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Yang W, Duan L, Zhao X, Niu L, Wang C, Fan D, Hong L. Integration of machine learning in biomarker discovery for esophageal squamous cell carcinoma: Applications and future directions. Pathol Res Pract 2025; 272:156083. [PMID: 40516138 DOI: 10.1016/j.prp.2025.156083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2025] [Revised: 06/12/2025] [Accepted: 06/12/2025] [Indexed: 06/16/2025]
Abstract
PURPOSE Recent advancements in sequencing technologies and bioinformatics algorithms have facilitated significant breakthroughs in both fundamental and clinical tumor research. Nevertheless, the processing and utilization of large-scale data continue to pose substantial challenges. Machine learning (ML)-based integrative analysis methods present a novel approach for navigating these complex datasets, thereby enhancing the understanding of tumors from multiple perspectives. METHODS Here, we present a comprehensive overview of ML processes and methodologies that have the potential to advance research and management of esophageal squamous cell carcinoma (ESCC). Specifically, our focus is on their application in key areas such as early detection, prognosis prediction, therapeutic target identification, and drug discovery. Additionally, we examine the challenges and opportunities that ML introduces in the context of ESCC research. RESULTS Our findings indicate that ML techniques have the capacity to enhance medical decision-making, improve patient care, and drive progress in healthcare. The prospective integration of ML in oncology poses several challenges, highlighting the need for interdisciplinary collaboration. Addressing these challenges will require coordinated efforts from medical professionals, data scientists, information technology specialists, and policymakers. CONCLUSIONS The identification of biomarkers for ESCC via ML significantly enhances the quality of medical care and supports expert diagnostic and therapeutic decision-making, thereby markedly improving diagnostic efficiency and advancing the field of intelligent healthcare.
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Affiliation(s)
- Wanli Yang
- Department of Digestive Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China; State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi' an, China
| | - Lili Duan
- Department of Digestive Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China; State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi' an, China
| | - Xinhui Zhao
- Department of Thyroid and Breast Surgery, Xi'an No.3 Hospital, The Affiliated Hospital of Northwest University, Xi'an, Shaanxi Province, China
| | - Liaoran Niu
- Department of Digestive Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China; State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi' an, China
| | - Chenyang Wang
- Department of Digestive Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China; State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi' an, China
| | - Daiming Fan
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi' an, China
| | - Liu Hong
- Department of Digestive Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China; State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi' an, China.
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Lin RY, Chen RP, Lin FQ. Crotonylation-Related Prognostic Model of Esophageal Squamous Cell Carcinoma Based on Transcriptome Analysis and Single-Cell Sequencing Analysis. Int J Gen Med 2025; 18:415-436. [PMID: 39895826 PMCID: PMC11784406 DOI: 10.2147/ijgm.s493800] [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: 10/04/2024] [Accepted: 01/21/2025] [Indexed: 02/04/2025] Open
Abstract
Background Crotonylation is an emerging lysine acylation modification implicated in various diseases, yet its role in esophageal squamous cell cancer (ESCC) is unexplored. This study aimed to investigate the role of crotonylation-related genes (CRGs) in ESCC using bioinformatics approaches. Methods We included three ESCC datasets and 24 CRGs. Differentially expressed genes (DEGs) from TCGA-ESCA were intersected with key module genes related to CRGs to identify candidate genes. Univariate and LASSO regression analyses were conducted to select prognostic genes, which were then used to construct risk models. Independent prognostic analysis and nomogram construction followed. Functional enrichment and immune infiltration analyses were performed using the prognostic genes. Single-cell analysis was conducted to assess cell communication and pseudotemporal dynamics in key cells. Results Intersection of 1529 DEGs with 1,048 key module genes yielded 55 candidate genes. OSM, FABP3, MICB, and FAM189A2 were identified as prognostic genes. These genes were used to classify ESCA patients into different risk groups and construct a nomogram. FABP3 and FAM189A2 were enriched in neuroactive ligand-receptor interaction and ribosome terms. MICB and FABP3 showed strong positive correlations with natural killer T (NKT) cells, while FAM189A2 negatively correlated with gamma delta T (γδT) cells. Single-cell analysis identified mast cells and neutrophils as key cells, differentiating into seven and three states, respectively. Conclusion Four genes (OSM, FABP3, MICB, and FAM189A2) were identified as prognostic crotonylation-related genes in ESCC, potentially involved in its pathogenesis. OSM was negatively correlated with ESCC, while FABP3 and MICB were positively correlated.
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Affiliation(s)
- Ruo-Yang Lin
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China
| | - Ren-Pin Chen
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China
| | - Fu-Qiang Lin
- Department of Thoracic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China
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Zhang WY, Chang YJ, Shi RH. Artificial intelligence enhances the management of esophageal squamous cell carcinoma in the precision oncology era. World J Gastroenterol 2024; 30:4267-4280. [PMID: 39492825 PMCID: PMC11525855 DOI: 10.3748/wjg.v30.i39.4267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Revised: 08/31/2024] [Accepted: 09/19/2024] [Indexed: 10/12/2024] Open
Abstract
Esophageal squamous cell carcinoma (ESCC) is the most common histological type of esophageal cancer with a poor prognosis. Early diagnosis and prognosis assessment are crucial for improving the survival rate of ESCC patients. With the advancement of artificial intelligence (AI) technology and the proliferation of medical digital information, AI has demonstrated promising sensitivity and accuracy in assisting precise detection, treatment decision-making, and prognosis assessment of ESCC. It has become a unique opportunity to enhance comprehensive clinical management of ESCC in the era of precision oncology. This review examines how AI is applied to the diagnosis, treatment, and prognosis assessment of ESCC in the era of precision oncology, and analyzes the challenges and potential opportunities that AI faces in clinical translation. Through insights into future prospects, it is hoped that this review will contribute to the real-world application of AI in future clinical settings, ultimately alleviating the disease burden caused by ESCC.
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Affiliation(s)
- Wan-Yue Zhang
- School of Medicine, Southeast University, Nanjing 221000, Jiangsu Province, China
| | - Yong-Jian Chang
- School of Cyber Science and Engineering, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Rui-Hua Shi
- Department of Gastroenterology, Zhongda Hospital, Southeast University, Nanjing 210009, Jiangsu Province, China
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Chen W, Kang Y, Sheng W, Huang Q, Cheng J, Pei S, Meng Y. A new 4-gene-based prognostic model accurately predicts breast cancer prognosis and immunotherapy response by integrating WGCNA and bioinformatics analysis. Front Immunol 2024; 15:1331841. [PMID: 38370403 PMCID: PMC10869553 DOI: 10.3389/fimmu.2024.1331841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/17/2024] [Indexed: 02/20/2024] Open
Abstract
Background Breast cancer (BRCA) is a common malignancy in women, and its resistance to immunotherapy is a major challenge. Abnormal expression of genes is important in the occurrence and development of BRCA and may also affect the prognosis of patients. Although many BRCA prognosis model scores have been developed, they are only applicable to a limited number of disease subtypes. Our goal is to develop a new prognostic score that is more accurate and applicable to a wider range of BRCA patients. Methods BRCA patient data from The Cancer Genome Atlas database was used to identify breast cancer-related genes (BRGs). Differential expression analysis of BRGs was performed using the 'limma' package in R. Prognostic BRGs were identified using co-expression and univariate Cox analysis. A predictive model of four BRGs was established using Cox regression and the LASSO algorithm. Model performance was evaluated using K-M survival and receiver operating characteristic curve analysis. The predictive ability of the signature in immune microenvironment and immunotherapy was investigated. In vitro experiments validated POLQ function. Results Our study identified a four-BRG prognostic signature that outperformed conventional clinicopathological characteristics in predicting survival outcomes in BRCA patients. The signature effectively stratified BRCA patients into high- and low-risk groups and showed potential in predicting the response to immunotherapy. Notably, significant differences were observed in immune cell abundance between the two groups. In vitro experiments demonstrated that POLQ knockdown significantly reduced the viability, proliferation, and invasion capacity of MDA-MB-231 or HCC1806 cells. Conclusion Our 4-BRG signature has the potential as an independent biomarker for predicting prognosis and treatment response in BRCA patients, complementing existing clinicopathological characteristics.
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Affiliation(s)
- Wenlong Chen
- Department of Thyroid and Breast Surgery, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China
| | - Yakun Kang
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Wenyi Sheng
- Department of Thyroid and Breast Surgery, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China
| | - Qiyan Huang
- Department of Thyroid and Breast Surgery, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China
| | - Jiale Cheng
- Department of Thyroid and Breast Surgery, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China
| | - Shengbin Pei
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - You Meng
- Department of Thyroid and Breast Surgery, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China
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Zhao T, Zhang Z, Li Y, Sun Z, Liu L, Deng X, Guo J, Zhu D, Cao S, Chai Y, Nikolaevna UV, Maratbek S, Wang Z, Zhang H. Brucella abortus modulates macrophage polarization and inflammatory response by targeting glutaminases through the NF-κB signaling pathway. Front Immunol 2023; 14:1180837. [PMID: 37325614 PMCID: PMC10266586 DOI: 10.3389/fimmu.2023.1180837] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 05/05/2023] [Indexed: 06/17/2023] Open
Abstract
Objectives The mechanism of Brucella infection regulating macrophage phenotype has not been completely elucidated until now. This study aimed to determine the mechanism of Brucella abortus in the modulation of macrophage phenotype using RAW264.7 cells as a model. Materials and methods RT-qPCR, ELISA and flow cytometry were used to detect the inflammatory factor production and phenotype conversion associated with M1/M2 polarization of macrophages by Brucella abortus infection. Western blot and immunofluorescence were used to analyze the role of nuclear factor kappa B (NF-κB) signaling pathway in regulation of Brucella abortus-induced macrophage polarization. Chromatin immunoprecipitation sequencing (Chip-seq), bioinformatics analysis and luciferase reporter assay were used to screen and validate NF-κB target genes associated with macrophage polarization and further verify its function. Results The results demonstrate that B. abortus induces a macrophage phenotypic switch and inflammatory response in a time-dependent manner. With the increase of infection time, B. abortus infection-induced M1-type increased first, peaked at 12 h, and then decreased, whereas the M2-type decreased first, trough at 12 h, and then increased. The trend of intracellular survival of B. abortus was consistent with that of M2 type. When NF-κB was inhibited, M1-type polarization was inhibited and M2-type was promoted, and the intracellular survival of B. abortus increased significantly. Chip-seq and luciferase reporter assay results showed that NF-κB binds to the glutaminase gene (Gls). Gls expression was down-regulated when NF-κB was inhibited. Furthermore, when Gls was inhibited, M1-type polarization was inhibited and M2-type was promoted, the intracellular survival of B. abortus increased significantly. Our data further suggest that NF-κB and its key target gene Gls play an important role in controlling macrophage phenotypic transformation. Conclusions Taken together, our study demonstrates that B. abortus infection can induce dynamic transformation of M1/M2 phenotype in macrophages. Highlighting NF-κB as a central pathway that regulates M1/M2 phenotypic transition. This is the first to elucidate the molecular mechanism of B. abortus regulation of macrophage phenotype switch and inflammatory response by regulating the key gene Gls, which is regulated by the transcription factor NF-κB.
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Affiliation(s)
- Tianyi Zhao
- State International Joint Research Center for Animal Health Breeding, College of Animal Science and Technology, Shihezi University, Shihezi, China
| | - Zedan Zhang
- State International Joint Research Center for Animal Health Breeding, College of Animal Science and Technology, Shihezi University, Shihezi, China
| | - Yitao Li
- State International Joint Research Center for Animal Health Breeding, College of Animal Science and Technology, Shihezi University, Shihezi, China
| | - Zhihua Sun
- State International Joint Research Center for Animal Health Breeding, College of Animal Science and Technology, Shihezi University, Shihezi, China
| | - Liangbo Liu
- State International Joint Research Center for Animal Health Breeding, College of Animal Science and Technology, Shihezi University, Shihezi, China
| | - Xingmei Deng
- State International Joint Research Center for Animal Health Breeding, College of Animal Science and Technology, Shihezi University, Shihezi, China
| | - Jia Guo
- State International Joint Research Center for Animal Health Breeding, College of Animal Science and Technology, Shihezi University, Shihezi, China
| | - Dexin Zhu
- State International Joint Research Center for Animal Health Breeding, College of Animal Science and Technology, Shihezi University, Shihezi, China
| | - Shuzhu Cao
- State International Joint Research Center for Animal Health Breeding, College of Animal Science and Technology, Shihezi University, Shihezi, China
| | - Yingjin Chai
- State International Joint Research Center for Animal Health Breeding, College of Animal Science and Technology, Shihezi University, Shihezi, China
| | - Usevich Vera Nikolaevna
- State International Joint Research Center for Animal Health Breeding, College of Animal Science and Technology, Shihezi University, Shihezi, China
- College of Veterinary, Ural State Agricultural University, Yekaterinburg, Russia
| | - Suleimenov Maratbek
- State International Joint Research Center for Animal Health Breeding, College of Animal Science and Technology, Shihezi University, Shihezi, China
- College of Veterinary, National Agricultural University of Kazakhstan, Nur Sultan, Kazakhstan
| | - Zhen Wang
- State International Joint Research Center for Animal Health Breeding, College of Animal Science and Technology, Shihezi University, Shihezi, China
| | - Hui Zhang
- State International Joint Research Center for Animal Health Breeding, College of Animal Science and Technology, Shihezi University, Shihezi, China
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Zhao W, Jing X, Wang T, Zhang F. Glutamine Deprivation Synergizes the Anticancer Effects of Cold Atmospheric Plasma on Esophageal Cancer Cells. Molecules 2023; 28:molecules28031461. [PMID: 36771124 PMCID: PMC9919221 DOI: 10.3390/molecules28031461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 01/29/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
Esophageal cancer is a highly aggressive malignancy with a low response to standard anti-cancer therapies. There is an unmet need to develop new therapeutic strategies to improve the clinical outcomes of current treatments. Cold atmospheric plasma (CAP) is a promising approach for cancer treatment, and has displayed anticancer efficacy in multiple preclinical models. Recent studies have shown that the efficacy of CAP is positively correlated with intracellular reactive oxygen species (ROS) levels. This suggests that aggressively increasing intracellular ROS levels has the potential to further improve CAP-mediated anticancer efficacy. Glutamine plays an important role in cellular ROS scavenging after being converted to glutathione (GSH, a well-described antioxidant) under physiological conditions, so reducing intracellular glutamine levels seems to be a promising strategy. To test this hypothesis, we treated esophageal cancer cells with CAP while controlling the supply of glutamine. The results showed that glutamine did affect the anticancer effect of CAP, and the combination of CAP stimulation and glutamine deprivation significantly inhibited the proliferation of esophageal cancer cells compared to the control group (p < 0.05). Furthermore, flow cytometric analysis documented a significant increase in more than 10% in apoptosis and necrosis of esophageal cancer cells after this synergistic treatment compared to the control group (p < 0.05). Thus, these results provide the first direct evidence that the biological function of CAP can be modulated by glutamine levels and that combined CAP stimulation and glutamine deprivation represent a promising strategy for the future treatment of esophageal cancer.
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Affiliation(s)
- Wei Zhao
- Henan Key Laboratory of Ion-Beam Bioengineering, School of Physics and Microelectronics, Zhengzhou University, Zhengzhou 450001, China
| | - Xumiao Jing
- Henan Key Laboratory of Ion-Beam Bioengineering, School of Physics and Microelectronics, Zhengzhou University, Zhengzhou 450001, China
| | - Tao Wang
- College of Nursing and Health, Zhengzhou University, Zhengzhou 450001, China
- Telethon Kids Institute, Perth, WA 6872, Australia
- School of Medicine, University of Western Australia, Perth, WA 6872, Australia
- Correspondence: (T.W.); (F.Z.)
| | - Fengqiu Zhang
- Henan Key Laboratory of Ion-Beam Bioengineering, School of Physics and Microelectronics, Zhengzhou University, Zhengzhou 450001, China
- Correspondence: (T.W.); (F.Z.)
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Peng J, Lu Y, Chen L, Qiu K, Chen F, Liu J, Xu W, Zhang W, Zhao Y, Yu Z, Ren J. The prognostic value of machine learning techniques versus cox regression model for head and neck cancer. Methods 2022; 205:123-132. [PMID: 35798257 DOI: 10.1016/j.ymeth.2022.07.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 05/18/2022] [Accepted: 07/01/2022] [Indexed: 10/17/2022] Open
Abstract
BACKGROUND Accurate prognostic prediction for head and neck cancer (HNC) is important for the improvement of clinical management. We aimed to compare the prognostic value of various machine learning techniques (MLTs) and statistical Cox regression model for different types of HNC. METHODS Clinical data of HNC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 1974 to 2016. The prediction performance of five ML models, including random forest (RF), gradient boosting decision tree (GBDT), support vector machine (SVM), neural network (NN) and deep learning (DL), were compared with the statistical Cox regression model by estimating the concordance index (C-index), integrated Brier score (IBS), time-dependent receiver operating characteristic (ROC) curve and the area under the curve (AUC). RESULTS Our results showed that the RF model outperformed all other models in prognostic prediction for all tumor sites of HNC, particularly for major salivary gland cancer (MSGC, C-index: 88.730 ± 0.8700, IBS: 7.680 ± 0.4800), oral cavity cancer (OCC, C-index: 84.250 ± 0.6700, IBS: 11.480 ± 0.3300) and oropharyngeal cancer (OPC, C-index: 82.510 ± 0.5400, IBS: 10.120 ± 0.1400). Meanwhile, we analyzed the importance of each clinical variable in the RF model, in which age and tumor size presented the strongest positive prognostic effects. Additionally, similar results can be observed in the internal (6th edition of the AJCC TNM staging system cohort) and external validations (the TCGA HNC cohort). CONCLUSIONS The RF model is a promising prognostic prediction tool for HNC patients, regardless of the anatomic subsites.
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Affiliation(s)
- Jiajia Peng
- Department of Oto-Rhino-Laryngology, West China Hospital, Sichuan University, Chengdu, China; West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yongmei Lu
- Department of Computer Science, Sichuan University, Chengdu, China
| | - Li Chen
- Department of Computer Science, Sichuan University, Chengdu, China
| | - Ke Qiu
- Department of Oto-Rhino-Laryngology, West China Hospital, Sichuan University, Chengdu, China
| | - Fei Chen
- Department of Oto-Rhino-Laryngology, West China Hospital, Sichuan University, Chengdu, China
| | - Jun Liu
- Department of Oto-Rhino-Laryngology, West China Hospital, Sichuan University, Chengdu, China
| | - Wei Xu
- Department of Computer Science, Sichuan University, Chengdu, China
| | - Wei Zhang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yu Zhao
- Department of Oto-Rhino-Laryngology, West China Hospital, Sichuan University, Chengdu, China; West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
| | - Zhonghua Yu
- Department of Computer Science, Sichuan University, Chengdu, China.
| | - Jianjun Ren
- Department of Oto-Rhino-Laryngology, West China Hospital, Sichuan University, Chengdu, China; West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China; Department of Biostatistics, Princess Margaret Cancer Centre and Dalla Lana School of Public Health, Toronto, Ontario, Canada.
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Identification of FABP7 as a Potential Biomarker for Predicting Prognosis and Antiangiogenic Drug Efficacy of Glioma. DISEASE MARKERS 2022; 2022:2091791. [PMID: 35783014 PMCID: PMC9249527 DOI: 10.1155/2022/2091791] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 05/18/2022] [Accepted: 06/13/2022] [Indexed: 11/17/2022]
Abstract
Objective Glioma is a common malignant tumor of the central nervous system with extremely poor prognosis. An efficient molecular marker for diagnosis and treatment is urgently needed. Fatty acid binding protein 7(FABP7), which regulates intracellular lipid metabolism, is highly expressed in nervous system tumors, but its prognostic value remains undetermined. The present study investigated the relationship between FABP7 expression and prognosis in glioma patients by bioinformatics analysis, as well as immunohistochemically evaluating the effect of FABP7 expression on the efficacy of antiangiogenic drugs. Methods Gene expression and clinical data on patients with glioma were collected from the China Glioma Genome Atlas (CGGA) database, The Cancer Genome Atlas (TCGA), and the Gene Expression Omnibus (GEO) databases. Levels of FABP7 expression and their association with the clinicopathologic characteristics of glioma patients were analyzed in the CGGA database. The relationships between FABP7 expression and clinical findings, such as survival and prognostic, were determined and used for nomogram construction. Mechanisms of action of FABP7 were assessed using GSEA software. FABP7 expression in the tissues of glioma patients treated with apatinib was evaluated immunohistochemically. Results FABP7 was highly expressed in glioma samples, with higher FABP7 expression associated with poorer patient prognosis and more advanced clinicopathological features. Bioinformatics analysis, including survival, receiver operating characteristic curve, and univariate and multivariate Cox analyses, showed that FABP7 was independently prognostic of outcomes in glioma patients. GSEA analysis showed that FABP7 was associated with angiogenesis, with FABP7 having correlation coefficients > 0.4 with seven factors in the angiogenic pathway, POSTN, TIMP1, PDGFA, FGFR1, S100A4, COL5A2, and STC1, and the expression of these factors related to patient prognosis. Immunohistochemistry showed that FABP7 expression was higher in glioma patients with poor survival after apatinib treatment. Conclusions High FABP7 expression is associated with poor prognosis in glioma patients. FABP7, which is important for glioma angiogenesis, may serve as an independent prognostic predictor in glioma patients.
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Peng Z, Liu XY, Cheng Z, Kai W, Song Z. Comprehensive analysis of a new immune-related prognostic signature for esophageal cancer and its correlation with infiltrating immune cells and target genes. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:1576. [PMID: 34790782 PMCID: PMC8576727 DOI: 10.21037/atm-21-4756] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 09/24/2021] [Indexed: 12/24/2022]
Abstract
Background The incidence of esophageal cancer (ESCA) is increasing rapidly, and the 5-year survival rate is less than 20%. This study provides new ideas for clinical treatment by establishing a prognostic signature composed of immune-related genes (IRGs), and fully analyzing its relationship with target genes and the tumor microenvironment (TME). Methods We downloaded the ESCA expression matrix and clinical information from The Cancer Genome Atlas (TCGA) database. Differential expression genes (DEGs) were identified with the edgeR package and crossed with the IRGs we obtained from the ImmPort database to obtain differential IRGs (DEIRGs). The prognostic signature was then obtained through univariate Cox, LASSO-Cox, and multivariate Cox analyses. The receiver operating characteristic (ROC) curve was used to evaluate the prediction effect of the model. The immune cell infiltration abundance obtained by ssGSEA and therapeutic target genes was used to perform sufficient correlation analysis with the obtained prognostic signature and related genes. Results A total of 173 samples were obtained from TCGA database, including 162 tumor and 11 normal samples. The 3,033 differential genes were used to obtain 254 DEIRGs by intersections with 2,483 IRGs (IRGs) obtained from the ImmPort Database. Finally, multivariate Cox regression analysis identified eight prognostic DEIRGs and established a new prognostic signature (HR: 2.49, 95% CI: 1.68–3.67; P<0.001). Based on the expression of the eight genes, the cohort was then divided into high and low risk groups and Kaplan-Meier (K-M) curves were plotted with the log-rank test P<0.0001 and 1-, 3-year area under the curve (AUC) >0.7. The K-M curves grouped according to high and low risks performed well in the two subgroup validation cohorts, with log-rank test P<0.05. There were differences in the degree of infiltration of 16 kinds of immune cells in tumor and normal samples, and the infiltration abundance of 12 kinds of immune cells was different in the high and low-risk groups. Conclusions An effective and validated prognostic signature composed of IRGs was established and had a strong correlation with immune cells and target genes of drug therapy.
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Affiliation(s)
- Zhang Peng
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xin-Yuan Liu
- School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Zeng Cheng
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Wu Kai
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhao Song
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Sun C, Wang S, Zhang Y, Yang F, Zeng T, Meng F, Yang M, Yang Y, Hua Y, Fu Z, Li J, Huang X, Wu H, Yin Y, Li W. Risk Signature of Cancer-Associated Fibroblast-Secreted Cytokines Associates With Clinical Outcomes of Breast Cancer. Front Oncol 2021; 11:628677. [PMID: 34395236 PMCID: PMC8356635 DOI: 10.3389/fonc.2021.628677] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 06/16/2021] [Indexed: 11/18/2022] Open
Abstract
Cancer-associated fibroblasts (CAFs) are key components in tumor microenvironment (TME). The secreted products of CAFs play important roles in regulating tumor cells and further impacting clinical prognosis. This study aims to reveal the relationship between CAF-secreted cytokines and breast cancer (BC) by constructing the risk signature. We performed three algorithms to reveal CAF-related cytokines in the TCGA BC dataset and identified five prognosis-related cytokines. Then we used single-cell RNA sequencing (ScRNA-Seq) datasets of BC to confirm the expression level of these five cytokines in CAFs. METABRIC and other independent datasets were utilized to validate the findings in further analyses. Based on the identified five-cytokine signature derived from CAFs, BC patients with high-risk score (RS) had shorter overall survival than low-RS cases. Further analysis suggested that the high-RS level correlated with cell proliferation and mast cell infiltration in BCs of the Basal-like subtype. The results also indicated that the level of RS could discriminate the high-risk BC cases harboring driver mutations (i.e., PI3KCA, CDH1, and TP53). Additionally, the status of five-cytokine signature was associated with the frequency and molecular timing of whole genome duplication (WGD) events. Intratumor heterogeneity (ITH) analysis among BC samples indicated that the high-RS level was associated with the increase of tumor subclones. This work demonstrated that the prognostic signature based on CAF-secreted cytokines was associated with clinical outcome, tumor progression, and genetic alteration. Our findings may provide insights to develop novel strategies for early intervention and prognostic prediction of BC.
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Affiliation(s)
- Chunxiao Sun
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Siwei Wang
- Department of Thoracic Surgery, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing, China
| | - Yuchen Zhang
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Fan Yang
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Tianyu Zeng
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Fanchen Meng
- Department of Thoracic Surgery, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing, China
| | - Mengzhu Yang
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yiqi Yang
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yijia Hua
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ziyi Fu
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jun Li
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiang Huang
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hao Wu
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yongmei Yin
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Personalized Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Wei Li
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Department of Oncology, Sir Run Run Hospital of Nanjing Medical University, Nanjing, China
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11
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Liu X, Niu X, Qiu Z. A Five-Gene Signature Based on Stromal/Immune Scores in the Tumor Microenvironment and Its Clinical Implications for Liver Cancer. DNA Cell Biol 2020; 39:1621-1638. [PMID: 32758021 DOI: 10.1089/dna.2020.5512] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Increasing evidence highlights the clinical significance of stromal cells and immune cells in the liver cancer microenvironment. However, reliable prognostic models have not been well established. This study aimed to develop a gene signature for liver cancer based on stromal and immune scores. Using the estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) algorithm, stromal and immune scores were estimated based on the transcriptome profile of The Cancer Genome Atlas (TCGA) liver cancer cohort. Stromal-/immune-related differentially expressed genes were identified, followed by functional enrichment analysis. The Cox regression model was used to select prognostic genes and construct a gene signature. Its predictive potential was evaluated by receiver operating characteristic (ROC). The correlation between the risk score and immune cell infiltration was analyzed using Tumor Immune Estimation Resource (TIMER). Three hundred sixty-four upregulated and 10 downregulated stromal-/immune-related genes were identified, were mainly enriched in immune-related processes and pathways. Through univariate and multivariate cox survival analysis, a five-gene risk score was constructed, composed of FABP3, HTRA3, OLFML2B, PDZD4 and SLAMF6. Patients with high score indicated a poorer prognosis than those with low risk score. The areas under the ROC curves of overall survival (OS), progression-free interval, 3-, 5-year, OS status were 0.68, 0.57, 0.72, 0.74 and 0.728, indicating its well performance on predicting patients' prognoses. Furthermore, the risk score and the five genes were significantly correlated with immune cell infiltration in the tumor microenvironment. In this study, we proposed a prognostic five-gene signature based on stromal/immune scores in the liver cancer microenvironment.
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Affiliation(s)
- Xichun Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xing Niu
- Department of Second Clinical College, Shengjing Hospital Affiliated to China Medical University, Shenyang, China
| | - Zhigang Qiu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
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12
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Liu T, Fang P, Han C, Ma Z, Xu W, Xia W, Hu J, Xu Y, Xu L, Yin R, Wang S, Zhang Q. Four transcription profile-based models identify novel prognostic signatures in oesophageal cancer. J Cell Mol Med 2020; 24:711-721. [PMID: 31746108 PMCID: PMC6933393 DOI: 10.1111/jcmm.14779] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Revised: 08/31/2019] [Accepted: 09/22/2019] [Indexed: 12/24/2022] Open
Abstract
Oesophageal cancer (ESCA) is a clinically challenging disease with poor prognosis and health-related quality of life. Here, we investigated the transcriptome of ESCA to identify high risk-related signatures. A total of 159 ESCA patients of The Cancer Genome Atlas (TCGA) were sorted by three phases. In the discovery phase, differentially expressed transcripts were filtered; in the training phase, two adjusted Cox regressions and two machine leaning models were used to construct and estimate signatures; and in the validation phase, prognostic signatures were validated in the testing dataset and the independent external cohort. We constructed two signatures from three types of RNA markers by Akaike information criterion (AIC) and least absolute shrinkage and selection operator (LASSO) Cox regressions, respectively, and all candidate markers were further estimated by Random Forest (RFS) and Support Vector Machine (SVM) algorithms. Both signatures had good predictive performances in the independent external oesophageal squamous cell carcinoma (ESCC) cohort and performed better than common clinicopathological indicators in the TCGA dataset. Machine learning algorithms predicted prognosis with high specificities and measured the importance of markers to verify the risk weightings. Furthermore, the cell function and immunohistochemical (IHC) staining assays identified that the common risky marker FABP3 is a novel oncogene in ESCA.
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Affiliation(s)
- Tongyan Liu
- Department of Thoracic SurgeryThe Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer ResearchNanjingChina
- Department of Scientific ResearchThe Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer ResearchNanjingChina
| | - Panqi Fang
- Department of Thoracic SurgeryThe Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer ResearchNanjingChina
- Department of Clinical PharmacySchool of Basic Medical Sciences and Clinical PharmacyChina Pharmaceutical UniversityNanjingChina
| | - Chencheng Han
- Department of Thoracic SurgeryThe Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer ResearchNanjingChina
- The Fourth Clinical College of Nanjing Medical UniversityNanjingChina
| | - Zhifei Ma
- Department of Thoracic SurgeryThe Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer ResearchNanjingChina
- The Fourth Clinical College of Nanjing Medical UniversityNanjingChina
| | - Weizhang Xu
- Department of Thoracic SurgeryThe Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer ResearchNanjingChina
| | - Wenjia Xia
- Department of Thoracic SurgeryThe Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer ResearchNanjingChina
| | - Jingwen Hu
- Department of Thoracic SurgeryThe Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer ResearchNanjingChina
| | - Youtao Xu
- Department of Thoracic SurgeryThe Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer ResearchNanjingChina
| | - Lin Xu
- Department of Thoracic SurgeryThe Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer ResearchNanjingChina
| | - Rong Yin
- Department of Thoracic SurgeryThe Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer ResearchNanjingChina
- Department of Scientific ResearchThe Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer ResearchNanjingChina
- Jiangsu Biobank of Clinical ResourcesNanjingChina
| | - Siwei Wang
- Department of Thoracic SurgeryThe Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer ResearchNanjingChina
- The Fourth Clinical College of Nanjing Medical UniversityNanjingChina
| | - Qin Zhang
- Department of Thoracic SurgeryThe Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer ResearchNanjingChina
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