medicine, Georges Pompidou European Hospital, APHP Centre, Paris, France.
| | - Audrey Didelot
- Centre de Recherche des Cordeliers, INSERM, Team Personalized Medicine, Pharmacogenomics and Therapeutic Optimization (MEPPOT), Université de Paris, Sorbonne Université, Paris, France
| | - Laetitia Marisa
- Department of Genetics and Molecular Medicine, Georges Pompidou European Hospital, APHP Centre, Paris, France
| | - Guillaume Beinse
- Centre de Recherche des Cordeliers, INSERM, Team Personalized Medicine, Pharmacogenomics and Therapeutic Optimization (MEPPOT), Université de Paris, Sorbonne Université, Paris, France
| | - Marine Sroussi
- Centre de Recherche des Cordeliers, INSERM, Team Personalized Medicine, Pharmacogenomics and Therapeutic Optimization (MEPPOT), Université de Paris, Sorbonne Université, Paris, France
| | | | - Elizabeth Fabre
- Department of Thoracic Oncology, Georges Pompidou European Hospital, APHP Centre, Paris, France
| | - Laure Gibault
- Department of Pathology, Georges Pompidou European Hospital, APHP Centre, Paris, France
| | - Pierre Laurent-Puig
- Centre de Recherche des Cordeliers, INSERM, Team Personalized Medicine, Pharmacogenomics and Therapeutic Optimization (MEPPOT), Université de Paris, Sorbonne Université, Paris, France
- Department of Genetics and Molecular Medicine, Georges Pompidou European Hospital, APHP Centre, Paris, France
| | - Sophie Mouillet-Richard
- Centre de Recherche des Cordeliers, INSERM, Team Personalized Medicine, Pharmacogenomics and Therapeutic Optimization (MEPPOT), Université de Paris, Sorbonne Université, Paris, France
| | - Antoine Legras
- Department of Thoracic Surgery, Georges Pompidou European Hospital, APHP Centre, Paris, France
| | - Hélène Blons
- Assistance Publique-Hôpitaux de Paris, Department of Biochemistry, Pharmacogenetics and Molecular Oncology, European Georges Pompidou Hospital, Paris Cancer Institute CARPEM, 20 Rue Leblanc, 75015, Paris, France.
- Centre de Recherche des Cordeliers, INSERM, Team Personalized Medicine, Pharmacogenomics and Therapeutic Optimization (MEPPOT), Université de Paris, Sorbonne Université, Paris, France.
- Department of Genetics and Molecular Medicine, Georges Pompidou European Hospital, APHP Centre, Paris, France.
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Carr SR, Wang H, Hudlikar R, Lu X, Zhang MR, Hoang CD, Yan F, Schrump DS. A Unique Gene Signature Predicting Recurrence Free Survival in Stage IA Lung Adenocarcinoma. J Thorac Cardiovasc Surg 2023; 165:1554-1564. [PMID: 37608989 PMCID: PMC10442056 DOI: 10.1016/j.jtcvs.2022.09.028] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Objective Resected stage IA lung adenocarcinoma (LUAD) has a reported 5-year recurrence free survival (RFS) of 63-81%. A unique gene signature stratifying patients with early stage LUAD as high or low-risk of recurrence would be valuable. Methods GEO datasets combining European and North American LUAD patients (n=684) were filtered for stage IA (n=105) to develop a robust signature for recurrence (RFSscore). Univariate Cox proportional hazard regression model was used to assess associations of gene expression with RFS and OS. Leveraging a bootstrap approach of these identified upregulated genes allowed construction of a model which was evaluated by Area Under the Received Operating Characteristics. The optimal signature has RFSscore calculated via a linear combination of expression of selected genes weighted by the corresponding Cox regression derived coefficients. Log-rank analysis calculated RFS and OS. Results were validated using the LUAD TCGA transcriptomic NGS based dataset. Results Rigorous bioinformatic analysis identified a signature of 4 genes: KNSTRN, PAFAH1B3, MIF, CHEK1. Kaplan-Meier analysis of stage IA LUAD with this signature resulted in 5-year RFS for low-risk of 90% compared to 53% for high-risk (HR 6.55, 95%CI 2.65-16.18, p-value <0.001), confirming the robustness of the gene signature with its clinical significance. Validation of the signature using TCGA dataset resulted in an AUC of 0.797 and 5-year RFS for low and high-risk stage IA patients being 91% and 67%, respectively (HR 3.44, 95%CI 1.16-10.23, p-value=0.044). Conclusions This 4 gene signature stratifies European and North American patients with pathologically confirmed stage IA LUAD into low and high-risk groups for OS and more importantly RFS.
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Affiliation(s)
- Shamus R Carr
- Thoracic Surgery Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Haitao Wang
- Thoracic Surgery Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Rasika Hudlikar
- Thoracic Surgery Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Xiaofan Lu
- State Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Mary R Zhang
- Thoracic Surgery Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Chuong D Hoang
- Thoracic Surgery Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Fangrong Yan
- State Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - David S Schrump
- Thoracic Surgery Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
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Peng H, Li X, Luan Y, Wang C, Wang W. A novel prognostic model related to oxidative stress for treatment prediction in lung adenocarcinoma. Front Oncol 2023; 13:1078697. [PMID: 36798829 PMCID: PMC9927401 DOI: 10.3389/fonc.2023.1078697] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 01/05/2023] [Indexed: 02/01/2023] Open
Abstract
Background The prognostic model based on oxidative stress for lung adenocarcinoma (LUAD) remains unclear. Methods The information of LUAD patients were acquired from TCGA dataset. We also collected two external datasets from GEO for verification. Oxidative stress-related genes (ORGs) were extracted from Genecards. We performed machine learning algorithms, including Univariate Cox regression, Random Survival Forest, and Least Absolute Shrinkage and Selection Operator (Lasso) analyses on the ORGs to build the OS-score and OS-signature. We drew the Kaplan-Meier and time-dependent receiver operating characteristic curve (ROC) to evaluate the efficacy of the OS-signature in predicting the prognosis of LUAD. We used GISTIC 2.0 and maftool algorithms to explore Genomic mutation of OS-signature. To analyze characteristic of tumor infiltrating immune cells, ESTIMATE, TIMER2.0, MCPcounter and ssGSEA algorithms were applied, thus evaluating the immunotherapeutic strategies. Chemotherapeutics sensitivity analysis was based on pRRophetic package. Finally, PCR assays was also used to detect the expression values of related genes in the OS-signature in cell lines. Results Ten ORGs with prognostic value and the OS-signature containing three prognostic ORGs were identified. The significantly better prognosis of LUAD patients was observed in LUAD patients. The efficiency and accuracy of OS-signature in predicting prognosis for LUAD patients was confirmed by survival ROC curves and two external validation data sets. It was clearly observed that patients with high OS-scores had lower immunomodulators levels (with a few exceptions), stromal score, immune score, ESTIMATE score and infiltrating immune cell populations. On the contrary, patients with higher OS-scores were more likely to have higher tumor purity. PCR assays showed that, MRPL44 and CYCS were significantly higher expressed in LUAD cell lines, while CAT was significantly lower expressed. Conclusion The novel oxidative stress-related model we identified could be used for prognosis and treatment prediction in lung adenocarcinoma.
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Affiliation(s)
| | | | | | | | - Wei Wang
- Department of Thoracic Surgery, Hebei Chest Hospital, Hebei Provincial Key Laboratory of Lung Disease, Shijiazhuang, Hebei, China
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10
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Xia QL, He XM, Ma Y, Li QY, Du YZ, Wang J. 5-mRNA-based prognostic signature of survival in lung adenocarcinoma. World J Clin Oncol 2023; 14:27-39. [PMID: 36699627 PMCID: PMC9850667 DOI: 10.5306/wjco.v14.i1.27] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 11/02/2022] [Accepted: 12/13/2022] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Lung adenocarcinoma (LUAD) is the most common non-small-cell lung cancer, with a high incidence and a poor prognosis. AIM To construct effective predictive models to evaluate the prognosis of LUAD patients. METHODS In this study, we thoroughly mined LUAD genomic data from the Gene Expression Omnibus (GEO) (GSE43458, GSE32863, and GSE27262) and the Cancer Genome Atlas (TCGA) datasets, including 698 LUAD and 172 healthy (or adjacent normal) lung tissue samples. Univariate regression and LASSO regression analyses were used to screen differentially expressed genes (DEGs) related to patient prognosis, and multivariate Cox regression analysis was applied to establish the risk score equation and construct the survival prognosis model. Receiver operating characteristic curve and Kaplan-Meier survival analyses with clinically independent prognostic parameters were performed to verify the predictive power of the model and further establish a prognostic nomogram. RESULTS A total of 380 DEGs were identified in LUAD tissues through GEO and TCGA datasets, and 5 DEGs (TCN1, CENPF, MAOB, CRTAC1 and PLEK2) were screened out by multivariate Cox regression analysis, indicating that the prognostic risk model could be used as an independent prognostic factor (Hazard ratio = 1.520, P < 0.001). Internal and external validation of the model confirmed that the prediction model had good sensitivity and specificity (Area under the curve = 0.754, 0.737). Combining genetic models and clinical prognostic factors, nomograms can also predict overall survival more effectively. CONCLUSION A 5-mRNA-based model was constructed to predict the prognosis of lung adenocarcinoma, which may provide clinicians with reliable prognostic assessment tools and help clinical treatment decisions.
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Affiliation(s)
- Qian-Lin Xia
- Laboratory Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Xiao-Meng He
- Scientific Research Center, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - Yan Ma
- Scientific Research Center, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - Qiu-Yue Li
- Scientific Research Center, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - Yu-Zhen Du
- Laboratory Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Jin Wang
- Scientific Research Center, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
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11
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Cheng Y, Yao J, Fang Q, Chen B, Zang G. A circadian rhythm-related biomarker for predicting prognosis and immunotherapy efficacy in lung adenocarcinoma. Aging (Albany NY) 2022; 14:9617-9631. [PMID: 36455876 PMCID: PMC9792196 DOI: 10.18632/aging.204411] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 11/21/2022] [Indexed: 12/02/2022]
Abstract
Lung adenocarcinoma (LUAD) remains a major reason of cancer-associated mortality globally, and there exists a lack of indicators for survival in LUAD patients. Therefore, it is clinically required to obtain a novel prognostically indicator for guiding clinical management. In this study, we established a circadian rhythm (CR) related signature by a combinative investigation of multiple datasets. The newly-established signature showed an acceptable ability to predict survival and could serve as an independent indicator for prognosis. Moreover, the newly-established signature was critically associated with tumor malignancy, including proliferation, invasion, EMT and metastasis. The newly-established signature was predictive of response to immune checkpoint blockade. Collectively, we established a CR-related gene signature that could forecast survival, tumor malignancy and therapeutic response; our findings could help guiding clinical management.
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Affiliation(s)
- Yuanjun Cheng
- Department of Cardiothoracic Surgery, People’s Hospital of Chizhou, Chizhou, China
| | - Jie Yao
- Department of Cardiothoracic Surgery, People’s Hospital of Chizhou, Chizhou, China
| | - Qianru Fang
- Department of Obstetrics, People’s Hospital of Chizhou, Chizhou, China
| | - Bin Chen
- Department of Cardiothoracic Surgery, People’s Hospital of Chizhou, Chizhou, China
| | - Guohui Zang
- Department of Cardiothoracic Surgery, People’s Hospital of Chizhou, Chizhou, China
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12
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Shi Y, Dai S, Lei Y. Development and validation of a combined metabolism and immune prognostic model in lung adenocarcinoma. J Thorac Dis 2022; 14:4983-4997. [PMID: 36647508 PMCID: PMC9840026 DOI: 10.21037/jtd-22-1695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 12/16/2022] [Indexed: 12/31/2022]
Abstract
Background Tumor metabolism and immune response can affect the biological behavior of tumor cells. There is an obvious relationship between glycolysis and immune response. However, the association between metabolism and immune response and prognosis in lung adenocarcinoma (LUAD) has not yet been examined in a comprehensive and detailed manner. The establishment of reliable models for predicting the prognosis of LUAD based on glycolysis ability and immune status is still highly anticipated. Methods The expression of genes were obtained from online databases, and the differentially expressed genes in LUAD tissues and adjacent tissues were identified. We used LUAD samples in The Cancer Genome Atlas (TCGA) database as training set and the Gene Expression Omnibus (GEO) databases as validation sets. The best predictive model was constructed using least absolute selection and shrinkage operator (LASSO) regression and Cox regression. The receiver operator characteristic (ROC) curve is used to verify the accuracy of the model. The expression status of the Glycolysis-related genes (GRGs) and the status of the immune cells in LUCD patients were further analyzed. The protein levels of the 3 identified genes were then tested in LUAD patients. Results We identified 3 GRGs and immune-related genes (i.e., fibroblast growth factor 2, hyaluronan-mediated motor receptor, and nuclear receptor 0B2) and constructed a stable comprehensive index of glycolysis and immunity (CIGI) prediction model. The validation results for this CIGI model were quite stable across different datasets and patient subgroups and the CIGI score can be included as an independent prognostic factor for LUAD patients. The area under the curve (AUC) values of 1-, 3- and 5-year of the finally established nomogram model are 0.767, 0.735 and 0.769. Further analysis showed that LUAD patients in the low-risk group had lower levels of glycolytic gene expression than those in the high-risk group and exhibited an immunosuppressed state. Finally, hyaluronan-mediated motor receptor may play a role in inhibiting cancer, while fibroblast growth factor 2 and nuclear receptor 0B2 may play roles in promoting cancer. Conclusions In this study, we established a new prognostic prediction model for LUAD patients that combines glycolysis ability and immune status.
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Affiliation(s)
- Yu Shi
- Department of Oncology, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Shihui Dai
- Department of Oncology, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yu Lei
- Department of Oncology, the First Affiliated Hospital of Anhui Medical University, Hefei, China
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13
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Saman H, Raza A, Patil K, Uddin S, Crnogorac-Jurcevic T. Non-Invasive Biomarkers for Early Lung Cancer Detection. Cancers (Basel) 2022; 14:5782. [PMID: 36497263 PMCID: PMC9739091 DOI: 10.3390/cancers14235782] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/18/2022] [Accepted: 07/20/2022] [Indexed: 11/27/2022] Open
Abstract
Worldwide, lung cancer (LC) is the most common cause of cancer death, and any delay in the detection of new and relapsed disease serves as a major factor for a significant proportion of LC morbidity and mortality. Though invasive methods such as tissue biopsy are considered the gold standard for diagnosis and disease monitoring, they have several limitations. Therefore, there is an urgent need to identify and validate non-invasive biomarkers for the early diagnosis, prognosis, and treatment of lung cancer for improved patient management. Despite recent progress in the identification of non-invasive biomarkers, currently, there is a shortage of reliable and accessible biomarkers demonstrating high sensitivity and specificity for LC detection. In this review, we aim to cover the latest developments in the field, including the utility of biomarkers that are currently used in LC screening and diagnosis. We comment on their limitations and summarise the findings and developmental stages of potential molecular contenders such as microRNAs, circulating tumour DNA, and methylation markers. Furthermore, we summarise research challenges in the development of biomarkers used for screening purposes and the potential clinical applications of newly discovered biomarkers.
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Affiliation(s)
- Harman Saman
- Hamad Medical Corporation, Doha 3050, Qatar
- Barts Cancer Institute, Queen Mary University of London, London EC1M 5PZ, UK
| | - Afsheen Raza
- National Center for Cancer Care and Research, Hamad Medical Corporation, Doha 3050, Qatar
| | - Kalyani Patil
- Translational Research Institute, Academic Health System, Hamad Medical Corporation, Doha 3050, Qatar
| | - Shahab Uddin
- Translational Research Institute, Academic Health System, Hamad Medical Corporation, Doha 3050, Qatar
- Dermatology Institute, Academic Health System, Hamad Medical Corporation, Doha 3050, Qatar
- Laboratory of Animal Research Centre, Qatar University, Doha 2731, Qatar
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14
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Zhang J, Chen A, Xue Z, Liang C. Identification of immune-associated prognostic biomarkers in lung adenocarcinoma on the basis of gene co-expression network. Immunopharmacol Immunotoxicol 2022; 45:334-346. [DOI: 10.1080/08923973.2022.2145965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Jianhai Zhang
- Department of Thoracic and Cardiac Surgery, Ruian People's Hospital, Zhejiang, China
| | - Ange Chen
- Department of Thoracic and Cardiac Surgery, Ruian People's Hospital, Zhejiang, China
| | - Zhang Xue
- Department of Thoracic and Cardiac Surgery, Ruian People's Hospital, Zhejiang, China
| | - Chengzhi Liang
- Department of Thoracic and Cardiac Surgery, Ruian People's Hospital, Zhejiang, China
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15
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Cattelani L, Fortino V. Identifying gene expression-based biomarkers in online learning environments. BIOINFORMATICS ADVANCES 2022; 2:vbac074. [PMID: 36699355 PMCID: PMC9710669 DOI: 10.1093/bioadv/vbac074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 09/07/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022]
Abstract
Motivation Gene expression-based classifiers are often developed using historical data by training a model on a small set of patients and a large set of features. Models trained in such a way can be afterwards applied for predicting the output for new unseen patient data. However, very often the accuracy of these models starts to decrease as soon as new data is fed into the trained model. This problem, known as concept drift, complicates the task of learning efficient biomarkers from data and requires special approaches, different from commonly used data mining techniques. Results Here, we propose an online ensemble learning method to continually validate and adjust gene expression-based biomarker panels over increasing volume of data. We also propose a computational solution to the problem of feature drift where gene expression signatures used to train the classifier become less relevant over time. A benchmark study was conducted to classify the breast tumors into known subtypes by using a large-scale transcriptomic dataset (∼3500 patients), which was obtained by combining two datasets: SCAN-B and TCGA-BRCA. Remarkably, the proposed strategy improves the classification performances of gold-standard biomarker panels (e.g. PAM50, OncotypeDX and Endopredict) by adding features that are clinically relevant. Moreover, test results show that newly discovered biomarker models can retain a high classification accuracy rate when changing the source generating the gene expression profiles. Availability and implementation github.com/UEFBiomedicalInformaticsLab/OnlineLearningBD. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Luca Cattelani
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Finland
| | - Vittorio Fortino
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Finland
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16
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Hu Y, Lu Y, Xing F, Hsu W. FGFR1/MAPK-directed brachyury activation drives PD-L1-mediated immune evasion to promote lung cancer progression. Cancer Lett 2022; 547:215867. [PMID: 35985510 DOI: 10.1016/j.canlet.2022.215867] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 08/02/2022] [Accepted: 08/04/2022] [Indexed: 11/02/2022]
Abstract
Immune checkpoint inhibitors provide promising benefits for patients with cancer. However, efficacy has been encumbered by high resistance rates. It is critical to understand the basic mechanisms of tumor-mediated resistance to this treatment modality. Previous studies have found that the transcription factor brachyury is highly expressed in lung cancer. Here, we show that brachyury activation induces the upregulation of PD-L1 leading to inactivation of T cell proliferation in vitro and inhibited infiltration of CD8+ and CD3+ T cells into tumor in an immunocompetent mouse model. We further demonstrate that FGFR1/MAPK activation regulates brachyury and PD-L1 expressions and promotes immunosuppression. Blocking FGFR1/MAPK suppresses brachyury and PD-L1 expressions, revives immune activity, and reverses the resistance to anti-PD-1 treatment to produce a durable therapeutic response. We also find that lung cancer patients with high activation of the FGFR1-MAPK-brachyury-PD-L1 signature and low expression of CD8A, CD3D, or PDCD1 have worse survival outcomes. These findings elucidate a novel mechanism of immune escape from immune checkpoint therapy and provide an opportunity to enhance its therapeutic efficacy in the treatment of a subset of FGFR1/MAPK/brachyury/PD-L1-driven lung cancer.
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Affiliation(s)
- Yunping Hu
- Department of Neurological Surgery, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA.
| | - Yong Lu
- The Methodist Hospital Research Institute, 6670 Bertner Avenue, Houston, Houston, TX, 77030, USA
| | - Fei Xing
- Department of Cancer Biology, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA
| | - Wesley Hsu
- Department of Neurological Surgery, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA.
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17
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Hinneh JA, Gillis JL, Moore NL, Butler LM, Centenera MM. The role of RHAMM in cancer: Exposing novel therapeutic vulnerabilities. Front Oncol 2022; 12:982231. [PMID: 36033439 PMCID: PMC9400171 DOI: 10.3389/fonc.2022.982231] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Receptor for hyaluronic acid-mediated motility (RHAMM) is a cell surface receptor for hyaluronic acid that is critical for cell migration and a cell cycle protein involved in microtubule assembly and stability. These functions of RHAMM are required for cellular stress responses and cell cycle progression but are also exploited by tumor cells for malignant progression and metastasis. RHAMM is often overexpressed in tumors and is an independent adverse prognostic factor for a number of cancers such as breast and prostate. Interestingly, pharmacological or genetic inhibition of RHAMM in vitro and in vivo ablates tumor invasiveness and metastatic spread, implicating RHAMM as a potential therapeutic target to restrict tumor growth and improve patient survival. However, RHAMM’s pro-tumor activity is dependent on its subcellular distribution, which complicates the design of RHAMM-directed therapies. An alternative approach is to identify downstream signaling pathways that mediate RHAMM-promoted tumor aggressiveness. Herein, we discuss the pro-tumoral roles of RHAMM and elucidate the corresponding regulators and signaling pathways mediating RHAMM downstream events, with a specific focus on strategies to target the RHAMM signaling network in cancer cells.
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Affiliation(s)
- Josephine A. Hinneh
- South Australian Immunogenomics Cancer Institute and Adelaide Medical School, Adelaide, SA, Australia
- Freemason’s Centre for Male Health and Wellbeing, The University of Adelaide, Adelaide, SA, Australia
- Precision Cancer Medicine, South Australian Health and Medical Research Institute, Adelaide, SA, Australia
- Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Joanna L. Gillis
- South Australian Immunogenomics Cancer Institute and Adelaide Medical School, Adelaide, SA, Australia
- Precision Cancer Medicine, South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Nicole L. Moore
- South Australian Immunogenomics Cancer Institute and Adelaide Medical School, Adelaide, SA, Australia
- Precision Cancer Medicine, South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Lisa M. Butler
- South Australian Immunogenomics Cancer Institute and Adelaide Medical School, Adelaide, SA, Australia
- Freemason’s Centre for Male Health and Wellbeing, The University of Adelaide, Adelaide, SA, Australia
- Precision Cancer Medicine, South Australian Health and Medical Research Institute, Adelaide, SA, Australia
- *Correspondence: Lisa M. Butler, ; Margaret M. Centenera,
| | - Margaret M. Centenera
- South Australian Immunogenomics Cancer Institute and Adelaide Medical School, Adelaide, SA, Australia
- Freemason’s Centre for Male Health and Wellbeing, The University of Adelaide, Adelaide, SA, Australia
- Precision Cancer Medicine, South Australian Health and Medical Research Institute, Adelaide, SA, Australia
- *Correspondence: Lisa M. Butler, ; Margaret M. Centenera,
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18
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Thaiparambil J, Dong J, Grimm SL, Perera D, Ambati CSR, Putluri V, Robertson MJ, Patel TD, Mistretta B, Gunaratne PH, Kim MP, Yustein JT, Putluri N, Coarfa C, El‐Zein R. Integrative metabolomics and transcriptomics analysis reveals novel therapeutic vulnerabilities in lung cancer. Cancer Med 2022; 12:584-596. [PMID: 35676822 PMCID: PMC9844651 DOI: 10.1002/cam4.4933] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 04/22/2022] [Accepted: 05/04/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Non-small cell lung cancer (NSCLC) comprises the majority (~85%) of all lung tumors, with lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) being the most frequently diagnosed histological subtypes. Multi-modal omics profiling has been carried out in NSCLC, but no studies have yet reported a unique metabolite-related gene signature and altered metabolic pathways associated with LUAD and LUSC. METHODS We integrated transcriptomics and metabolomics to analyze 30 human lung tumors and adjacent noncancerous tissues. Differential co-expression was used to identify modules of metabolites that were altered between normal and tumor. RESULTS We identified unique metabolite-related gene signatures specific for LUAD and LUSC and key pathways aberrantly regulated at both transcriptional and metabolic levels. Differential co-expression analysis revealed that loss of coherence between metabolites in tumors is a major characteristic in both LUAD and LUSC. We identified one metabolic onco-module gained in LUAD, characterized by nine metabolites and 57 metabolic genes. Multi-omics integrative analysis revealed a 28 metabolic gene signature associated with poor survival in LUAD, with six metabolite-related genes as individual prognostic markers. CONCLUSIONS We demonstrated the clinical utility of this integrated metabolic gene signature in LUAD by using it to guide repurposing of AZD-6482, a PI3Kβ inhibitor which significantly inhibited three genes from the 28-gene signature. Overall, we have integrated metabolomics and transcriptomics analyses to show that LUAD and LUSC have distinct profiles, inferred gene signatures with prognostic value for patient survival, and identified therapeutic targets and repurposed drugs for potential use in NSCLC treatment.
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Affiliation(s)
| | - Jianrong Dong
- Center for Precision and Environmental HealthBaylor College of MedicineHoustonTexasUSA,Molecular and Cellular Biology DepartmentBaylor College of MedicineHoustonTexasUSA
| | - Sandra L. Grimm
- Center for Precision and Environmental HealthBaylor College of MedicineHoustonTexasUSA,Dan L Duncan Comprehensive Cancer CenterBaylor College of MedicineHoustonTexasUSA,Advanced Technology CoresBaylor College of MedicineHoustonTexasUSA
| | - Dimuthu Perera
- Advanced Technology CoresBaylor College of MedicineHoustonTexasUSA
| | | | - Vasanta Putluri
- Advanced Technology CoresBaylor College of MedicineHoustonTexasUSA
| | - Matthew J. Robertson
- Dan L Duncan Comprehensive Cancer CenterBaylor College of MedicineHoustonTexasUSA,Advanced Technology CoresBaylor College of MedicineHoustonTexasUSA
| | - Tajhal D. Patel
- Texas Children’s Cancer and Hematology Centers and The Faris D. Virani Ewing Sarcoma CenterBaylor College of MedicineHoustonTexasUSA
| | - Brandon Mistretta
- Department of Biology and BiochemistryUniversity of HoustonHoustonTexasUSA
| | - Preethi H. Gunaratne
- Molecular and Cellular Biology DepartmentBaylor College of MedicineHoustonTexasUSA,Department of Biology and BiochemistryUniversity of HoustonHoustonTexasUSA
| | - Min P. Kim
- Houston Methodist Cancer CenterHoustonTexasUSA,Division of Thoracic Surgery, Department of SurgeryHouston Methodist HospitalHoustonTexasUSA
| | - Jason T. Yustein
- Molecular and Cellular Biology DepartmentBaylor College of MedicineHoustonTexasUSA,Dan L Duncan Comprehensive Cancer CenterBaylor College of MedicineHoustonTexasUSA,Texas Children’s Cancer and Hematology Centers and The Faris D. Virani Ewing Sarcoma CenterBaylor College of MedicineHoustonTexasUSA,Integrative Molecular and Biological Sciences ProgramBaylor College of MedicineHoustonTexasUSA
| | - Nagireddy Putluri
- Molecular and Cellular Biology DepartmentBaylor College of MedicineHoustonTexasUSA,Advanced Technology CoresBaylor College of MedicineHoustonTexasUSA
| | - Cristian Coarfa
- Center for Precision and Environmental HealthBaylor College of MedicineHoustonTexasUSA,Molecular and Cellular Biology DepartmentBaylor College of MedicineHoustonTexasUSA,Dan L Duncan Comprehensive Cancer CenterBaylor College of MedicineHoustonTexasUSA,Advanced Technology CoresBaylor College of MedicineHoustonTexasUSA
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19
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Petrovic D, Bodinier B, Dagnino S, Whitaker M, Karimi M, Campanella G, Haugdahl Nøst T, Polidoro S, Palli D, Krogh V, Tumino R, Sacerdote C, Panico S, Lund E, Dugué PA, Giles GG, Severi G, Southey M, Vineis P, Stringhini S, Bochud M, Sandanger TM, Vermeulen RCH, Guida F, Chadeau-Hyam M. Epigenetic mechanisms of lung carcinogenesis involve differentially methylated CpG sites beyond those associated with smoking. Eur J Epidemiol 2022; 37:629-640. [PMID: 35595947 PMCID: PMC9288379 DOI: 10.1007/s10654-022-00877-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 04/25/2022] [Indexed: 12/24/2022]
Abstract
Smoking-related epigenetic changes have been linked to lung cancer, but the contribution of epigenetic alterations unrelated to smoking remains unclear. We sought for a sparse set of CpG sites predicting lung cancer and explored the role of smoking in these associations. We analysed CpGs in relation to lung cancer in participants from two nested case-control studies, using (LASSO)-penalised regression. We accounted for the effects of smoking using known smoking-related CpGs, and through conditional-independence network. We identified 29 CpGs (8 smoking-related, 21 smoking-unrelated) associated with lung cancer. Models additionally adjusted for Comprehensive Smoking Index-(CSI) selected 1 smoking-related and 49 smoking-unrelated CpGs. Selected CpGs yielded excellent discriminatory performances, outperforming information provided by CSI only. Of the 8 selected smoking-related CpGs, two captured lung cancer-relevant effects of smoking that were missed by CSI. Further, the 50 CpGs identified in the CSI-adjusted model complementarily explained lung cancer risk. These markers may provide further insight into lung cancer carcinogenesis and help improving early identification of high-risk patients.
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Affiliation(s)
- Dusan Petrovic
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
- Department of Epidemiology and Health Systems (DESS), University Centre for General Medicine and Public Health (UNISANTE), Lausanne, Switzerland
- Department and Division of Primary Care Medicine, University Hospital of Geneva, Geneva, Switzerland
| | - Barbara Bodinier
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | - Sonia Dagnino
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | - Matthew Whitaker
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | - Maryam Karimi
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
- Bureau de Biostatistique et d'Épidémiologie, Institut Gustave Roussy, Université Paris-Saclay, Villejuif, France
- Oncostat U1018, Inserm, Équipe Labellisée Ligue Contre Le Cancer, Université Paris-Saclay, Villejuif, France
| | - Gianluca Campanella
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | - Therese Haugdahl Nøst
- Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
| | | | - Domenico Palli
- Molecular and Nutritional Epidemiology Unit, Cancer Research and Prevention Institute-ISPO, Florence, Italy
| | - Vittorio Krogh
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Rosario Tumino
- Hyblean Association for Epidemiological Research, AIRE- ONLUS, Ragusa, Italy
| | - Carlotta Sacerdote
- Unit of Cancer Epidemiology Città Della Salute e della Scienza University-Hospital, Via Santena 7, 10126, Turin, Italy
| | - Salvatore Panico
- Department of Clinical Medicine and Surgery, Federico II University, Naples, Italy
| | - Eiliv Lund
- Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
- The Norwegian Cancer Registry, Oslo, Norway
| | - Pierre-Antoine Dugué
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Australia
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Australia
| | - Gianluca Severi
- Centre for Research in Epidemiology and Population Health, Inserm (Institut National de La Sante Et de a Recherche Medicale), Villejuif, France
| | - Melissa Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Australia
| | - Paolo Vineis
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | - Silvia Stringhini
- Department of Epidemiology and Health Systems (DESS), University Centre for General Medicine and Public Health (UNISANTE), Lausanne, Switzerland
- Department and Division of Primary Care Medicine, University Hospital of Geneva, Geneva, Switzerland
| | - Murielle Bochud
- Department of Epidemiology and Health Systems (DESS), University Centre for General Medicine and Public Health (UNISANTE), Lausanne, Switzerland
| | - Torkjel M Sandanger
- Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
| | - Roel C H Vermeulen
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
- Julius Centre for Health Sciences and Primary Care, University Medical Centre, Utrecht, Utrecht, The Netherlands
| | - Florence Guida
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
- Group of Genetic Epidemiology, International Agency for Research on Cancer (IARC) - World Health Organization (WHO), Lyon, France
| | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK.
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands.
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20
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CDCP1: A promising diagnostic biomarker and therapeutic target for human cancer. Life Sci 2022; 301:120600. [DOI: 10.1016/j.lfs.2022.120600] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 04/25/2022] [Accepted: 04/27/2022] [Indexed: 12/25/2022]
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21
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Molecular Radiobiology in Non-Small Cell Lung Cancer: Prognostic and Predictive Response Factors. Cancers (Basel) 2022; 14:cancers14092202. [PMID: 35565331 PMCID: PMC9101029 DOI: 10.3390/cancers14092202] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/21/2022] [Accepted: 04/27/2022] [Indexed: 12/11/2022] Open
Abstract
Simple Summary The identification of prognostic and predictive gene signatures of response to cancer treatment (radiotherapy) could help in making therapeutic decisions in patients affected by NSCLC. There are multiple proposals for gene signatures that attempt to predict survival or predict response to treatment (not radiotherapy), but they mainly focus on early stages or metastasis at diagnosis. In contrast, there have been few studies that raise these predictive and/or prognostic elements in nonmetastatic locally advanced stages, where treatment with ionizing radiation plays an important role. In this work, we review in depth previous works discovering the prognostic and predictive response factors in non-small cell lung cancer, specially focused on non-deeply studied radiation-based therapy. Abstract Non-small-cell lung cancer (NSCLC) is the leading cause of cancer-related death worldwide, generating huge economic and social impacts that have not slowed in recent years. Oncological treatment for this neoplasm usually includes surgery, chemotherapy, treatments on molecular targets and ionizing radiation. The prognosis in terms of overall survival (OS) and the different therapeutic responses between patients can be explained, to a large extent, by the existence of widely heterogeneous molecular profiles. The identification of prognostic and predictive gene signatures of response to cancer treatment, could help in making therapeutic decisions in patients affected by NSCLC. Given the published scientific evidence, we believe that the search for prognostic and/or predictive gene signatures of response to radiotherapy treatment can significantly help clinical decision-making. These signatures may condition the fractions, the total dose to be administered and/or the combination of systemic treatments in conjunction with radiation. The ultimate goal is to achieve better clinical results, minimizing the adverse effects associated with current cancer therapies.
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22
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Peinado-Serrano J, Quintanal-Villalonga Á, Muñoz-Galvan S, Verdugo-Sivianes EM, Mateos JC, Ortiz-Gordillo MJ, Carnero A. A Six-Gene Prognostic and Predictive Radiotherapy-Based Signature for Early and Locally Advanced Stages in Non-Small-Cell Lung Cancer. Cancers (Basel) 2022; 14:cancers14092054. [PMID: 35565183 PMCID: PMC9099638 DOI: 10.3390/cancers14092054] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/11/2022] [Accepted: 04/13/2022] [Indexed: 12/11/2022] Open
Abstract
Simple Summary The search for prognostic and/or predictive gene signatures of the response to radiotherapy treatment can significantly aid clinical decision making. These signatures can condition the fractionation, the total dose to be administered, and/or the combination of systemic treatments and radiation. The ultimate goal is to achieve better clinical results, as well as to minimize the adverse effects associated with current cancer therapies. To this end, we analyzed the intrinsic radiosensitivity of 15 NSCLC lines and found the differences in gene expression levels between radiosensitive and radioresistant lines, resulting in a potentially applicable six-gene signature in NSCLC patients. The six-gene signature had the ability to predict overall survival and progression-free survival (PFS), which could translate into a prediction of the response to the cancer treatment received. Abstract Non-small-cell lung cancer (NSCLC) is the leading cause of cancer death worldwide, generating an enormous economic and social impact that has not stopped growing in recent years. Cancer treatment for this neoplasm usually includes surgery, chemotherapy, molecular targeted treatments, and ionizing radiation. The prognosis in terms of overall survival (OS) and the disparate therapeutic responses among patients can be explained, to a great extent, by the existence of widely heterogeneous molecular profiles. The main objective of this study was to identify prognostic and predictive gene signatures of response to cancer treatment involving radiotherapy, which could help in making therapeutic decisions in patients with NSCLC. To achieve this, we took as a reference the differential gene expression pattern among commercial cell lines, differentiated by their response profile to ionizing radiation (radiosensitive versus radioresistant lines), and extrapolated these results to a cohort of 107 patients with NSCLC who had received radiotherapy (among other therapies). We obtained a six-gene signature (APOBEC3B, GOLM1, FAM117A, KCNQ1OT1, PCDHB2, and USP43) with the ability to predict overall survival and progression-free survival (PFS), which could translate into a prediction of the response to the cancer treatment received. Patients who had an unfavorable prognostic signature had a median OS of 24.13 months versus 71.47 months for those with a favorable signature, and the median PFS was 12.65 months versus 47.11 months, respectively. We also carried out a univariate analysis of multiple clinical and pathological variables and a bivariate analysis by Cox regression without any factors that substantially modified the HR value of the proposed gene signature.
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Affiliation(s)
- Javier Peinado-Serrano
- Instituto de Biomedicina de Sevilla, IBIS, Hospital Universitario Virgen del Rocío, Consejo Superior de Investigaciones Científicas, Universidad de Sevilla, Avda. Manuel Siurot s/n, 41013 Seville, Spain; (J.P.-S.); (S.M.-G.); (E.M.V.-S.)
- CIBERONC, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Department of Radiation Oncology, Hospital Universitario Virgen del Rocío, Avda. Manuel Siurot s/n, 41013 Seville, Spain;
| | | | - Sandra Muñoz-Galvan
- Instituto de Biomedicina de Sevilla, IBIS, Hospital Universitario Virgen del Rocío, Consejo Superior de Investigaciones Científicas, Universidad de Sevilla, Avda. Manuel Siurot s/n, 41013 Seville, Spain; (J.P.-S.); (S.M.-G.); (E.M.V.-S.)
- CIBERONC, Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Eva M. Verdugo-Sivianes
- Instituto de Biomedicina de Sevilla, IBIS, Hospital Universitario Virgen del Rocío, Consejo Superior de Investigaciones Científicas, Universidad de Sevilla, Avda. Manuel Siurot s/n, 41013 Seville, Spain; (J.P.-S.); (S.M.-G.); (E.M.V.-S.)
- CIBERONC, Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Juan C. Mateos
- Radiation Physics Department, Hospital Universitario Virgen del Rocío, Avda. Manuel Siurot s/n, 41013 Seville, Spain;
- Departamento de Fisiología Médica y Biofisica, Universidad de Sevilla, 41013 Seville, Spain
| | - María J. Ortiz-Gordillo
- Department of Radiation Oncology, Hospital Universitario Virgen del Rocío, Avda. Manuel Siurot s/n, 41013 Seville, Spain;
| | - Amancio Carnero
- Instituto de Biomedicina de Sevilla, IBIS, Hospital Universitario Virgen del Rocío, Consejo Superior de Investigaciones Científicas, Universidad de Sevilla, Avda. Manuel Siurot s/n, 41013 Seville, Spain; (J.P.-S.); (S.M.-G.); (E.M.V.-S.)
- CIBERONC, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Correspondence:
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23
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Lee NSY, Shafiq J, Field M, Fiddler C, Varadarajan S, Gandhidasan S, Hau E, Vinod SK. Predicting 2-year survival in stage I-III non-small cell lung cancer: the development and validation of a scoring system from an Australian cohort. Radiat Oncol 2022; 17:74. [PMID: 35418206 PMCID: PMC9008968 DOI: 10.1186/s13014-022-02050-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 04/06/2022] [Indexed: 12/24/2022] Open
Abstract
Background There are limited data on survival prediction models in contemporary inoperable non-small cell lung cancer (NSCLC) patients. The objective of this study was to develop and validate a survival prediction model in a cohort of inoperable stage I-III NSCLC patients treated with radiotherapy. Methods Data from inoperable stage I-III NSCLC patients diagnosed from 1/1/2016 to 31/12/2017 were collected from three radiation oncology clinics. Patient, tumour and treatment-related variables were selected for model inclusion using univariate and multivariate analysis. Cox proportional hazards regression was used to develop a 2-year overall survival prediction model, the South West Sydney Model (SWSM) in one clinic (n = 117) and validated in the other clinics (n = 144). Model performance, assessed internally and on one independent dataset, was expressed as Harrell’s concordance index (c-index). Results The SWSM contained five variables: Eastern Cooperative Oncology Group performance status, diffusing capacity of the lung for carbon monoxide, histological diagnosis, tumour lobe and equivalent dose in 2 Gy fractions. The SWSM yielded a c-index of 0.70 on internal validation and 0.72 on external validation. Survival probability could be stratified into three groups using a risk score derived from the model. Conclusions A 2-year survival model with good discrimination was developed. The model included tumour lobe as a novel variable and has the potential to guide treatment decisions. Further validation is needed in a larger patient cohort.
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Affiliation(s)
- Natalie Si-Yi Lee
- South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Jesmin Shafiq
- South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia.,Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
| | - Matthew Field
- South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia.,Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
| | | | - Suganthy Varadarajan
- Blacktown Cancer and Haematology Centre, Blacktown Hospital, Blacktown, NSW, Australia
| | | | - Eric Hau
- Blacktown Cancer and Haematology Centre, Blacktown Hospital, Blacktown, NSW, Australia.,Crown Princess Mary Cancer Centre, Westmead Hospital, Westmead, NSW, Australia.,University of Sydney, Sydney, NSW, Australia
| | - Shalini Kavita Vinod
- South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia. .,Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia. .,Cancer Therapy Centre, Liverpool Hospital, Locked Bag 7103, Liverpool BC, NSW, 1871, Australia.
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24
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Identification and Validation of 7-lncRNA Signature of Epigenetic Disorders by Comprehensive Epigenetic Analysis. DISEASE MARKERS 2022; 2022:5118444. [PMID: 35237359 PMCID: PMC8885251 DOI: 10.1155/2022/5118444] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 01/27/2022] [Accepted: 02/07/2022] [Indexed: 12/30/2022]
Abstract
The survival rate of patients with lung adenocarcinoma (LUAD) is low. This study analyzed the correlation between the expression of long noncoding RNA (lncRNA) and epigenetic alterations along with the investigation of the prognostic value of these outcomes for LUAD. Differentially expressed lncRNAs were identified based on multiomic data and positively related genes using DESeq2 in R, differentially histone-modifying genes specific to LUAD based on histone modification data, gene enhancers from information collected from the FANTOM5 (Function Annotation Of The Mammalian Genome-5) (fantom.gsc.riken.jp/5) human enhancer database, gene promoters using the ChIPseeker and the human lincRNAs Transcripts database in R, and differentially methylated regions (DMRs) using Bumphunter in R. Overall survival was estimated by Kaplan-Meier, comparisons were performed among groups using log-rank tests to derive differences between sample subclasses, and epigenetic lncRNAs (epi-lncRNAs) potentially relevant to LUAD prognosis were identified. A total of seven dysregulated epi-lncRNAs in LUAD were identified by comparing histone modifications and alterations in histone methylation regions on lncRNA promoter and enhancer elements, including H3K4me2, H3K27me3, H3K4me1, H3K9me3, H4K20me1, H3K9ac, H3K79me2, H3K27ac, H3K4me3, and H3K36me3. Furthermore, 69 LUAD-specific dysregulated epi-lncRNAs were identified. Moreover, lncRNAs-based prognostic analysis of LUAD samples was performed and explored that seven of these lncRNAs, including A2M-AS1, AL161431.1, DDX11-AS1, FAM83A-AS1, MHENCR, MNX1-AS1, and NKILA (7-EpiLncRNA), showed the potential to serve as markers for LUAD prognosis. Additionally, patients having a high 7-EpiLncRNA score showed a generally more unfavorable prognosis compared with those which scored lower. Seven lncRNAs were identified as markers of prognosis in patients with LUAD. The outcomes of this research will help us understand epigenetically aberrant regulation of lncRNA expression in LUAD in a better way and have implications for research advances in the regulatory role of lncRNAs in LUAD.
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25
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Yan J, Yuan W, Zhang J, Li L, Zhang L, Zhang X, Zhang M. Identification and Validation of a Prognostic Prediction Model in Diffuse Large B-Cell Lymphoma. Front Endocrinol (Lausanne) 2022; 13:846357. [PMID: 35498426 PMCID: PMC9048048 DOI: 10.3389/fendo.2022.846357] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 02/25/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous group with varied pathophysiological, genetic, and clinical features, accounting for approximately one-third of all lymphoma cases worldwide. Notwithstanding that unprecedented scientific progress has been achieved over the years, the survival of DLBCL patients remains low, emphasizing the need to develop novel prognostic biomarkers for early risk stratification and treatment optimization. METHOD In this study, we screened genes related to the overall survival (OS) of DLBCL patients in datasets GSE117556, GSE10846, and GSE31312 using univariate Cox analysis. Survival-related genes among the three datasets were screened according to the criteria: hazard ratio (HR) >1 or <1 and p-value <0.01. Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analysis were used to optimize and establish the final gene risk prediction model. The TCGA-NCICCR datasets and our clinical cohort were used to validate the performance of the prediction model. CIBERSORT and ssGSEA algorithms were used to estimate immune scores in the high- and low-risk groups. RESULTS We constructed an eight-gene prognostic signature that could reliably predict the clinical outcome in training, testing, and validation cohorts. Our prognostic signature also performed distinguished areas under the ROC curve in each dataset, respectively. After stratification based on clinical characteristics such as cell-of-origin (COO), age, eastern cooperative oncology group (ECOG) performance status, international prognostic index (IPI), stage, and MYC/BCL2 expression, the difference in OS between the high- and low-risk groups was statistically significant. Next, univariate and multivariate analyses revealed that the risk score model had a significant prediction value. Finally, a nomogram was established to visualize the prediction model. Of note, we found that the low-risk group was enriched with immune cells. CONCLUSION In summary, we identified an eight-gene prognostic prediction model that can effectively predict survival outcomes of patients with DLBCL and built a nomogram to visualize the perdition model. We also explored immune alterations between high- and low-risk groups.
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Affiliation(s)
- Jiaqin Yan
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Wei Yuan
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- The Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
- State Key Laboratory of Esophageal Cancer Prevention and Treatment, Zhengzhou University, Zhengzhou, China
| | - Junhui Zhang
- Otorhinolaryngology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ling Li
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lei Zhang
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xudong Zhang
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Mingzhi Zhang
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Mingzhi Zhang,
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Meng J, Cao L, Song H, Chen L, Qu Z. Integrated analysis of gene expression and DNA methylation datasets identified key genes and a 6-gene prognostic signature for primary lung adenocarcinoma. Genet Mol Biol 2021; 44:e20200465. [PMID: 34787244 PMCID: PMC8596225 DOI: 10.1590/1678-4685-gmb-2020-0465] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 08/20/2021] [Indexed: 12/13/2022] Open
Abstract
Lung adenocarcinoma (LUAD) is the main subtype of non-small cell lung cancer with a poor survival prognosis. In our study, gene expression, DNA methylation, and clinicopathological data of primary LUAD were utilized to identify potential prognostic markers for LUAD, which were recruited from The Cancer Genome Atlas (TCGA) database. Univariate regression analysis showed that there were 21 methylation-associated DEGs related to overall survival (OS), including 9 down- and 12 up-regulated genes. The 12 up-regulated genes with hypomethylation may be risky genes, whereas the other 9 down-regulated genes with hypermethylation might be protective genes. By using the Step-wise multivariate Cox analysis, a methylation-associated 6-gene (consisting of CCL20, F2, GNPNAT1, NT5E, B3GALT2, and VSIG2) prognostic signature was constructed and the risk score based on this gene signature classified patients into high- or low-risk groups. Patients of the high-risk group had shorter OS than those of the low-risk group in both the training and validation cohort. Multivariate Cox analysis and the stratified analysis revealed that the risk score was an independent prognostic factor for LUAD patients. The methylation-associated gene signature may serve as a prognostic factor for LUAD patients and the represent hypermethylated or hypomethylated genes might be potential targets for LUAD therapy.
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Affiliation(s)
- Jing Meng
- Inner Mongolia People's Hospital, Department of Stomatology, Hohhot, China
| | - Lei Cao
- Inner Mongolia People's Hospital, Department of Clinical Medical Research Center, Hohhot, China
| | - Huifang Song
- Inner Mongolia People's Hospital, Department of Respiratory and Critical Care Medicine, Hohhot, China
| | - Lichun Chen
- Inner Mongolia People's Hospital, Department of Stomatology, Hohhot, China
| | - Zhiguo Qu
- Inner Mongolia People's Hospital, Department of Stomatology, Hohhot, China
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27
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Giannos P, Kechagias KS, Gal A. Identification of Prognostic Gene Biomarkers in Non-Small Cell Lung Cancer Progression by Integrated Bioinformatics Analysis. BIOLOGY 2021; 10:1200. [PMID: 34827193 PMCID: PMC8615219 DOI: 10.3390/biology10111200] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 11/08/2021] [Accepted: 11/15/2021] [Indexed: 01/06/2023]
Abstract
The progression of non-small cell lung cancer (NSCLC) is linked to epithelial-mesenchymal transition (EMT), a biologic process that enables tumor cells to acquire a migratory phenotype and resistance to chemo- and immunotherapies. Discovery of novel biomarkers in NSCLC progression is essential for improved prognosis and pharmacological interventions. In the current study, we performed an integrated bioinformatics analysis on gene expression datasets of TGF-β-induced EMT in NSCLC cells to identify novel gene biomarkers and elucidate their regulation in NSCLC progression. The gene expression datasets were extracted from the NCBI Gene Expression Omnibus repository, and differentially expressed genes (DEGs) between TGF-β-treated and untreated NSCLC cells were retrieved. A protein-protein interaction network was constructed and hub genes were identified. Functional and pathway enrichment analyses were conducted on module DEGs, and a correlation between the expression levels of module genes and survival of NSCLC patients was evaluated. Prediction of interactions of the biomarker genes with transcription factors and miRNAs was also carried out. We described four protein clusters in which DEGs were associated with ubiquitination (Module 1), regulation of cell death and cell adhesions (Module 2), oxidation-reduction reactions of aerobic respiration (Module 3) and mitochondrial translation (Module 4). From the module genes, we identified ten prognostic gene biomarkers in NSCLC. Low expression levels of KCTD6, KBTBD7, LMO7, SPSB2, RNF19A, FOXA2, DHTKD1, CDH1 and PDHB and high expression level of KLHL25 were associated with reduced overall survival of NSCLC patients. Most of these biomarker genes were involved in protein ubiquitination. The regulatory network of the gene biomarkers revealed their interaction with tumor suppressor miRNAs and transcription factors involved in the mechanisms of cancer progression. This ten-gene prognostic signature can be useful to improve risk prediction and therapeutic strategies in NSCLC. Our analysis also highlights the importance of deregulation of ubiquitination in EMT-associated NSCLC progression.
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Affiliation(s)
- Panagiotis Giannos
- School of Applied Sciences, University of Brighton, Lewes Road, Brighton BN2 4GJ, UK
- Department of Life Sciences, Faculty of Natural Sciences, Imperial College London, South Kensington, London SW7 2AZ, UK
| | - Konstantinos S. Kechagias
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London W12 0NN, UK;
| | - Annamaria Gal
- School of Applied Sciences, University of Brighton, Lewes Road, Brighton BN2 4GJ, UK
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Wang H, Wang X, Xu L, Cao H, Zhang J. Nonnegative matrix factorization-based bioinformatics analysis reveals that TPX2 and SELENBP1 are two predictors of the inner sub-consensuses of lung adenocarcinoma. Cancer Med 2021; 10:9058-9077. [PMID: 34734491 PMCID: PMC8683537 DOI: 10.1002/cam4.4386] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 09/21/2021] [Accepted: 10/14/2021] [Indexed: 12/24/2022] Open
Abstract
Background Lung adenocarcinoma (LUAD) is a heterogeneous disease. However the inner sub‐groups of LUAD have not been fully studied. Markers predicted the sub‐groups and prognosis of LUAD are badly needed. Aims To identify biomarkers associated with the sub‐groups and prognosis of LUAD. Materials and Methods Using nonnegative matrix factorization (NMF) clustering, LUAD patients from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) datasets and LUAD cell lines from Genomics of Drug Sensitivity in Cancer (GDSC) dataset were divided into different sub‐consensuses based on the gene expression profiling. The overall survival of LUAD patients in each sub‐consensus was determined by Kaplan‐Meier survival analysis. The common genes which were differentially expressed in each sub‐consensus of LUAD patients and LUAD cell lines were identified using TBtools. The predictive accuracy of TPX2 and SELENBP1 for theinner sub‐consensuses of LUAD was determined by Receiver operator characteristic (ROC) analysis. The Kaplan‐Meier survival analysis was also used to test the prognostic significance of TPX2 and SELENBP1 in LUAD patients. Results Using nonnegative matrix factorization clustering, LUAD patients in The Cancer Genome Atlas (TCGA), GSE30219, GSE42127, GSE50081, GSE68465, and GSE72094 datasets were divided into three sub‐consensuses. Sub‐consensus3 LUAD patients were with low overall survival and were with high TP53 mutations. Similarly, LUAD cell lines were also divided into three sub‐consensuses by NMF method, and sub‐consensus2 cell lines were resistant to EGFR inhibitors. Identification of the common genes which were differentially expressed in different sub‐consensuses of LUAD patients and LUAD cell lines revealed that TPX2 was highly expressed in sub‐consensus3 LUAD patients and sub‐consensus2 LUAD cell lines. On the contrary, SELENBP1 was highly expressed in sub‐consensus1 LUAD patients and sub‐consensus1 LUAD cell lines. The expression levels of TPX2 and SELENBP1 could distinguish sub‐consensus3 LUAD patients or sub‐consensus2 LUAD cell lines from other sub‐consensuses of LUAD patients or cell lines. Moreover, compared with normal lung tissues, TPX2 was highly expressed, while, SELENBP1 was lowly expressed in LUAD tissues. Furthermore, the higher expression levels of TPX2 were associated with the lower relapse‐free survival and the lower overall survival of LUAD patients. While, the higher expression levels of SELENBP1 were associated with the higher relapse‐free survival and higher overall survival. At last, we showed that TP53 mutant LUAD patients were with higher TPX2 and lower SELENBP1 expressions. Discussion Both iCluster and NMF method are proved to be robust LUAD classification systems. However, the LUAD patients in different iclusters had no significant clinical overall survival, while, sub‐consensus3 LUAD patients from NMF classification were with lower overall survival than other sub‐consensuses. Conclusions By integrated analysis of 1765 LUAD patients and 64 LUAD cell lines, we showed that NMF was a robust inner sub‐consensuses classification method of LUAD. TPX2 and SELENBP1 were differentially expressed in different LUAD sub‐ consensuses, and predicted the inner sub‐consensuses of LUAD with high accuracy. TPX2 was an unfavorable prognostic biomarker of LUAD which was up‐regulated in LUAD tissues and associated with the low overall survival of LUAD. SELENBP1 was a favorable prognostic biomarker of LUAD which was down‐regulated in LUAD tissues and associated with the prolonged overall survival of LUAD.
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Affiliation(s)
- Haiwei Wang
- Fujian Key Laboratory for Prenatal Diagnosis and Birth Defect, Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.,Key Laboratory of Technical Evaluation of Fertility Regulation for Non-human Primate, National Health and Family Planning Commission, Fuzhou, Fujian, China
| | - Xinrui Wang
- Fujian Key Laboratory for Prenatal Diagnosis and Birth Defect, Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.,Key Laboratory of Technical Evaluation of Fertility Regulation for Non-human Primate, National Health and Family Planning Commission, Fuzhou, Fujian, China
| | - Liangpu Xu
- Fujian Key Laboratory for Prenatal Diagnosis and Birth Defect, Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.,Key Laboratory of Technical Evaluation of Fertility Regulation for Non-human Primate, National Health and Family Planning Commission, Fuzhou, Fujian, China
| | - Hua Cao
- Fujian Key Laboratory for Prenatal Diagnosis and Birth Defect, Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.,Key Laboratory of Technical Evaluation of Fertility Regulation for Non-human Primate, National Health and Family Planning Commission, Fuzhou, Fujian, China
| | - Ji Zhang
- State Key Laboratory for Medical Genomics, Shanghai Institute of Hematology, Rui-Jin Hospital Affiliated to School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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29
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Shi J, Chen Y, Wang Z, Guo J, Tong C, Tong J, Hu W, Li C, Li X. Comprehensive Bioinformatics Analysis to Identify the Gene HMMR Associated With Lung Adenocarcinoma Prognosis and Its Mechanism of Action in Multiple Cancers. Front Oncol 2021; 11:712795. [PMID: 34692489 PMCID: PMC8526859 DOI: 10.3389/fonc.2021.712795] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 08/25/2021] [Indexed: 12/09/2022] Open
Abstract
Background Lung cancer is the third most frequently diagnosed cancer in the world, with lung adenocarcinoma (LUAD) as the most common pathological type. But studies on the predictive effect of a single gene on LUAD are limited. We aimed to discover new predictive markers for LUAD. Methods Differentially high-expressed genes at each stage were obtained from the TCGA and GTEx databases. The functions of these genes were investigated through GO enrichment and KEGG pathway analyses. Then, the key genes were selected by applying whole gene overall survival time. The expression of the key gene was studied in LUAD, and survival analysis was performed using Kaplan-Meier mapper, followed by univariate and multifactorial COX analysis. Finally, the gene expression and its prognostic significance in the pan-cancer were examined. Results A total of 10,106 DEGs were obtained from the two datasets. The top 266 differentially upregulated genes intersected with the top 1,497 overall survival-related genes, and 87 key genes were identified. High-expressed HMMR was associated with a poor prognosis of LUAD. Univariate and multifactorial Cox analysis showed that HMMR was an independent prognostic factor for LUAD patients. A high HMMR expression was strongly associated with the overall survival (OS) and disease-specific survival (DSS) in 11 cancer types and with poorer OS, DSS, and PFI in 10 cancer types. Conclusion HMMR may be an independent prognostic indicator and an important biomarker in diagnosing and predicting the survival of LUAD patients. Also, HMMR may be a key predictor of a variety of cancers.
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Affiliation(s)
- Jianguang Shi
- Thoracic Surgery Department, Ningbo First Hospital, Ningbo, China
| | - Yingqi Chen
- Thoracic Surgery Department, Ningbo First Hospital, Ningbo, China
| | - Zishan Wang
- Thoracic Surgery Department, Ningbo First Hospital, Ningbo, China
| | - Jin Guo
- Thoracic Surgery Department, Ningbo First Hospital, Ningbo, China
| | - Changyong Tong
- Thoracic Surgery Department, Ningbo First Hospital, Ningbo, China
| | - Jingjie Tong
- Thoracic Surgery Department, Ningbo First Hospital, Ningbo, China
| | - Wentao Hu
- Thoracic Surgery Department, Ningbo First Hospital, Ningbo, China
| | - Chenwei Li
- Thoracic Surgery Department, Ningbo First Hospital, Ningbo, China
| | - Xinjian Li
- Thoracic Surgery Department, Ningbo First Hospital, Ningbo, China
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Liu L, Xu K, Zhou Y. Development of a novel embryonic germline gene-related prognostic model of lung adenocarcinoma. PeerJ 2021; 9:e12257. [PMID: 34721973 PMCID: PMC8542372 DOI: 10.7717/peerj.12257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 09/15/2021] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Emerging evidence implicates the correlation of embryonic germline genes with the tumor progress and patient's outcome. However, the prognostic value of these genes in lung adenocarcinoma (LUAD) has not been fully studied. Here we systematically evaluated this issue, and constructed a novel signature and a nomogram associated with embryonic germline genes for predicting the outcomes of lung adenocarcinoma. METHODS The LUAD cohorts retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were used as training set and testing set, respectively. The embryonic germline genes were downloaded from the website https://venn.lodder.dev. Then, the differentially expressed embryonic germline genes (DEGGs) between the tumor and normal samples were identified by limma package. The functional enrichment and pathway analyses were also performed by clusterProfiler package. The prognostic model was constructed by the least absolute shrinkage and selection operator (LASSO)-Cox regression method. Survival and Receiver Operating Characteristic (ROC) analyses were performed to validate the model using training set and four testing GEO datasets. Finally, a prognostic nomogram based on the signature genes was constructed using multivariate regression method. RESULTS Among the identified 269 DEGGs, 249 were up-regulated and 20 were down-regulated. GO and KEGG analyses revealed that these DEGGs were mainly enriched in the process of cell proliferation and DNA damage repair. Then, 103 DEGGs with prognostic value were identified by univariate Cox regression and further filtered by LASSO method. The resulting sixteen DEGGs were included in step multivariate Cox regression and an eleven embryonic germline gene related signature (EGRS) was constructed. The model could robustly stratify the LUAD patients into high-risk and low-risk groups in both training and testing sets, and low-risk patients had much better outcomes. The multi-ROC analysis also showed that the EGRS model had the best predictive efficacy compared with other common clinicopathological factors. The EGRS model also showed robust predictive ability in four independent external datasets, and the area under curve (AUC) was 0.726 (GSE30219), 0.764 (GSE50081), 0.657 (GSE37745) and 0.668 (GSE72094). More importantly, the expression level of some genes in EGRS has a significant correlation with the progression of LUAD clinicopathology, suggesting these genes might play an important role in the progression of LUAD. Finally, based on EGRS genes, we built and calibrated a nomogram for conveniently evaluating patients' outcomes.
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Affiliation(s)
- Linjun Liu
- Department of Biotechnology, College of Life Science & Chemistry, Beijing University of Technology, Chaoyang, Beijing, China
| | - Ke Xu
- NHC Key Laboratory of Biosafety, China CDC, National Institute for Viral Disease Control and Prevention, Beijing, China
| | - Yubai Zhou
- Department of Biotechnology, College of Life Science & Chemistry, Beijing University of Technology, Chaoyang, Beijing, China
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Liu L, He H, Peng Y, Yang Z, Gao S. A four-gene prognostic signature for predicting the overall survival of patients with lung adenocarcinoma. PeerJ 2021; 9:e11911. [PMID: 34631307 PMCID: PMC8465999 DOI: 10.7717/peerj.11911] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 07/14/2021] [Indexed: 01/12/2023] Open
Abstract
Background The prognosis of patients for lung adenocarcinoma (LUAD) is known to vary widely; the 5-year overall survival rate is just 63% even for the pathological IA stage. Thus, in order to identify high-risk patients and facilitate clinical decision making, it is vital that we identify new prognostic markers that can be used alongside TNM staging to facilitate risk stratification. Methods We used mRNA expression from The Cancer Genome Atlas (TCGA) cohort to identify a prognostic gene signature and combined this with clinical data to develop a predictive model for the prognosis of patients for lung adenocarcinoma. Kaplan-Meier curves, Lasso regression, and Cox regression, were used to identify specific prognostic genes. The model was assessed via the area under the receiver operating characteristic curve (AUC-ROC) and validated in an independent dataset (GSE50081) from the Gene Expression Omnibus (GEO). Results Our analyses identified a four-gene prognostic signature (CENPH, MYLIP, PITX3, and TRAF3IP3) that was associated with the overall survival of patients with T1-4N0-2M0 in the TCGA dataset. Multivariate regression suggested that the total risk score for the four genes represented an independent prognostic factor for the TCGA and GEO cohorts; the hazard ratio (HR) (high risk group vs low risk group) were 2.34 (p < 0.001) and 2.10 (p = 0.017). Immune infiltration estimations, as determined by an online tool (TIMER2.0) showed that CD4+ T cells were in relative abundance in the high risk group compared to the low risk group in both of the two cohorts (both p < 0.001). We established a composite prognostic model for predicting OS, combined with risk-grouping and clinical factors. The AUCs for 1-, 3-, 5- year OS in the training set were 0.750, 0.737, and 0.719; and were 0.645, 0.766, and 0.725 in the validation set. The calibration curves showed a good match between the predicted probabilities and the actual probabilities. Conclusions We identified a four-gene predictive signature which represents an independent prognostic factor and can be used to identify high-risk patients from different TNM stages of LUAD. A new prognostic model that combines a prognostic gene signature with clinical features exhibited better discriminatory ability for OS than traditional TNM staging.
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Affiliation(s)
- Lei Liu
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/ Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Huayu He
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/ Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yue Peng
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/ Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhenlin Yang
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/ Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shugeng Gao
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/ Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Zheng H, Liu H, Lu Y, Li H. Identification of a Novel Signature Predicting Overall Survival in Head and Neck Squamous Cell Carcinoma. Front Surg 2021; 8:717084. [PMID: 34631779 PMCID: PMC8498039 DOI: 10.3389/fsurg.2021.717084] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 08/27/2021] [Indexed: 12/21/2022] Open
Abstract
Background: Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous tumor with a high incidence and poor prognosis. Therefore, effective predictive models are needed to evaluate patient outcomes and optimize treatment. Methods: Robust Rank Aggregation (RRA) method was used to identify highly robust differentially-expressed genes (DEGs) between HNSCC and normal tissue in 9 GEO and TCGA datasets. Univariate Cox regression analysis and Lasso Cox regression analysis were performed to identify DEGs related to the Overall survival (OS) and to construct a prognostic gene signature (HNSCCSig). External validation was performed using GSE65858 dataset. Moreover, comprehensive bioinformatics analyses were used to identify the association between HNSCCSig and tumor immune environment. Results: A total of 257 reliable DEGs were identified by differentially analysis result of TCGA and GSE65858 datasets. The HNSCCSig including 7 mRNAs (SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3) were developed and validated to identify high-risk group who had a worse OS than low-risk group in TCGA and GSE65858 datasets. Cox regression analysis showed that the HNSCCSig could independently predict OS in both the TCGA and the GSE65858 datasets. Further research demonstrated that the infiltration bundance of CD8 + T cells, B cells, neutrophils, and NK cells were significantly lower in the high-risk group. A nomogram was also constructed by combining the HNSCCSig and clinical characters. Conclusion: We established and validated the HNSCCSig consisting of SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3. A nomogram combining HNSCCSig and some clinical parameters was constructed to identify high-risk HNSCC-patients with poor prognosis.
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Affiliation(s)
- Haige Zheng
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Huixian Liu
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yumin Lu
- The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, China
| | - Hengguo Li
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
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Kumar D, Patel SA, Khan R, Chawla S, Mohapatra N, Dixit M. IQ Motif-Containing GTPase-Activating Protein 2 Inhibits Breast Cancer Angiogenesis By Suppressing VEGFR2-AKT Signaling. Mol Cancer Res 2021; 20:77-91. [PMID: 34615693 DOI: 10.1158/1541-7786.mcr-20-1044] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 06/17/2021] [Accepted: 10/01/2021] [Indexed: 12/24/2022]
Abstract
Antiangiogenesis cancer therapies are facing setbacks due to side effects and resistance. Parallel targeting of multiple pathways can help in the development of more effective therapies. This requires the discovery of new molecules that can regulate multiple cellular processes. Our study has recently established the association of reduced IQGAP2 expression in breast cancer with EMT and poor prognosis of the patient. Existing literature indirectly suggests the role of IQGAP2 in angiogenesis that is still unexplored. In this study, we searched the role of IQGAP2 in tumor angiogenesis in a comprehensive manner using cell culture, patients, and animal models. Depletion of IQGAP2 in breast cancer cells increased proliferation, migration, and tubulogenesis of HUVECs. Findings were validated in ex ovo CAM, Matrigel plug and skin wound-healing assays in mouse model, showing that the reduction of IQGAP2 significantly increased angiogenesis. As a confirmation, IHC analysis of the patient's tissues showed a negative correlation of IQGAP2 expression with the microvessel density. Mechanistically, loss of IQGAP2 appeared to activate VEGF-A via ERK activation in tumor cells, which activated the VEGFR2-AKT axis in HUVECs. IMPLICATIONS: The findings of this study suggest the antiangiogenic properties of IQGAP2 in breast cancer. The Dual effect of IQGAP2 on EMT and angiogenesis makes it a potential target for anticancer therapy.
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Affiliation(s)
- Dinesh Kumar
- School of Biological Sciences, National Institute of Science Education and Research, Bhubaneswar, HBNI, Khurda, Odisha, India
| | - Saket Awadhesbhai Patel
- School of Biological Sciences, National Institute of Science Education and Research, Bhubaneswar, HBNI, Khurda, Odisha, India
| | - Rehan Khan
- School of Biological Sciences, National Institute of Science Education and Research, Bhubaneswar, HBNI, Khurda, Odisha, India
| | - Saurabh Chawla
- School of Biological Sciences, National Institute of Science Education and Research, Bhubaneswar, HBNI, Khurda, Odisha, India
| | | | - Manjusha Dixit
- School of Biological Sciences, National Institute of Science Education and Research, Bhubaneswar, HBNI, Khurda, Odisha, India.
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Kim YM, Hong S. Controversial roles of cold‑inducible RNA‑binding protein in human cancer (Review). Int J Oncol 2021; 59:91. [PMID: 34558638 DOI: 10.3892/ijo.2021.5271] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 09/06/2021] [Indexed: 11/05/2022] Open
Abstract
Cold‑inducible RNA‑binding protein (CIRBP) is a cold‑shock protein comprised of an RNA‑binding motif that is induced by several stressors, such as cold shock, UV radiation, nutrient deprivation, reactive oxygen species and hypoxia. CIRBP can modulate post‑transcriptional regulation of target mRNA, which is required to control DNA repair, circadian rhythms, cell growth, telomere integrity and cardiac physiology. In addition, the crucial function of CIRBP in various human diseases, including cancers and inflammatory disease, has been reported. Although CIRBP is primarily considered to be an oncogene, it may also serve a role in tumor suppression. In the present study, the controversial roles of CIRBP in various human cancers is summarized, with a focus on the interconnectivity between CIRBP and its target mRNAs involved in tumorigenesis. CIRBP may represent an important prognostic marker and therapeutic target for cancer therapy.
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Affiliation(s)
- Young-Mi Kim
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon University, Incheon 21999, Republic of Korea
| | - Suntaek Hong
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon University, Incheon 21999, Republic of Korea
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Li X, Zuo H, Zhang L, Sun Q, Xin Y, Zhang L. Validating HMMR Expression and Its Prognostic Significance in Lung Adenocarcinoma Based on Data Mining and Bioinformatics Methods. Front Oncol 2021; 11:720302. [PMID: 34527588 PMCID: PMC8435795 DOI: 10.3389/fonc.2021.720302] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 08/10/2021] [Indexed: 12/25/2022] Open
Abstract
Hyaluronic acid-mediated motility receptor (HMMR), a tumor-related gene, plays a vital role in the occurrence and progression of various cancers. This research is aimed to reveal the effect of HMMR in lung adenocarcinoma (LUAD). We first obtained the gene expression profiles and clinical data of patients with LUAD from The Cancer Genome Atlas (TCGA) database. Then, based on the TCGA cohort, the HMMR expression difference between LUAD tissues and nontumor tissues was detected and verified with public tissue microarrays (TMAs), clinical LUAD specimen cohort, and Gene Expression Omnibus (GEO) cohort. Logistic regression analysis and chi-square test were adopted to study the correlation between HMMR expression and clinicopathological parameters. The effect of HMMR expression on survival was evaluated by Kaplan–Meier survival analysis and using the Cox regression model. Furthermore, Gene Set Enrichment Analysis (GSEA) was utilized to screen out signaling pathways related to LUAD and the co-expression analysis was employed to build the protein–protein interaction (PPI) network. The HMMR expression level in LUAD tissues was dramatically higher than that in nontumor tissues. Logistic regression analysis and chi-square test demonstrated that the high HMMR expression in LUAD has relation with gender, pathological stage, T classification, lymph node metastasis, and distant metastasis. The Kaplan–Meier curve suggested a poor prognosis for LUAD patients with high HMMR expression. Multivariate analysis implied that the high HMMR expression was a vital independent predictor of poor overall survival (OS). GSEA indicated that a total of 15 signaling pathways were enriched in samples with the high HMMR expression phenotype. The PPI network gave 10 genes co-expressed with HMMR. HMMR may be an oncogene in LUAD and is expected to become a potential prognostic indicator and therapeutic target for LUAD.
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Affiliation(s)
- Xia Li
- First Clinical College, Xuzhou Medical University, Xuzhou, China.,Department of Radiation Oncology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Haiwei Zuo
- School of Medical Information & Engineering, Xuzhou Medical University, Xuzhou, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Qiuwen Sun
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, China
| | - Yong Xin
- Department of Radiation Oncology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.,Cancer Institute, Xuzhou Medical University, Xuzhou, China
| | - Longzhen Zhang
- Department of Radiation Oncology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.,Cancer Institute, Xuzhou Medical University, Xuzhou, China
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Liu X, Shang X, Li J, Zhang S. The Prognosis and Immune Checkpoint Blockade Efficacy Prediction of Tumor-Infiltrating Immune Cells in Lung Cancer. Front Cell Dev Biol 2021; 9:707143. [PMID: 34422829 PMCID: PMC8370893 DOI: 10.3389/fcell.2021.707143] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 06/30/2021] [Indexed: 01/11/2023] Open
Abstract
Backgrounds The high morbidity and mortality of lung cancer are serious public health problems. The prognosis of lung cancer and whether to apply immune checkpoint blockade (ICB) are currently urgent problems to be solved. Methods Using R software, we performed Kaplan–Meier (K-M) analysis, Cox regression analysis, functional enrichment analysis, Spearman correlation analysis, and the single-sample gene set enrichment analysis. Results On the Tumor IMmune Estimation Resource (TIMER2.0) website, we calculated the abundance of tumor-infiltrating immune cells (TIICs) of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) patients. B cell and myeloid dendritic cell (DC1) were independent prognostic factors for LUAD and LUSC patients, respectively. Enrichment analysis confirmed that genes highly related to B cell or DC1 were closely related to the immune activation of lung cancer patients. In terms of adaptive immune resistance markers, CD8A, CD8B, immunomodulators (immunostimulants, major histocompatibility complex, receptors, and chemokines), immune-related pathways, tumor microenvironment score, and TIICs, high B cell/DC1 infiltration tissue was inflamed and immune-activated and might benefit more from the ICB. Genes most related to B cell [CD19, toll-like receptor 10 (TLR10), and Fc receptor-like A (FCRLA)] and DC1 (ITGB2, LAPTM5, and SLC7A7) partially clarified the roles of B cell/DC1 in predicting ICB efficacy. Among the 186 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, there were three and four KEGG pathways, which partially explained the molecular mechanisms by which B cell and DC1 simultaneously predicted the prognosis and efficacy of immunotherapy, respectively. Among five immune subtypes, the abundance of B cell/DC1 and expression of six hub genes were higher in immune C2, C3, and C6. Conclusion B cell and DC1 could predict the prognosis and ICB efficacy of LUAD and LUSC patients, respectively. The six hub genes and seven KEGG pathways might be novel immunotherapy targets. Immune C2, C3, and C6 subtypes of lung cancer patients might benefit more from ICB therapy.
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Affiliation(s)
- Xiangzheng Liu
- Department of Thoracic Surgery, Peking University First Hospital, Peking University, Beijing, China
| | - Xueqian Shang
- Department of Thoracic Surgery, Peking University First Hospital, Peking University, Beijing, China
| | - Jian Li
- Department of Thoracic Surgery, Peking University First Hospital, Peking University, Beijing, China
| | - Shijie Zhang
- Department of Thoracic Surgery, Peking University First Hospital, Peking University, Beijing, China
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Zheng W, Lu Y, Feng X, Yang C, Qiu L, Deng H, Xue Q, Sun K. Improving the overall survival prognosis prediction accuracy: A 9-gene signature in CRC patients. Cancer Med 2021; 10:5998-6009. [PMID: 34346563 PMCID: PMC8419765 DOI: 10.1002/cam4.4104] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 05/24/2021] [Accepted: 06/05/2021] [Indexed: 12/24/2022] Open
Abstract
Colorectal cancer (CRC) is a malignant tumor and morbidity rates are among the highest in the world. The variation in CRC patients' prognosis prompts an urgent need for new molecular biomarkers to improve the accuracy for predicting the CRC patients' prognosis or as a complement to the traditional TNM staging for clinical practice. CRC patients' gene expression data of HTSeq‐FPKM and matching clinical information were downloaded from The Cancer Genome Atlas (TCGA) datasets. Patients were randomly divided into a training dataset and a test dataset. By univariate and multivariate Cox regression survival analyses and Lasso regression analysis, a prediction model which divided each patient into high‐or low‐risk group was constructed. The differences in survival time between the two groups were compared by the Kaplan–Meier method and the log‐rank test. The weighted gene co‐expression network analysis (WGCNA) was used to explore the relationship between all the survival‐related genes. The survival outcomes of patients whose overall survival (OS) time were significantly lower in the high‐risk group than that in the low‐risk group both in the training and test datasets. Areas under the ROC curves which termed AUC values of our 9‐gene signature achieved 0.823 in the training dataset and 0.806 in the test dataset. A nomogram was constructed for clinical practice when we combined the 9‐gene signature with TNM stage and age to evaluate the survival time of patients with CRC, and the C‐index increased from 0.739 to 0.794. In conclusion, we identified nine novel biomarkers that not only are independent prognostic indexes for CRC patients but also can serve as a good supplement to traditional clinicopathological factors to more accurately evaluate the survival of CRC patients.
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Affiliation(s)
- Wenbo Zheng
- Department of General Surgery & Guangdong Province Key Laboratory of Precision Medicine for Gastrointestinal Tumor, The First School of Clinical Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yijia Lu
- Department of Obstetrics and Gynaecology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xiaochuang Feng
- Department of General Surgery & Guangdong Province Key Laboratory of Precision Medicine for Gastrointestinal Tumor, The First School of Clinical Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Chunzhao Yang
- Department of General Surgery & Guangdong Province Key Laboratory of Precision Medicine for Gastrointestinal Tumor, The First School of Clinical Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Ling Qiu
- Department of General Surgery & Guangdong Province Key Laboratory of Precision Medicine for Gastrointestinal Tumor, The First School of Clinical Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Haijun Deng
- Department of General Surgery & Guangdong Province Key Laboratory of Precision Medicine for Gastrointestinal Tumor, The First School of Clinical Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Qi Xue
- Department of General Surgery, Traditional Chinese and Western Medicine Hospital, Southern Medical University, Guangzhou, China
| | - Kai Sun
- Department of General Surgery & Guangdong Province Key Laboratory of Precision Medicine for Gastrointestinal Tumor, The First School of Clinical Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
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Bai H, Wang Y, Liu H, Lu J. Development of a Four-mRNA Expression-Based Prognostic Signature for Cutaneous Melanoma. Front Genet 2021; 12:680617. [PMID: 34335689 PMCID: PMC8320537 DOI: 10.3389/fgene.2021.680617] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 05/17/2021] [Indexed: 01/22/2023] Open
Abstract
We aim to find a biomarker that can effectively predict the prognosis of patients with cutaneous melanoma (CM). The RNA sequencing data of CM was downloaded from The Cancer Genome Atlas (TCGA) database and randomly divided into training group and test group. Survival statistical analysis and machine-learning approaches were performed on the RNA sequencing data of CM to develop a prognostic signature. Using univariable Cox proportional hazards regression, random survival forest algorithm, and receiver operating characteristic (ROC) in the training group, the four-mRNA signature including CD276, UQCRFS1, HAPLN3, and PIP4P1 was screened out. The four-mRNA signature could divide patients into low-risk and high-risk groups with different survival outcomes (log-rank p < 0.001). The predictive efficacy of the four-mRNA signature was confirmed in the test group, the whole TCGA group, and the independent GSE65904 (log-rank p < 0.05). The independence of the four-mRNA signature in prognostic prediction was demonstrated by multivariate Cox analysis. ROC and timeROC analyses showed that the efficiency of the signature in survival prediction was better than other clinical variables such as melanoma Clark level and tumor stage. This study highlights that the four-mRNA model could be used as a prognostic signature for CM patients with potential clinical application value.
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Affiliation(s)
- Haiya Bai
- Department of Female Plastic Surgery, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, China
| | - Youliang Wang
- Department of Pediatric Surgery, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, China
| | - Huimin Liu
- Department of Female Plastic Surgery, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, China
| | - Junyang Lu
- Department of Female Plastic Surgery, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, China
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Lei X, Zhang M, Guan B, Chen Q, Dong Z, Wang C. Identification of hub genes associated with prognosis, diagnosis, immune infiltration and therapeutic drug in liver cancer by integrated analysis. Hum Genomics 2021; 15:39. [PMID: 34187556 PMCID: PMC8243535 DOI: 10.1186/s40246-021-00341-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 06/16/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Liver cancer is one of the most common cancers and causes of cancer death worldwide. The objective was to elucidate novel hub genes which were benefit for diagnosis, prognosis, and targeted therapy in liver cancer via integrated analysis. METHODS GSE84402, GSE101685, and GSE112791 were filtered from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified by using the GEO2R. The GO and KEGG pathway of DEGs were analyzed in the DAVID. PPI and TF network of the DEGs were constructed by using the STRING, TRANSFAC, and Harmonizome. The relationship between hub genes and prognoses in liver cancer was analyzed in UALCAN based on The Cancer Genome Atlas (TCGA). The diagnostic value of hub genes was evaluated by ROC. The relationship between hub genes and tumor-infiltrate lymphocytes was analyzed in TIMER. The protein levels of hub genes were verified in HPA. The interaction between the hub genes and the drug were identified in DGIdb. RESULTS In total, 108 upregulated and 60 downregulated DEGs were enriched in 148 GO terms and 20 KEGG pathways. The mRNA levels and protein levels of CDK1, HMMR, PTTG1, and TTK were higher in liver cancer tissues compared to normal tissues, which showed excellent diagnostic and prognostic value. CDK1, HMMR, PTTG1, and TTK were positively correlated with tumor-infiltrate lymphocytes, which might involve tumor immune response. The CDK1, HMMR, and TTK had close interaction with anticancer agents. CONCLUSIONS The CDK1, HMMR, PTTG1, and TTK were hub genes in liver cancer; hence, they might be potential biomarkers for diagnosis, prognosis, and targeted therapy of liver cancer.
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Affiliation(s)
- Xinyi Lei
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of Jinan University, No.613 Huangpu Road West, Guangzhou, 510630, China
| | - Miao Zhang
- Department of Respiratory, the First Affiliated Hospital of Jinan University, Guangzhou, 510630, China
| | - Bingsheng Guan
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of Jinan University, No.613 Huangpu Road West, Guangzhou, 510630, China
| | - Qiang Chen
- Department of Oncology, the First Affiliated Hospital of Jinan University, Guangzhou, 510630, China
| | - Zhiyong Dong
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of Jinan University, No.613 Huangpu Road West, Guangzhou, 510630, China.
| | - Cunchuan Wang
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of Jinan University, No.613 Huangpu Road West, Guangzhou, 510630, China.
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Cheng Y, Hou K, Wang Y, Chen Y, Zheng X, Qi J, Yang B, Tang S, Han X, Shi D, Wang X, Liu Y, Hu X, Che X. Identification of Prognostic Signature and Gliclazide as Candidate Drugs in Lung Adenocarcinoma. Front Oncol 2021; 11:665276. [PMID: 34249701 PMCID: PMC8264429 DOI: 10.3389/fonc.2021.665276] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 06/04/2021] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer, with high incidence and mortality. To improve the curative effect and prolong the survival of patients, it is necessary to find new biomarkers to accurately predict the prognosis of patients and explore new strategy to treat high-risk LUAD. METHODS A comprehensive genome-wide profiling analysis was conducted using a retrospective pool of LUAD patient data from the previous datasets of Gene Expression Omnibus (GEO) including GSE18842, GSE19188, GSE40791 and GSE50081 and The Cancer Genome Atlas (TCGA). Differential gene analysis and Cox proportional hazard model were used to identify differentially expressed genes with survival significance as candidate prognostic genes. The Kaplan-Meier with log-rank test was used to assess survival difference. A risk score model was developed and validated using TCGA-LUAD and GSE50081. Additionally, The Connectivity Map (CMAP) was used to predict drugs for the treatment of LUAD. The anti-cancer effect and mechanism of its candidate drugs were studied in LUAD cell lines. RESULTS We identified a 5-gene signature (KIF20A, KLF4, KRT6A, LIFR and RGS13). Risk Score (RS) based on 5-gene signature was significantly associated with overall survival (OS). Nomogram combining RS with clinical pathology parameters could potently predict the prognosis of patients with LUAD. Moreover, gliclazide was identified as a candidate drug for the treatment of high-RS LUAD. Finally, gliclazide was shown to induce cell cycle arrest and apoptosis in LUAD cells possibly by targeting CCNB1, CCNB2, CDK1 and AURKA. CONCLUSION This study identified a 5-gene signature that can predict the prognosis of patients with LUAD, and Gliclazide as a potential therapeutic drug for LUAD. It provides a new direction for the prognosis and treatment of patients with LUAD.
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Affiliation(s)
- Yang Cheng
- Department of Respiratory and Infectious Disease of Geriatrics, The First Hospital of China Medical University, Shenyang, China
| | - Kezuo Hou
- Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, China
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China
- Liaoning Province Clinical Research Center for Cancer, The First Hospital of China Medical University, Shenyang, China
| | - Yizhe Wang
- Department of Respiratory and Infectious Disease of Geriatrics, The First Hospital of China Medical University, Shenyang, China
| | - Yang Chen
- Department of Respiratory and Infectious Disease of Geriatrics, The First Hospital of China Medical University, Shenyang, China
| | - Xueying Zheng
- Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, China
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China
- Liaoning Province Clinical Research Center for Cancer, The First Hospital of China Medical University, Shenyang, China
| | - Jianfei Qi
- Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland, Baltimore, MD, United States
| | - Bowen Yang
- Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, China
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China
- Liaoning Province Clinical Research Center for Cancer, The First Hospital of China Medical University, Shenyang, China
| | - Shiying Tang
- Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, China
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China
- Liaoning Province Clinical Research Center for Cancer, The First Hospital of China Medical University, Shenyang, China
| | - Xu Han
- Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, China
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China
- Liaoning Province Clinical Research Center for Cancer, The First Hospital of China Medical University, Shenyang, China
| | - Dongyao Shi
- Department of Respiratory and Infectious Disease of Geriatrics, The First Hospital of China Medical University, Shenyang, China
| | - Ximing Wang
- Department of Respiratory and Infectious Disease of Geriatrics, The First Hospital of China Medical University, Shenyang, China
| | - Yunpeng Liu
- Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, China
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China
- Liaoning Province Clinical Research Center for Cancer, The First Hospital of China Medical University, Shenyang, China
| | - Xuejun Hu
- Department of Respiratory and Infectious Disease of Geriatrics, The First Hospital of China Medical University, Shenyang, China
| | - Xiaofang Che
- Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, China
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China
- Liaoning Province Clinical Research Center for Cancer, The First Hospital of China Medical University, Shenyang, China
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Ren EH, Deng YJ, Yuan WH, Zhang GZ, Wu ZL, Li CY, Xie QQ. An Immune-Related Long Non-Coding RNA Signature to Predict the Prognosis of Ewing's Sarcoma Based on a Machine Learning Iterative Lasso Regression. Front Cell Dev Biol 2021; 9:651593. [PMID: 34124041 PMCID: PMC8187926 DOI: 10.3389/fcell.2021.651593] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 04/16/2021] [Indexed: 01/21/2023] Open
Abstract
The aim of this study was to construct a new immune-associated long non-coding RNA (lncRNA) signature to predict the prognosis of Ewing sarcoma (ES) and explore its molecular mechanisms. We downloaded transcriptome and clinical prognosis data from the Gene Expression Omnibus (GSE17679, which included 88 ES samples and 18 matched normal skeletal muscle samples), and used it as a training set to identify immune-related lncRNAs with different expression levels in ES. Univariable Cox regression was used to screen immune-related lncRNAs related to ES prognosis, and an immune-related lncRNA signature was constructed based on machine learning iterative lasso regression. An external verification set was used to confirm the predictive ability of the signature. Clinical feature subgroup analysis was used to explore whether the signature was an independent prognostic factor. In addition, CIBERSORT was used to explore immune cell infiltration in the high- and low-risk groups, and to analyze the correlations between the lncRNA signature and immune cell levels. Gene set enrichment and variation analyses were used to explore the possible regulatory mechanisms of the immune-related lncRNAs in ES. We also analyzed the expression of 17 common immunotherapy targets in the high- and low-risk groups to identify any that may be regulated by immune-related lncRNAs. We screened 35 immune-related lncRNAs by univariate Cox regression. Based on this, an immune-related 11-lncRNA signature was generated by machine learning iterative lasso regression. Analysis of the external validation set confirmed its high predictive ability. DPP10 antisense RNA 3 was negatively correlated with resting dendritic cell, neutrophil, and γδ T cell infiltration, and long intergenic non-protein coding RNA 1398 was positively correlated with resting dendritic cells and M2 macrophages. These lncRNAs may affect ES prognosis by regulating GSE17721_CTRL_VS_PAM3CSK4_12H_BMDC_UP, GSE2770_IL4_ACT_VS_ACT_CD4_TCELL_48H_UP, GSE29615_CTRL_VS_DAY3_ LAIV_IFLU_VACCINE_PBMC_UP, complement signaling, interleukin 2-signal transducer and activator of transcription 5 signaling, and protein secretion. The immune-related 11-lncRNA signature may also have regulatory effects on the immunotherapy targets CD40 molecule, CD70 molecule, and CD276 molecule. In conclusion, we constructed a new immune-related 11-lncRNA signature that can stratify the prognoses of patients with ES.
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Affiliation(s)
- En-Hui Ren
- Breast Disease Diagnosis and Treatment Center, Affiliated Hospital of Qinghai University, Affiliated Cancer Hospital of Qinghai University, Xining, China.,Department of Orthopaedics, Lanzhou University Second Hospital, Lanzhou, China
| | - Ya-Jun Deng
- Department of Orthopaedics, Lanzhou University Second Hospital, Lanzhou, China
| | - Wen-Hua Yuan
- Department of Orthopaedics, Lanzhou University Second Hospital, Lanzhou, China
| | - Guang-Zhi Zhang
- Department of Orthopaedics, Lanzhou University Second Hospital, Lanzhou, China
| | - Zuo-Long Wu
- Department of Orthopaedics, Lanzhou University Second Hospital, Lanzhou, China
| | - Chun-Ying Li
- The Fourth People's Hospital of Qinghai Province, Xining, China
| | - Qi-Qi Xie
- Breast Disease Diagnosis and Treatment Center, Affiliated Hospital of Qinghai University, Affiliated Cancer Hospital of Qinghai University, Xining, China.,Department of Orthopaedics, Lanzhou University Second Hospital, Lanzhou, China
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Feng D, Wang J, Yang W, Li J, Lin X, Zha F, Wang X, Ma L, Choi NT, Mii Y, Takada S, Huen MSY, Guo Y, Zhang L, Gao B. Regulation of Wnt/PCP signaling through p97/VCP-KBTBD7-mediated Vangl ubiquitination and endoplasmic reticulum-associated degradation. SCIENCE ADVANCES 2021; 7:7/20/eabg2099. [PMID: 33990333 PMCID: PMC8121430 DOI: 10.1126/sciadv.abg2099] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 03/25/2021] [Indexed: 05/12/2023]
Abstract
The four-pass transmembrane proteins Vangl1 and Vangl2 are dedicated core components of Wnt/planar cell polarity (Wnt/PCP) signaling that critically regulate polarized cell behaviors in many morphological and physiological processes. Here, we found that the abundance of Vangl proteins is tightly controlled by the ubiquitin-proteasome system through endoplasmic reticulum-associated degradation (ERAD). The key ERAD component p97/VCP directly binds to Vangl at a highly conserved VCP-interacting motif and recruits the E3 ligase KBTBD7 via its UBA-UBX adaptors to promote Vangl ubiquitination and ERAD. We found that Wnt5a/CK1 prevents Vangl ubiquitination and ERAD by inducing Vangl phosphorylation, which facilitates Vangl export from the ER to the plasma membrane. We also provide in vivo evidence that KBTBD7 regulates convergent extension during zebrafish gastrulation and functions as a tumor suppressor in breast cancer by promoting Vangl degradation. Our findings reveal a previously unknown regulatory mechanism of Wnt/PCP signaling through the p97/VCP-KBTBD7-mediated ERAD pathway.
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Affiliation(s)
- Di Feng
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- The University of Hong Kong-Shenzhen Institute of Research and Innovation (HKU-SIRI), Shenzhen, China
| | - Jin Wang
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- The University of Hong Kong-Shenzhen Institute of Research and Innovation (HKU-SIRI), Shenzhen, China
| | - Wei Yang
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- The University of Hong Kong-Shenzhen Institute of Research and Innovation (HKU-SIRI), Shenzhen, China
| | - Jingyu Li
- Department of Biomedical Sciences, College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong SAR, China
| | - Xiaochen Lin
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Fangzi Zha
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Xiaolu Wang
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Luyao Ma
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Nga Ting Choi
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- The University of Hong Kong-Shenzhen Institute of Research and Innovation (HKU-SIRI), Shenzhen, China
| | - Yusuke Mii
- Exploratory Research Center on Life and Living Systems (ExCELLS) and National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Japan
- Japan Science and Technology Agency, PRESTO, Kawaguchi, Japan
| | - Shinji Takada
- Exploratory Research Center on Life and Living Systems (ExCELLS) and National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Japan
| | - Michael S Y Huen
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Yusong Guo
- Division of Life Science, Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Liang Zhang
- Department of Biomedical Sciences, College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong SAR, China
| | - Bo Gao
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
- The University of Hong Kong-Shenzhen Institute of Research and Innovation (HKU-SIRI), Shenzhen, China
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Wang G, Zhan T, Li F, Shen J, Gao X, Xu L, Li Y, Zhang J. The prediction of survival in Gastric Cancer based on a Robust 13-Gene Signature. J Cancer 2021; 12:3344-3353. [PMID: 33976744 PMCID: PMC8100809 DOI: 10.7150/jca.49658] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 03/27/2021] [Indexed: 12/13/2022] Open
Abstract
Gastric cancer represents a major public health problem. Owing to the great heterogeneity of GC, conventional clinical characteristics are limited in the accurate prediction of individual outcomes and survival. This study aimed to establish a robust gene signature to predict the prognosis of GC based on multiple datasets. Initially, we downloaded raw data from four independent datasets of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and performed univariate Cox proportional hazards regression analysis to identify prognostic genes associated with overall survival (OS) from each dataset. Thirteen common genes from four datasets were screened as candidate prognostic signatures. Then, a risk score model was developed based on this 13‑gene signature and validated by four independent datasets and the entire cohort. Patients with a high-risk score had poorer OS and recurrence-free survival (RFS). Multivariate regression and stratified analysis revealed that the 13-gene signature was not only an independent predictive factor but also associated with recurrence when adjusting for other clinical factors. Furthermore, in the high-risk group, gene set enrichment analysis (GSEA) showed that the mTOR signaling pathway and MAPK signaling pathway were significantly enriched. The present study provided a robust and reliable gene signature for prognostic prediction of both OS and RFS of patients with GC, which may be useful for delivering individualized management of patients.
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Affiliation(s)
- Guoguang Wang
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Tian Zhan
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Fan Li
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jian Shen
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiang Gao
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Lei Xu
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yuan Li
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center For Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Jianping Zhang
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Chen Q, Qiu B, Zeng X, Hu L, Huang D, Chen K, Qiu X. Identification of a tumor microenvironment-related gene signature to improve the prediction of cervical cancer prognosis. Cancer Cell Int 2021; 21:182. [PMID: 33766042 PMCID: PMC7992856 DOI: 10.1186/s12935-021-01867-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 03/06/2021] [Indexed: 12/24/2022] Open
Abstract
Background Previous studies have found that the microenvironment of cervical cancer (CESC) affects the progression and treatment of this disease. Thus, we constructed a multigene model to assess the survival of patients with cervical cancer. Methods We scored 307 CESC samples from The Cancer Genome Atlas (TCGA) and divided them into high and low matrix and immune scores using the ESTIMATE algorithm for differential gene analysis. Cervical cancer patients were randomly divided into a training group, testing group and combined group. The multigene signature prognostic model was constructed by Cox analyses. Multivariate Cox analysis was applied to evaluate the significance of the multigene signature for cervical cancer prognosis. Prognosis was assessed by Kaplan–Meier curves comparing the different groups, and the accuracy of the prognostic model was analyzed by receiver operating characteristic-area under the curve (ROC-AUC) analysis and calibration curve. The Tumor Immune Estimation Resource (TIMER) database was used to analyze the relationship between the multigene signature and immune cell infiltration. Results We obtained 420 differentially expressed genes in the tumor microenvironment from 307 patients with cervical cancer. A three-gene signature (SLAMF1, CD27, SELL) model related to the tumor microenvironment was constructed to assess patient survival. Kaplan–Meier analysis showed that patients with high risk scores had a poor prognosis. The ROC-AUC value indicated that the model was an accurate predictor of cervical cancer prognosis. Multivariate cox analysis showed the three-gene signature to be an independent risk factor for the prognosis of cervical cancer. A nomogram combining the three-gene signature and clinical features was constructed, and calibration plots showed that the nomogram resulted in an accurate prognosis for patients. The three-gene signature was associated with T stage, M stage and degree of immune infiltration in patients with cervical cancer. Conclusions This research suggests that the developed three-gene signature may be applied as a biomarker to predict the prognosis of and personalized therapy for CESC.
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Affiliation(s)
- Qian Chen
- Department of Research, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, 530021, China.,Department of Epidemiology, School of Public Health, Guangxi Medical University, Nanning, Guangxi, 530021, China
| | - Bingqing Qiu
- Department of Nuclear Medicine, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, 530021, China
| | - Xiaoyun Zeng
- Department of Epidemiology, School of Public Health, Guangxi Medical University, Nanning, Guangxi, 530021, China
| | - Lang Hu
- Guangxi Medical University Cancer Hospital, Nanning, Guangxi, 530021, China
| | - Dongping Huang
- Department of Nutrition, School of Public Health, Guangxi Medical University, Nanning, Guangxi, 530021, China
| | - Kaihua Chen
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, 530021, China
| | - Xiaoqiang Qiu
- Department of Epidemiology, School of Public Health, Guangxi Medical University, Nanning, Guangxi, 530021, China.
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Wang X, Xiao Z, Gong J, Liu Z, Zhang M, Zhang Z. A prognostic nomogram for lung adenocarcinoma based on immune-infiltrating Treg-related genes: from bench to bedside. Transl Lung Cancer Res 2021; 10:167-182. [PMID: 33569302 PMCID: PMC7867791 DOI: 10.21037/tlcr-20-822] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background Accumulating evidence suggests that lymphocyte infiltration in the tumor microenvironment is positively correlated with tumorigenesis and development, while the role of Tregs (regulatory T cells) has been controversial. Therefore, we attempted to discover the possible value of Tregs for lung adenocarcinoma (LUAD). Methods The gene-sequencing data of LUAD were applied from three Gene Expression Omnibus (GEO) datasets—GSE10072, GSE32863 and GSE43458; the corresponding fractions of tumor-infiltrating immune cells were extracted from the CIBERSORTx portal. Weighted gene coexpression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis were conducted to identify the significant module and candidate genes related to Tregs. The role of candidate genes in LUAD was further verified using data from The Cancer Genome Atlas (TCGA) database. Finally, we constructed a nomogram model to predict the prognosis of LUAD by plotting Kaplan-Meier (K-M), receiver operating characteristic (ROC) and calibration curves, which elucidated the performance of the nomogram. Results In total, 10,047 genes in 333 samples (196 tumor and 137 normal samples) from the GEO database were included. By WGCNA and PPI analysis, we identified a significant black module and 36 candidate genes related to Treg. Next, the candidate genes were verified using TCGA data by Cox regression analysis to screen 13 hub genes that stratified LUAD patients into low- or high-risk groups. Low-risk patients showed a significantly longer overall survival (OS) than high-risk patients (3-year OS: 70.2% vs. 35.2%; 5-year OS: 36.6% vs. 0; P=1.651E-09), and the areas under the ROC curves (AUCs) showed good (3-year AUC: 0.733; 5-year AUC: 0.777). Next, we constructed a survival nomogram combining the hub genes and clinical parameters; the low-risk patients still showed a favorable prognosis compared with that of the high-risk patients (P=7.073E-13), and the AUCs were better (3-year AUC: 0.763; 5-year AUC: 0.873). Conclusions We revealed the role of immune-infiltrating Treg-related genes in LUAD and constructed a prognostic nomogram, which may help clinicians make optimal therapeutic decisions and help patients obtain better outcomes.
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Affiliation(s)
- Xiaofei Wang
- Department of Lung Cancer Surgery, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Zengtuan Xiao
- Department of Lung Cancer Surgery, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Jialin Gong
- Department of Lung Cancer Surgery, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Zuo Liu
- Department of Lung Cancer Surgery, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Mengzhe Zhang
- Department of Lung Cancer Surgery, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Zhenfa Zhang
- Department of Lung Cancer Surgery, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
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Liu M, Tong L, Liang B, Song X, Xie L, Peng H, Huang D. A 15-Gene Signature and Prognostic Nomogram for Predicting Overall Survival in Non-Distant Metastatic Oral Tongue Squamous Cell Carcinoma. Front Oncol 2021; 11:587548. [PMID: 33767977 PMCID: PMC7985252 DOI: 10.3389/fonc.2021.587548] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Accepted: 01/28/2021] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Oral tongue squamous cell carcinoma (OTSCC) is a devastating tumor with poor prognosis. There is an urgent need for reliable biomarkers to help predict prognosis and guide treatment for OTSCC. In the current study, we aimed to develop a robust multi-gene signature and prognostic nomogram to predict the prognosis of patients with non-distant metastatic OTSCC. METHODS OTSCC-related differentially-expressed genes were screened from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression based on 1,000 bootstrap replicates, LASSO regression and stepwise multivariate Cox regression were utilized to develop a novel multi-mRNA signature for predicting overall survival in OTSCC. The concordance index, area under receiver operating characteristic (ROC AUC) and calibration curve were employed to assess the prediction capacity of the novel multi-gene model. In addition, a prognostic nomogram was constructed to facilitate the clinical use of the fitted model. The Kaplan-Meier with log-rank test was employed to assess differences in overall survival. RESULTS We successfully established a novel 15-mRNA prognostic model for predicting overall survival of non-distant metastatic OTSCC, involving ADTRP, ITGA3, RFC4, CCDC96, CYP2J2, NELL2, SPHK1, SPAG16, HBEGF, S100A9, EGFL6, ADGRG6, PDE4D, ABCA4, and CTTN. The prediction ability of this 15-gene signature was independent of other clinicopathological factors, with an HR of 11.5 (95% CI: 4.70-28.3). Moreover, internal validation by bootstrap analysis yielded a C-index of 0.849, with a 3-year AUC of 0.907 and 5-year AUC of 0.944, which implied excellent prediction accuracy of the fitted model. In addition, external validation by using the GEO dataset (GSE41116) yielded a C-index of 0.804, with a 3-year AUC of 0.868 and 5-year AUC of 0.855, which also indicated good prediction ability of the 15-gene model. Finally, a prognostic nomogram integrating risk group, grade, T stage and N stage was established. CONCLUSION Our results demonstrate our 15-gene signature was independently associated with overall survival in non-distant metastatic OTSCC. Moreover, the prognostic nomogram integrating the 15-gene signature and clinicopathological factors has potential to be developed as a prognostic tool.
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Affiliation(s)
- Muyuan Liu
- Department of Head and Neck, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Litian Tong
- Department of Anesthesiology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Bin Liang
- Department of Cell Biology and Genetics, Key Laboratory of Molecular Biology in High Cancer Incidence Coastal Chaoshan Area of Guangdong Higher Education Institutes, Shantou University Medical College, Shantou, China
| | - Xuhong Song
- Department of Cell Biology and Genetics, Key Laboratory of Molecular Biology in High Cancer Incidence Coastal Chaoshan Area of Guangdong Higher Education Institutes, Shantou University Medical College, Shantou, China
| | - Lingzhu Xie
- Department of Cell Biology and Genetics, Key Laboratory of Molecular Biology in High Cancer Incidence Coastal Chaoshan Area of Guangdong Higher Education Institutes, Shantou University Medical College, Shantou, China
| | - Hanwei Peng
- Department of Head and Neck, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Dongyang Huang
- Department of Cell Biology and Genetics, Key Laboratory of Molecular Biology in High Cancer Incidence Coastal Chaoshan Area of Guangdong Higher Education Institutes, Shantou University Medical College, Shantou, China
- Research Center of Translational Medicine, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
- *Correspondence: Dongyang Huang,
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Wu C, Rao X, Lin W. Immune landscape and a promising immune prognostic model associated with TP53 in early-stage lung adenocarcinoma. Cancer Med 2020; 10:806-823. [PMID: 33314730 PMCID: PMC7897963 DOI: 10.1002/cam4.3655] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 11/01/2020] [Accepted: 11/26/2020] [Indexed: 02/06/2023] Open
Abstract
Purpose TP53 mutation, one of the most frequent mutations in early‐stage lung adenocarcinoma (LUAD), triggers a series of alterations in the immune landscape, progression, and clinical outcome of early‐stage LUAD. Our study was designed to unravel the effects of TP53 mutation on the immunophenotype of early‐stage LUAD and formulate a TP53‐associated immune prognostic model (IPM) that can estimate prognosis in early‐stage LUAD patients. Materials and methods Immune‐associated differentially expressed genes (DEGs) between TP53 mutated (TP53MUT) and TP53 wild‐type (TP53WT) early‐stage LUAD were comprehensively analyzed. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) analysis identified the prognostic immune‐associated DEGs. We constructed and validated an IPM based on the TCGA and a meta‐GEO composed of GSE72094, GSE42127, and GSE31210, respectively. The CIBERSORT algorithm was analyzed for assessing the percentage of immune cell types. A nomogram model was established for clinical application. Results TP53 mutation occurred in approximately 50.00% of LUAD patients, stimulating a weakened immune response in early‐stage LUAD. Sixty‐seven immune‐associated DEGs were determined between TP53WT and TP53MUT cohort. An IPM consisting of two prognostic immune‐associated DEGs (risk score = 0.098 * ENTPD2 expression + 0.168 * MIF expression) was developed through 397 cases in the TCGA and further validated based on 623 patients in a meta‐GEO. The IPM stratified patients into low or high risk of undesirable survival and was identified as an independent prognostic indicator in multivariate analysis (HR = 2.09, 95% CI: 1.43–3.06, p < 0.001). Increased expressions of PD‐L1, CTLA‐4, and TIGIT were revealed in the high‐risk group. Prognostic nomogram incorporating the IPM and other clinicopathological parameters (TNM stage and age) achieved optimal predictive accuracy and clinical utility. Conclusion The IPM based on TP53 status is a reliable and robust immune signature to identify early‐stage LUAD patients with high risk of unfavorable survival.
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Affiliation(s)
- Chengde Wu
- Department of Thoracic Surgery, Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, Hainan, China
| | - Xiang Rao
- Department of Pathology, Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, Hainan, China
| | - Wei Lin
- Department of Thoracic Surgery, Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, Hainan, China
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Identification of 6 Hub Proteins and Protein Risk Signature of Colorectal Cancer. BIOMED RESEARCH INTERNATIONAL 2020; 2020:6135060. [PMID: 33376727 PMCID: PMC7744197 DOI: 10.1155/2020/6135060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 11/15/2020] [Accepted: 11/18/2020] [Indexed: 12/30/2022]
Abstract
Background Colorectal cancer (CRC) is the second most common cause of cancer death in the United States and the third most common cancer globally. The incidence of CRC tends to be younger, and we urgently need a reliable prognostic assessment strategy. Methods Protein expression profile and clinical information of 390 CRC patients/samples were downloaded from the TCPA and TCGA database, respectively. The Kaplan-Meier, Cox regression, and Pearson correlation analysis were applied in this study. Results Based on the TCPA and TCGA database, we screened 6 hub proteins and first constructed protein risk signature, all of which were significantly associated with CRC patients' overall survival (OS). The risk score was an independent prognostic factor and significantly related with the size of the tumor in situ (T). 6 hub proteins were differentially expressed in cancer and normal tissues and in different CRC stages, which were validated at the ONCOMINE database. Next, 40 coexpressed proteins of 6 hub proteins were extracted from the TCPA database. In the protein-protein interaction (PPI) network, HER1, HER2, and CTNNB1 were at the center. Function enrichment analysis illustrated that 46 proteins were mainly involved in the EGFR (HER1) tyrosine kinase inhibitor resistance pathway. Conclusion Studies indicated that 6 hub proteins might be considered as new targets for CRC therapies, and the protein risk signature can be used to predict the OS of CRC patients.
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Cai Q, He B, Zhang P, Zhao Z, Peng X, Zhang Y, Xie H, Wang X. Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods. J Transl Med 2020; 18:463. [PMID: 33287830 PMCID: PMC7720605 DOI: 10.1186/s12967-020-02635-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 11/27/2020] [Indexed: 12/25/2022] Open
Abstract
Background Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms. Method RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs). Results Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient = − 1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them. Conclusion In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.
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Affiliation(s)
- Qidong Cai
- Department of Thoracic Surgery, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China.,Hunan Key Laboratory of Early Diagnosis and Precision Therapy, Department of Thoracic Surgery, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Boxue He
- Department of Thoracic Surgery, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China.,Hunan Key Laboratory of Early Diagnosis and Precision Therapy, Department of Thoracic Surgery, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Pengfei Zhang
- Department of Thoracic Surgery, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China.,Hunan Key Laboratory of Early Diagnosis and Precision Therapy, Department of Thoracic Surgery, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Zhenyu Zhao
- Department of Thoracic Surgery, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China.,Hunan Key Laboratory of Early Diagnosis and Precision Therapy, Department of Thoracic Surgery, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Xiong Peng
- Department of Thoracic Surgery, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China.,Hunan Key Laboratory of Early Diagnosis and Precision Therapy, Department of Thoracic Surgery, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Yuqian Zhang
- Department of Thoracic Surgery, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China.,Hunan Key Laboratory of Early Diagnosis and Precision Therapy, Department of Thoracic Surgery, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Hui Xie
- Department of Thoracic Surgery, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China.,Hunan Key Laboratory of Early Diagnosis and Precision Therapy, Department of Thoracic Surgery, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Xiang Wang
- Department of Thoracic Surgery, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China. .,Hunan Key Laboratory of Early Diagnosis and Precision Therapy, Department of Thoracic Surgery, The Second Xiangya Hospital, Central South University, Changsha, 410011, China.
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Tian WJ, Liu SS, Li BR. The Combined Detection of Immune Genes for Predicting the Prognosis of Patients With Non-Small Cell Lung Cancer. Technol Cancer Res Treat 2020; 19:1533033820977504. [PMID: 33256552 PMCID: PMC7711225 DOI: 10.1177/1533033820977504] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Lung cancer is one of the leading causes of cancer-related death. In recent years, there has been an increasing interest in the fields of tumor and immunity. This study focused on the possible prognostic value of immune genes in non-small cell lung cancer patients. We used The Cancer Genome Atlas (TCGA) to download gene expression data and clinical information of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). The immune gene list was downloaded from the Immport database. We then constructed immune gene prognostic models on the basis of Cox regression analysis. We further evaluated the clinical significance of the models via survival analysis, receiver operating characteristic (ROC) curves, and independent prognostic factor analysis. Moreover, we analyzed the associations of prognostic models with both mutation burdens and neoantigens. Using the Gene Expression Omnibus (GEO) and Kaplan-Meier plotter databases, we evaluated the validity of the prognostic models. The prognostic model of LUAD included 13 immune genes, and the prognostic model of LUSC contained 10 immune genes. High-risk patients based on prognostic models had a lower 5-year survival rate than did low-risk patients. The ROC curve analysis demonstrated the prediction accuracy of the prognostic models, as the area under the curve (AUC) was 0.742, 0.707, and 0.711 for LUAD, and 0.668, 0.703, and 0.668 for LUSC, when the predicted survival times were 1, 3, and 5 years, respectively. The mutation burden analysis showed that mutation level was associated with the risk score in patients with LUAD. The analysis based on GEO and Kaplan-Meier plotter demonstrated the prognostic validity of the models. Therefore, immune gene-related models of LUAD and LUSC can predict prognosis. Further study of these genes may enable us to better distinguish between LUAD and LUSC and lead to improvement in immunotherapy for lung cancer.
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Affiliation(s)
- Wen-Juan Tian
- Department of Clinical Laboratory, Second Affiliated Hospital, 117799Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China.,School of Medicine, 117799Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China
| | - Shan-Shan Liu
- Department of Clinical Laboratory, Second Affiliated Hospital, 117799Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China.,School of Medicine, 117799Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China
| | - Bu-Rong Li
- Department of Clinical Laboratory, Second Affiliated Hospital, 117799Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China
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