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Xu M, Tang J, Sun Q, Meng J, Chen G, Chang Y, Yao Y, Ji J, Luo H, Chen L, Lu M, Shi W. CENPN contributes to pancreatic carcinoma progression through the MDM2-mediated p53 signaling pathway. Arch Med Sci 2024; 20:1655-1671. [PMID: 39649279 PMCID: PMC11623148 DOI: 10.5114/aoms/171956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 09/06/2023] [Indexed: 12/10/2024] Open
Abstract
Introduction We undertook an in-depth investigation of the data pertaining to pancreatic adenocarcinoma (PAAD) to identify potential targets for the development of precision therapies. Material and methods The construction of a protein-protein interaction (PPI) network was based on overlapping differentially expressed genes (DEGs) identified in the GSE16515, GSE32676, and GSE125158 datasets. A subsequent bioinformatic analysis was performed on the interconnected genes within the PPI network, leading to the identification of the central gene, CENPN. In vitro experimentation such as CCK8 and Transwell experiments was employed to elucidate the impact of CENPN expression patterns on PAAD cell proliferation, migration, and invasion. Furthermore, the investigation revealed through comprehensive enrichment analysis that the pivotal signaling pathway associated with CENPN is the p53 signaling pathway. Results Following a comprehensive bioinformatic analysis of 161 concordant differentially expressed genes (DEGs) across three microarray datasets, CENPN emerged as the central gene under investigation. Overexpression of CENPN in pancreatic adenocarcinoma (PAAD) was associated with unfavorable patient outcomes and heightened sensitivity to four PAAD therapies: gemcitabine, docetaxel, paclitaxel, and sunitinib. Reduced CENPN expression impeded PAAD cell proliferation, migration, and invasion; however, these effects were counteracted upon upregulation of CENPN expression. Additionally, CENPN interacted with MDM2, promoting PAAD progression by targeting the p53 signaling pathway. Conclusions The findings of our study substantiate that CENPN is associated with the pathogenesis of PAAD. Consequently, CENPN appears to be a promising candidate for targeted precision therapy in clinical applications.
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Affiliation(s)
- Ming Xu
- Department of Gastroenterology, Shanghai Pudong New Area People’s Hospital, Shanghai, China
| | - Jie Tang
- Department of Gastroenterology, Shanghai Pudong New Area People’s Hospital, Shanghai, China
| | - Qiong Sun
- Department of Oncology, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Jing Meng
- Department of Oncology, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Guoyu Chen
- Department of Gastroenterology, Shanghai Pudong New Area People’s Hospital, Shanghai, China
| | - Yunli Chang
- Department of Gastroenterology, Shanghai Pudong New Area People’s Hospital, Shanghai, China
| | - Yao Yao
- Department of Gastroenterology, Shanghai Pudong New Area People’s Hospital, Shanghai, China
| | - Jieru Ji
- Department of Gastroenterology, Shanghai Pudong New Area People’s Hospital, Shanghai, China
| | - Hao Luo
- Department of Gastroenterology, Shanghai Pudong New Area People’s Hospital, Shanghai, China
| | - Lingling Chen
- Department of Gastroenterology, Shanghai Pudong New Area People’s Hospital, Shanghai, China
| | - Minxue Lu
- Department of Gastroenterology, Huzhou College Affiliated Nantaihu Hospital, Huzhou, Zhejiang, China
| | - Weiwei Shi
- Department of Oncology, The First Medical Center, Chinese PLA General Hospital, Beijing, China
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Zhang Y, Wang Y, He X, Yao R, Fan L, Zhao L, Lu B, Pang Z. Genome instability-related LINC02577, LINC01133 and AC107464.2 are lncRNA prognostic markers correlated with immune microenvironment in pancreatic adenocarcinoma. BMC Cancer 2023; 23:430. [PMID: 37173624 PMCID: PMC10176692 DOI: 10.1186/s12885-023-10831-4] [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: 05/11/2022] [Accepted: 04/09/2023] [Indexed: 05/15/2023] Open
Abstract
BACKGROUND Pancreatic adenocarcinoma (PAAD) is a leading cause of malignancy-related deaths worldwide, and the efficacy of immunotherapy on PAAD is limited. Studies report that long non-coding RNAs (lncRNAs) play an important role in modulating genomic instability and immunotherapy. However, the identification of genome instability-related lncRNAs and their clinical significance has not been investigated in PAAD. METHODS The current study developed a computational framework for mutation hypothesis based on lncRNA expression profile and somatic mutation spectrum in pancreatic adenocarcinoma genome. We explored the potential of GInLncRNAs(genome instability-related lncRNAs) through co-expression analysis and function enrichment analysis. We further analyzed GInLncRNAs by Cox regression and used the results to construct a prognostic lncRNA signature. Finally, we analyzed the relationship between GILncSig (genomic instability derived 3-lncRNA signature) and immunotherapy. RESULTS A GILncSig was developed using bioinformatics analyses. It could divide patients into high-risk and low-risk groups, and there was a significant difference in OS between the two groups. In addition, GILncSig was associated with genome mutation rate in pancreatic adenocarcinoma, indicating its potential value as a marker for genomic instability. The GILncSig accurately grouped wild type patients of KRAS into two risk groups. The prognosis of the low-risk group was significantly improved. GILncSig was significantly correlated with the level of immune cell infiltration and immune checkpoint. CONCLUSIONS In summary, the current study provides a basis for further studies on the role of lncRNA in genomic instability and immunotherapy. The study provides a novel method for identification of cancer biomarkers related to genomic instability and immunotherapy.
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Affiliation(s)
- Yinjiang Zhang
- School of Pharmacy, Minzu University of China, No. 27, Zhongguancunnan Street, Haidian District, Beijing, 100081, People's Republic of China
- Key Laboratory of Ethnomedicine, Ministry of Education, Minzu University of China), Beijing, People's Republic of China
| | - Yao Wang
- Department of Radiation Oncology, Chinese PLA General Hospital, Beijing, 100853, China
| | - Xu He
- School of Pharmacy, Minzu University of China, No. 27, Zhongguancunnan Street, Haidian District, Beijing, 100081, People's Republic of China
- Key Laboratory of Ethnomedicine, Ministry of Education, Minzu University of China), Beijing, People's Republic of China
| | - Rongfei Yao
- School of Pharmacy, Minzu University of China, No. 27, Zhongguancunnan Street, Haidian District, Beijing, 100081, People's Republic of China
- Key Laboratory of Ethnomedicine, Ministry of Education, Minzu University of China), Beijing, People's Republic of China
| | - Lu Fan
- School of Pharmacy, Minzu University of China, No. 27, Zhongguancunnan Street, Haidian District, Beijing, 100081, People's Republic of China
- Key Laboratory of Ethnomedicine, Ministry of Education, Minzu University of China), Beijing, People's Republic of China
| | - Linyi Zhao
- School of Pharmacy, Minzu University of China, No. 27, Zhongguancunnan Street, Haidian District, Beijing, 100081, People's Republic of China
- Key Laboratory of Ethnomedicine, Ministry of Education, Minzu University of China), Beijing, People's Republic of China
| | - Binan Lu
- School of Pharmacy, Minzu University of China, No. 27, Zhongguancunnan Street, Haidian District, Beijing, 100081, People's Republic of China.
- Key Laboratory of Ethnomedicine, Ministry of Education, Minzu University of China), Beijing, People's Republic of China.
| | - Zongran Pang
- School of Pharmacy, Minzu University of China, No. 27, Zhongguancunnan Street, Haidian District, Beijing, 100081, People's Republic of China.
- Key Laboratory of Ethnomedicine, Ministry of Education, Minzu University of China), Beijing, People's Republic of China.
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Li J, Li W, Wang H, Ni B, Liu Y. Development and validation of a novel ferroptosis‑related lncRNA prognostic signature for pancreatic adenocarcinoma. Mol Med Rep 2023; 27:56. [PMID: 36660936 PMCID: PMC9879080 DOI: 10.3892/mmr.2023.12943] [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: 04/19/2022] [Accepted: 11/29/2022] [Indexed: 01/19/2023] Open
Abstract
Long non‑coding RNAs (lncRNAs) serve a pivotal role in the regulation of cancer cell ferroptosis. However, the prognostic value of ferroptosis‑related lncRNAs in pancreatic adenocarcinoma (PAAD) largely remains unclear. We aimed at constructing a lncRNA‑based signature to improve the prognosis prediction of PAAD. In the present study, the transcriptome profiling data and clinical information of patients with PAAD were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Gene Consortium (ICGC) databases. Univariate Cox regression analysis of the TCGA cohort demonstrated that 26 ferroptosis‑related lncRNAs had significant prognostic value for PAAD (all P<0.01). Least absolute shrinkage and selection operator regression and multivariate Cox proportional hazards regression analyses were performed to construct a prognostic ferroptosis‑related lncRNA signature (FRLS) comprising nine ferroptosis‑related lncRNAs. The efficacy of this FRLS was verified in the training (TCGA) and validation (ICGC) cohorts. Based on the risk model, high risk scores were significantly correlated with poor overall survival (OS) (hazard ratio, 1.314; 95% confidence interval, 1.218‑1.418; P<0.001). The receiver operating characteristic curves and principal component analysis further demonstrated the robust prognostic ability of the FRLS. Furthermore, a nomogram with favorable predictive efficacy for the prediction of OS was constructed based on the FRLS and clinical features. Gene set enrichment analysis demonstrated that the genes in the FRLS participated in a number of cancer‑associated immunoregulatory pathways. Importantly, it was demonstrated that immune infiltration and response to cancer immunotherapy differed significantly between the high and low‑risk groups according to the FRLS. In conclusion, the risk signature based on the FRLS has potential for the clinical prediction of prognosis and immunotherapy response in patients with PAAD.
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Affiliation(s)
- Jian Li
- Department of Pathophysiology, College of High Altitude Military Medicine, Third Military Medical University, Chongqing 400038, P.R. China,Department of General Surgery, Air Force Hospital of Western Theater Command, Chengdu, Sichuan 610065, P.R. China,Institute of Hepatopancreatobiliary Surgery, Southwest Hospital, Third Military Medical University, Chongqing 400038, P.R. China
| | - Wenhua Li
- Department of Cadre Ward, Air Force Hospital of Western Theater Command, Chengdu, Sichuan 610065, P.R. China
| | - Huaizhi Wang
- Institute of Hepatopancreatobiliary Surgery, Southwest Hospital, Third Military Medical University, Chongqing 400038, P.R. China
| | - Bing Ni
- Department of Pathophysiology, College of High Altitude Military Medicine, Third Military Medical University, Chongqing 400038, P.R. China,Correspondence to: Professor Bing Ni, Department of Pathophysiology, College of High Altitude Military Medicine, Third Military Medical University, 30 Gaotanyan Main Street, Shapingba, Chongqing 400038, P.R. China, E-mail:
| | - Yongkang Liu
- Department of General Surgery, Air Force Hospital of Western Theater Command, Chengdu, Sichuan 610065, P.R. China,Dr Yongkang Liu, Department of General Surgery, Air Force Hospital of Western Theater Command, 18 Shunjiang Street, Jinjiang, Chengdu, Sichuan 610065, P.R. China, E-mail:
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Yang H, Zhang W, Ding J, Hu J, Sun Y, Peng W, Chu Y, Xie L, Mei Z, Shao Z, Xiao Y. A novel genomic instability-derived lncRNA signature to predict prognosis and immune characteristics of pancreatic ductal adenocarcinoma. Front Immunol 2022; 13:970588. [PMID: 36148233 PMCID: PMC9486402 DOI: 10.3389/fimmu.2022.970588] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 08/29/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignant tumor of the digestive system. Its grim prognosis is mainly attributed to the lack of means for early diagnosis and poor response to treatments. Genomic instability is shown to be an important cancer feature and prognostic factor, and its pattern and extent may be associated with poor treatment outcomes in PDAC. Recently, it has been reported that long non-coding RNAs (lncRNAs) play a key role in maintaining genomic instability. However, the identification and clinical significance of genomic instability-related lncRNAs in PDAC have not been fully elucidated. METHODS Genomic instability-derived lncRNA signature (GILncSig) was constructed based on the results of multiple regression analysis combined with genomic instability-associated lncRNAs and its predictive power was verified by the Kaplan-Meier method. And real-time quantitative polymerase chain reaction (qRT-PCR) was used for simple validation in human cancers and their adjacent non-cancerous tissues. In addition, the correlation between GILncSig and tumor microenvironment (TME) and epithelial-mesenchymal transition (EMT) was investigated by Pearson correlation analysis. RESULTS The computational framework identified 206 lncRNAs associated with genomic instability in PDAC and was subsequently used to construct a genome instability-derived five lncRNA-based gene signature. Afterwards, we successfully validated its prognostic capacity in The Cancer Genome Atlas (TCGA) cohort. In addition, via careful examination of the transcriptome expression profile of PDAC patients, we discovered that GILncSig is associated with EMT and an adaptive immunity deficient immune profile within TME. CONCLUSIONS Our study established a genomic instability-associated lncRNAs-derived model (GILncSig) for prognosis prediction in patients with PDAC, and revealed the potential functional regulatory role of GILncSig.
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Affiliation(s)
- Huijie Yang
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Weiwen Zhang
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Jin Ding
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Jingyi Hu
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Yi Sun
- Department of Pathology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Weijun Peng
- Department of Integrated Traditional Chinese and Western Medicine, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Yi Chu
- Department of Gastroenterology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Lingxiang Xie
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Zubing Mei
- Department of Anorectal Surgery, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Anorectal Disease Institute of Shuguang Hospital, Shanghai, China
| | - Zhuo Shao
- Department of General Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Yang Xiao
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital, Central South University, Changsha, China
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Zhu X, Yu R, Peng Y, Miao Y, Jiang K, Li Q. Identification of genomic instability related lncRNA signature with prognostic value and its role in cancer immunotherapy in pancreatic cancer. Front Genet 2022; 13:990661. [PMID: 36118868 PMCID: PMC9481284 DOI: 10.3389/fgene.2022.990661] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 08/08/2022] [Indexed: 12/02/2022] Open
Abstract
Background: Increasing evidence suggested the critical roles of lncRNAs in the maintenance of genomic stability. However, the identification of genomic instability-related lncRNA signature (GILncSig) and its role in pancreatic cancer (PC) remains largely unexplored. Methods: In the present study, a systematic analysis of lncRNA expression profiles and somatic mutation profiles was performed in PC patients from The Cancer Genome Atlas (TCGA). We then develop a risk score model to describe the characteristics of the model and verify its prediction accuracy. ESTIMATE algorithm, single-sample gene set enrichment analysis (ssGSEA), and CIBERSORT analysis were employed to reveal the correlation between tumor immune microenvironment, immune infiltration, immune checkpoint blockade (ICB) therapy, and GILncSig in PC. Results: We identified 206 GILnc, of which five were screened to develop a prognostic GInLncSig model. Multivariate Cox regression analysis and stratified analysis revealed that the prognostic value of the GILncSig was independent of other clinical variables. Receiver operating characteristic (ROC) analysis suggested that GILncSig is better than the existing lncRNA-related signatures in predicting survival. Additionally, the prognostic performance of the GILncSig was also found to be favorable in patients carrying wild-type KRAS, TP53, and SMAD4. Besides, a nomogram exhibited appreciable reliability for clinical application in predicting the prognosis of patients. Finally, the relationship between the GInLncSig model and the immune landscape in PC reflected its application value in clinical immunotherapy. Conclusion: In summary, the GILncSig identified by us may serve as novel prognostic biomarkers, and could have a crucial role in immunotherapy decisions for PC patients.
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Affiliation(s)
- Xiaole Zhu
- Pancreas Center, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Pancreas Institute, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Rong Yu
- Pancreas Center, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Pancreas Institute, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yunpeng Peng
- Pancreas Center, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Pancreas Institute, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yi Miao
- Pancreas Center, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Pancreas Institute, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Kuirong Jiang
- Pancreas Center, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Pancreas Institute, Nanjing Medical University, Nanjing, Jiangsu, China
- *Correspondence: Kuirong Jiang, ; Qiang Li,
| | - Qiang Li
- Pancreas Center, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Pancreas Institute, Nanjing Medical University, Nanjing, Jiangsu, China
- *Correspondence: Kuirong Jiang, ; Qiang Li,
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Dai L, Mugaanyi J, Cai X, Lu C, Lu C. Pancreatic adenocarcinoma associated immune-gene signature as a novo risk factor for clinical prognosis prediction in hepatocellular carcinoma. Sci Rep 2022; 12:11944. [PMID: 35831362 PMCID: PMC9279485 DOI: 10.1038/s41598-022-16155-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 07/05/2022] [Indexed: 11/30/2022] Open
Abstract
Pancreatic adenocarcinoma (PAAD) has high mortality and a very poor prognosis. Both surgery and chemotherapy have a suboptimal therapeutic effect, and this caused a need to find new approaches such as immunotherapy. Therefore, it is essential to develop a new model to predict patient prognosis and facilitate early intervention. Our study screened out and validated the target molecules based on the TCGA-PAAD dataset. We established the risk signature using univariate and multivariate Cox regression analysis and used GSE62452 and GSE28735 to verify the accuracy and reliability of the model. Expanded application of PAAD-immune-related genes signature (-IRGS) on other datasets was conducted, and the corresponding nomograms were constructed. We also analyzed the correlation between immune-related cells/genes and potential treatments. Our research demonstrated that a high riskscore of PAAD-IRGS in patients with PAAD was correlated with poor overall survival, disease-specific survival and progression free interval. The same results were observed in patients with LIHC. The models constructed were confirmed to be accurate and reliable. We found various correlations between PAAD-IRGS and immune-related cells/genes, and the potential therapeutic agents. These findings indicate that PAAD-IRGS may be a promising indicator for prognosis and of the tumor-immune microenvironment status in PAAD.
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Affiliation(s)
- Lei Dai
- Department of Hepatopancreatobiliary Surgery, Ningbo Medical Centre Lihuili Hospital, Ningbo University, 1111 Jiangnan Road, Ningbo, 315040, Zhejiang, China
| | - Joseph Mugaanyi
- Department of Hepatopancreatobiliary Surgery, Ningbo Medical Centre Lihuili Hospital, Ningbo University, 1111 Jiangnan Road, Ningbo, 315040, Zhejiang, China
| | - Xingchen Cai
- Department of Hepatopancreatobiliary Surgery, Ningbo Medical Centre Lihuili Hospital, Ningbo University, 1111 Jiangnan Road, Ningbo, 315040, Zhejiang, China
| | - Caide Lu
- Department of Hepatopancreatobiliary Surgery, Ningbo Medical Centre Lihuili Hospital, Ningbo University, 1111 Jiangnan Road, Ningbo, 315040, Zhejiang, China.
| | - Changjiang Lu
- Department of Hepatopancreatobiliary Surgery, Ningbo Medical Centre Lihuili Hospital, Ningbo University, 1111 Jiangnan Road, Ningbo, 315040, Zhejiang, China.
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Zhang Z, Huang L, Li J, Wang P. Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system. BMC Bioinformatics 2022; 23:124. [PMID: 35395711 PMCID: PMC8991575 DOI: 10.1186/s12859-022-04657-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 03/11/2022] [Indexed: 12/13/2022] Open
Abstract
Objectives Immune microenvironment was closely related to the occurrence and progression of colorectal cancer (CRC). The objective of the current research was to develop and verify a Machine learning survival predictive system for CRC based on immune gene expression data and machine learning algorithms. Methods The current study performed differentially expressed analyses between normal tissues and tumor tissues. Univariate Cox regression was used to screen prognostic markers for CRC. Prognostic immune genes and transcription factors were used to construct an immune-related regulatory network. Three machine learning algorithms were used to create an Machine learning survival predictive system for CRC. Concordance indexes, calibration curves, and Brier scores were used to evaluate the performance of prognostic model. Results Twenty immune genes (BCL2L12, FKBP10, XKRX, WFS1, TESC, CCR7, SPACA3, LY6G6C, L1CAM, OSM, EXTL1, LY6D, FCRL5, MYEOV, FOXD1, REG3G, HAPLN1, MAOB, TNFSF11, and AMIGO3) were recognized as independent risk factors for CRC. A prognostic nomogram was developed based on the previous immune genes. Concordance indexes were 0.852, 0.778, and 0.818 for 1-, 3- and 5-year survival. This prognostic model could discriminate high risk patients with poor prognosis from low risk patients with favorable prognosis. Conclusions The current study identified twenty prognostic immune genes for CRC patients and constructed an immune-related regulatory network. Based on three machine learning algorithms, the current research provided three individual mortality predictive curves. The Machine learning survival predictive system was available at: https://zhangzhiqiao8.shinyapps.io/Artificial_Intelligence_Survival_Prediction_for_CRC_B1005_1/, which was valuable for individualized treatment decision before surgery. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04657-3.
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Affiliation(s)
- Zhiqiao Zhang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, Guangdong, China
| | - Liwen Huang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, Guangdong, China
| | - Jing Li
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, Guangdong, China
| | - Peng Wang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, Guangdong, China.
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Olmedo-Suárez MÁ, Ramírez-Díaz I, Pérez-González A, Molina-Herrera A, Coral-García MÁ, Lobato S, Sarvari P, Barreto G, Rubio K. Epigenetic Regulation in Exposome-Induced Tumorigenesis: Emerging Roles of ncRNAs. Biomolecules 2022; 12:513. [PMID: 35454102 PMCID: PMC9032613 DOI: 10.3390/biom12040513] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 03/18/2022] [Accepted: 03/21/2022] [Indexed: 02/06/2023] Open
Abstract
Environmental factors, including pollutants and lifestyle, constitute a significant role in severe, chronic pathologies with an essential societal, economic burden. The measurement of all environmental exposures and assessing their correlation with effects on individual health is defined as the exposome, which interacts with our unique characteristics such as genetics, physiology, and epigenetics. Epigenetics investigates modifications in the expression of genes that do not depend on the underlying DNA sequence. Some studies have confirmed that environmental factors may promote disease in individuals or subsequent progeny through epigenetic alterations. Variations in the epigenetic machinery cause a spectrum of different disorders since these mechanisms are more sensitive to the environment than the genome, due to the inherent reversible nature of the epigenetic landscape. Several epigenetic mechanisms, including modifications in DNA (e.g., methylation), histones, and noncoding RNAs can change genome expression under the exogenous influence. Notably, the role of long noncoding RNAs in epigenetic processes has not been well explored in the context of exposome-induced tumorigenesis. In the present review, our scope is to provide relevant evidence indicating that epigenetic alterations mediate those detrimental effects caused by exposure to environmental toxicants, focusing mainly on a multi-step regulation by diverse noncoding RNAs subtypes.
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Affiliation(s)
- Miguel Ángel Olmedo-Suárez
- International Laboratory EPIGEN, Consejo de Ciencia y Tecnología del Estado de Puebla (CONCYTEP), Puebla 72160, Mexico; (M.Á.O.-S.); (I.R.-D.); (A.P.-G.); (A.M.-H.); (M.Á.C.-G.); (S.L.); (P.S.); (G.B.)
- Licenciatura en Médico Cirujano, Universidad de la Salud del Estado de Puebla (USEP), Puebla 72000, Mexico
| | - Ivonne Ramírez-Díaz
- International Laboratory EPIGEN, Consejo de Ciencia y Tecnología del Estado de Puebla (CONCYTEP), Puebla 72160, Mexico; (M.Á.O.-S.); (I.R.-D.); (A.P.-G.); (A.M.-H.); (M.Á.C.-G.); (S.L.); (P.S.); (G.B.)
- Facultad de Biotecnología, Campus Puebla, Universidad Popular Autónoma del Estado de Puebla (UPAEP), Puebla 72410, Mexico
| | - Andrea Pérez-González
- International Laboratory EPIGEN, Consejo de Ciencia y Tecnología del Estado de Puebla (CONCYTEP), Puebla 72160, Mexico; (M.Á.O.-S.); (I.R.-D.); (A.P.-G.); (A.M.-H.); (M.Á.C.-G.); (S.L.); (P.S.); (G.B.)
- Licenciatura en Médico Cirujano, Universidad de la Salud del Estado de Puebla (USEP), Puebla 72000, Mexico
| | - Alejandro Molina-Herrera
- International Laboratory EPIGEN, Consejo de Ciencia y Tecnología del Estado de Puebla (CONCYTEP), Puebla 72160, Mexico; (M.Á.O.-S.); (I.R.-D.); (A.P.-G.); (A.M.-H.); (M.Á.C.-G.); (S.L.); (P.S.); (G.B.)
- Licenciatura en Médico Cirujano, Universidad de la Salud del Estado de Puebla (USEP), Puebla 72000, Mexico
| | - Miguel Ángel Coral-García
- International Laboratory EPIGEN, Consejo de Ciencia y Tecnología del Estado de Puebla (CONCYTEP), Puebla 72160, Mexico; (M.Á.O.-S.); (I.R.-D.); (A.P.-G.); (A.M.-H.); (M.Á.C.-G.); (S.L.); (P.S.); (G.B.)
- Decanato de Ciencias de la Salud, Campus Puebla, Universidad Popular Autónoma del Estado de Puebla (UPAEP), Puebla 72410, Mexico
| | - Sagrario Lobato
- International Laboratory EPIGEN, Consejo de Ciencia y Tecnología del Estado de Puebla (CONCYTEP), Puebla 72160, Mexico; (M.Á.O.-S.); (I.R.-D.); (A.P.-G.); (A.M.-H.); (M.Á.C.-G.); (S.L.); (P.S.); (G.B.)
- Licenciatura en Médico Cirujano, Universidad de la Salud del Estado de Puebla (USEP), Puebla 72000, Mexico
| | - Pouya Sarvari
- International Laboratory EPIGEN, Consejo de Ciencia y Tecnología del Estado de Puebla (CONCYTEP), Puebla 72160, Mexico; (M.Á.O.-S.); (I.R.-D.); (A.P.-G.); (A.M.-H.); (M.Á.C.-G.); (S.L.); (P.S.); (G.B.)
| | - Guillermo Barreto
- International Laboratory EPIGEN, Consejo de Ciencia y Tecnología del Estado de Puebla (CONCYTEP), Puebla 72160, Mexico; (M.Á.O.-S.); (I.R.-D.); (A.P.-G.); (A.M.-H.); (M.Á.C.-G.); (S.L.); (P.S.); (G.B.)
- Laboratoire IMoPA, CNRS, Université de Lorraine, UMR 73635 Nancy, France
- Lung Cancer Epigenetic, Max-Planck-Institute for Heart and Lung Research, 61231 Bad Nauheim, Germany
| | - Karla Rubio
- International Laboratory EPIGEN, Consejo de Ciencia y Tecnología del Estado de Puebla (CONCYTEP), Puebla 72160, Mexico; (M.Á.O.-S.); (I.R.-D.); (A.P.-G.); (A.M.-H.); (M.Á.C.-G.); (S.L.); (P.S.); (G.B.)
- Licenciatura en Médico Cirujano, Universidad de la Salud del Estado de Puebla (USEP), Puebla 72000, Mexico
- Laboratoire IMoPA, CNRS, Université de Lorraine, UMR 73635 Nancy, France
- Lung Cancer Epigenetic, Max-Planck-Institute for Heart and Lung Research, 61231 Bad Nauheim, Germany
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9
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Zhang Y, Fang Y, Ma L, Xu J, Lv C, Deng L, Zhu G. LINC00857 regulated by ZNF460 enhances the expression of CLDN12 by sponging miR-150-5p and recruiting SRSF1 for alternative splicing to promote epithelial-mesenchymal transformation of pancreatic adenocarcinoma cells. RNA Biol 2021; 19:548-559. [PMID: 35442145 PMCID: PMC9037484 DOI: 10.1080/15476286.2021.1992995] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 10/08/2021] [Indexed: 12/14/2022] Open
Abstract
Recent research unveiled that LINC00857 plays a regulatory role in multiple human cancers, such as lung adenocarcinoma and gastric cancer. Nevertheless, the function of LINC00857 in pancreatic adenocarcinoma (PAAD) remains unclear. This study concentrates on LINC00857 to discuss the relevant molecular mechanism of this gene in PAAD. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) and western blot were implemented for measuring the expressions of RNAs and proteins. Wound healing and Transwell assays were used to assess cell migration and invasion, and fluorescent in situ hybridization (FISH) to locate LINC00857 in PAAD cells. Additionally, mechanism assays were conducted to validate the interaction between genes. Results indicated that LINC00857 was upregulated in PAAD cells and the knockdown of LINC00857 impeded PAAD cell migration, invasion and epithelial-mesenchymal transition (EMT). Further, it was found that LNC00857 regulates CLDN12 expression by targeting miR-150-5p. Moreover, LINC00857 was confirmed to recruit serine/arginine-rich splicing factor 1 (SRSF1) to promote the alternative splicing (AS) targeting CLDN12, affecting the phenotypes of PAAD cells. In addition, the transcription factor ZNF460 was proven to positively regulate LINC00857 expression. To sum up, LINC00857 regulated by ZNF460 upregulates CLDN12 expression by sponging miR-150-5p and recruiting SRSF1 to facilitate the progression of PAAD cells.[Figure: see text].
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Affiliation(s)
- Yong Zhang
- Department of General Surgery, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Yuan Fang
- Department of General Surgery, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Lijie Ma
- Department of General Surgery, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Jing Xu
- Department of General Surgery, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Chentao Lv
- Department of General Surgery, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Li Deng
- Department of General Surgery, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Guanghui Zhu
- Department of General Surgery, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
- Department of Gastrointestinal Surgery, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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10
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Lei Y, Tang R, Xu J, Zhang B, Liu J, Liang C, Meng Q, Hua J, Yu X, Wang W, Shi S. Construction of a novel risk model based on the random forest algorithm to distinguish pancreatic cancers with different prognoses and immune microenvironment features. Bioengineered 2021; 12:3593-3602. [PMID: 34238114 PMCID: PMC8806465 DOI: 10.1080/21655979.2021.1951527] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 06/26/2021] [Indexed: 12/16/2022] Open
Abstract
Immune-related long noncoding RNAs (irlncRNAs) are actively involved in regulating the immune status. This study aimed to establish a risk model of irlncRNAs and further investigate the roles of irlncRNAs in predicting prognosis and the immune landscape in pancreatic cancer. The transcriptome profiles and clinical information of 176 pancreatic cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Immune-related genes (irgenes) downloaded from ImmPort were used to screen 1903 immune-related lncRNAs (irlncRNAs) using Pearson's correlation analysis (R > 0.5; p < 0.001). Random survival forest (RSF) and survival tree analysis showed that 9 irlncRNAs were highly correlated with overall survival (OS) according to the variable importance (VIMP) and minimal depth. Next, Cox regression analysis was used to establish a risk model with 3 irlncRNAs (LINC00462, LINC01887, RP11-706C16.8) that was evaluated by Kaplan-Meier analysis, the areas under the curve (AUCs) of the receiver operating characteristics and the C-index. Additionally, we performed Cox regression analysis to establish the clinical prognostic model, which showed that the risk score was an independent prognostic factor (p < 0.001). A nomogram and calibration plots were drawn to visualize the clinical features. The Wilcoxon signed-rank test and Pearson's correlation analysis further explored the irlncRNA signatures and immune cell infiltration, as well as the immunotherapy response.
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Affiliation(s)
- Yalan Lei
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Rong Tang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Jin Xu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Bo Zhang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Jiang Liu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Chen Liang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Qingcai Meng
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Jie Hua
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Xianjun Yu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Wei Wang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Si Shi
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
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11
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Xiong G, Pan S, Jin J, Wang X, He R, Peng F, Li X, Wang M, Zheng J, Zhu F, Qin R. Long Noncoding Competing Endogenous RNA Networks in Pancreatic Cancer. Front Oncol 2021; 11:765216. [PMID: 34760707 PMCID: PMC8573238 DOI: 10.3389/fonc.2021.765216] [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: 08/26/2021] [Accepted: 09/20/2021] [Indexed: 12/12/2022] Open
Abstract
Pancreatic cancer (PC) is a highly malignant disease characterized by insidious onset, rapid progress, and poor therapeutic effects. The molecular mechanisms associated with PC initiation and progression are largely insufficient, hampering the exploitation of novel diagnostic biomarkers and development of efficient therapeutic strategies. Emerging evidence recently reveals that noncoding RNAs (ncRNAs), including long ncRNAs (lncRNAs) and microRNAs (miRNAs), extensively participate in PC pathogenesis. Specifically, lncRNAs can function as competing endogenous RNAs (ceRNAs), competitively sequestering miRNAs, therefore modulating the expression levels of their downstream target genes. Such complex lncRNA/miRNA/mRNA networks, namely, ceRNA networks, play crucial roles in the biological processes of PC by regulating cell growth and survival, epithelial-mesenchymal transition and metastasis, cancer stem cell maintenance, metabolism, autophagy, chemoresistance, and angiogenesis. In this review, the emerging knowledge on the lncRNA-associated ceRNA networks involved in PC initiation and progression will be summarized, and the potentials of the competitive crosstalk as diagnostic, prognostic, and therapeutic targets will be comprehensively discussed.
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Affiliation(s)
- Guangbing Xiong
- Department of Biliary-Pancreatic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shutao Pan
- Department of Biliary-Pancreatic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jikuan Jin
- Department of Biliary-Pancreatic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoxiang Wang
- Department of Biliary-Pancreatic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ruizhi He
- Department of Biliary-Pancreatic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Feng Peng
- Department of Biliary-Pancreatic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xu Li
- Department of Biliary-Pancreatic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Min Wang
- Department of Biliary-Pancreatic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jianwei Zheng
- Department of Biliary-Pancreatic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Feng Zhu
- Department of Biliary-Pancreatic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Renyi Qin
- Department of Biliary-Pancreatic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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12
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Ramya Devi KT, Karthik D, Mahendran T, Jaganathan MK, Hemdev SP. Long noncoding RNAs: role and contribution in pancreatic cancer. Transcription 2021; 12:12-27. [PMID: 34036896 DOI: 10.1080/21541264.2021.1922071] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Noncoding RNAs are proclaimed to be expressed in various cancer types and one such type is found to be pancreatic ductal adenocarcinoma (PDAC). The long noncoding RNAs (LncRNAs) affect the migration, invasion, and growth of tumor cells by playing important roles in the process of epigenesis, post-transcription, and transcriptional regulation along with the maintenance of apoptosis and cell cycle. It is quite subtle whether the alterations in lncRNAs would impact PDAC progression and development. This review throws a spotlight on the lncRNAs associated with tumor functions: MALAT-1, HOTAIR, HOXA13, H19, LINC01559, LINC00460, SNHG14, SNHG16, DLX6-AS1, MSC-AS1, ABHD11-AS1, DUXAP8, DANCR, XIST, DLEU2, etc. are upregulated lncRNAs whereas GAS5, HMlincRNA717, MIAT, LINC01111, lncRNA KCNK15-AS1, etc. are downregulated lncRNAs inhibiting the invasion and progression of PDAC. These data provided helps in the assessment of lncRNAs in the development, metastasis, and occurrence of PDAC and also play a vital role in the evolution of biomarkers and therapeutic agents for the treatment of PDAC.
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Affiliation(s)
- K T Ramya Devi
- Department of Biotechnology, School of Bioengineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Tamil Nadu, India
| | - Dharshene Karthik
- Department of Biotechnology, School of Bioengineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Tamil Nadu, India.,Department of Industrial Biotechnology, Sri Venkateswara College of Engineering, Chennai, India
| | - TharunSelvam Mahendran
- Molecular Biology and Genetics Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore, India
| | - M K Jaganathan
- Department of Biotechnology, School of Bioengineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Tamil Nadu, India
| | - Sanjana Prakash Hemdev
- School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, United States
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13
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He T, Huang L, Li J, Wang P, Zhang Z. Potential Prognostic Immune Biomarkers of Overall Survival in Ovarian Cancer Through Comprehensive Bioinformatics Analysis: A Novel Artificial Intelligence Survival Prediction System. Front Med (Lausanne) 2021; 8:587496. [PMID: 34109184 PMCID: PMC8180546 DOI: 10.3389/fmed.2021.587496] [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: 08/27/2020] [Accepted: 04/19/2021] [Indexed: 12/24/2022] Open
Abstract
Background: The tumour immune microenvironment plays an important role in the biological mechanisms of tumorigenesis and progression. Artificial intelligence medicine studies based on big data and advanced algorithms are helpful for improving the accuracy of prediction models of tumour prognosis. The current research aims to explore potential prognostic immune biomarkers and develop a predictive model for the overall survival of ovarian cancer (OC) based on artificial intelligence algorithms. Methods: Differential expression analyses were performed between normal tissues and tumour tissues. Potential prognostic biomarkers were identified using univariate Cox regression. An immune regulatory network was constructed of prognostic immune genes and their highly related transcription factors. Multivariate Cox regression was used to identify potential independent prognostic immune factors and develop a prognostic model for ovarian cancer patients. Three artificial intelligence algorithms, random survival forest, multitask logistic regression, and Cox survival regression, were used to develop a novel artificial intelligence survival prediction system. Results: The current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes between tumour samples and normal samples. Further univariate Cox regression identified 84 prognostic immune gene biomarkers for ovarian cancer patients in the model dataset (GSE32062 dataset and GSE53963 dataset). An immune regulatory network was constructed involving 63 immune genes and 5 transcription factors. Fourteen immune genes (PSMB9, FOXJ1, IFT57, MAL, ANXA4, CTSH, SCRN1, MIF, LTBR, CTSD, KIFAP3, PSMB8, HSPA5, and LTN1) were recognised as independent risk factors by multivariate Cox analyses. Kaplan-Meier survival curves showed that these 14 prognostic immune genes were closely related to the prognosis of ovarian cancer patients. A prognostic nomogram was developed by using these 14 prognostic immune genes. The concordance indexes were 0.760, 0.733, and 0.765 for 1-, 3-, and 5-year overall survival, respectively. This prognostic model could differentiate high-risk patients with poor overall survival from low-risk patients. According to three artificial intelligence algorithms, the current study developed an artificial intelligence survival predictive system that could provide three individual mortality risk curves for ovarian cancer. Conclusion: In conclusion, the current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes in ovarian cancer patients. Multivariate Cox analyses identified fourteen prognostic immune biomarkers for ovarian cancer. The current study constructed an immune regulatory network involving 63 immune genes and 5 transcription factors, revealing potential regulatory associations among immune genes and transcription factors. The current study developed a prognostic model to predict the prognosis of ovarian cancer patients. The current study further developed two artificial intelligence predictive tools for ovarian cancer, which are available at https://zhangzhiqiao8.shinyapps.io/Smart_Cancer_Survival_Predictive_System_17_OC_F1001/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_17_OC_F1001/. An artificial intelligence survival predictive system could help improve individualised treatment decision-making.
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Affiliation(s)
- Tingshan He
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Guangzhou, China
| | - Liwen Huang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Guangzhou, China
| | - Jing Li
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Guangzhou, China
| | - Peng Wang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Guangzhou, China
| | - Zhiqiao Zhang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Guangzhou, China
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14
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Lin J, Lu S, Jiang Z, Hu C, Zhang Z. Competing endogenous RNA network identifies mRNA biomarkers for overall survival of lung adenocarcinoma: two novel on-line precision medicine predictive tools. PeerJ 2021; 9:e11412. [PMID: 34012732 PMCID: PMC8109009 DOI: 10.7717/peerj.11412] [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: 09/08/2020] [Accepted: 04/15/2021] [Indexed: 12/09/2022] Open
Abstract
Background Individual mortality risk predicted curve at the individual level can provide valuable information for directing individual treatment decision. The present study attempted to explore potential post-transcriptional biological regulatory mechanism related with overall survival of lung adenocarcinoma (LUAD) patients through competitive endogenous RNA (ceRNA) network and develop two precision medicine predictive tools for predicting the individual mortality risk curves for overall survival of LUAD patients. Methods Multivariable Cox regression analyses were performed to explore the potential prognostic indicators, which were used to construct a prognostic model for overall survival of LUAD patients. Time-dependent receiver operating characteristic (ROC) curves were used to assess the predictive performance of prognostic model. Results There were 494 LUAD patients in model cohort and 233 LUAD patients in validation cohort. Differentially expressed mRNAs, miRNAs, and lncRNAs were identified between LUAD tissues and normal tissues. A ceRNA regulatory network was constructed on previous differentially expressed mRNAs, miRNAs, and lncRNAs. Fourteen mRNA biomarkers were identified as independent risk factors by multivariate Cox regression and used to develop a prognostic model for overall survival of LUAD patients. The C-indexes of prognostic model in model group were 0.786 (95% CI [0.744–0.828]), 0.736 (95% CI [0.694–0.778]) and 0.766 (95% CI [0.724–0.808]) for one year, two year and three year overall survival respectively. Two precision medicine predicted tools were developed for predicting individual mortality risk curves for LUAD patients. Conclusion The current study explored potential post-transcriptional biological regulatory mechanism and prognostic biomarkers for overall survival of LUAD patients. Two on-line precision medicine predictive tools were helpful to predict the individual mortality risk predicted curves for overall survival of LUAD patients. Smart Cancer Survival Predictive System could be used at https://zhangzhiqiao2.shinyapps.io/Smart_cancer_predictive_system_9_LUAD_E1002/.
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Affiliation(s)
- Jinsong Lin
- Department of Internal Medicine, The Affiliated Chencun Hospital of Shunde Hospital, Southern Medical University, Shunde, Guangdong, china
| | - Shubiao Lu
- Department of Internal Medicine, The Affiliated Chencun Hospital of Shunde Hospital, Southern Medical University, Shunde, Guangdong, china
| | - Zhijian Jiang
- Department of Internal Medicine, The Affiliated Chencun Hospital of Shunde Hospital, Southern Medical University, Shunde, Guangdong, china
| | - Chongjing Hu
- Department of Internal Medicine, The Affiliated Chencun Hospital of Shunde Hospital, Southern Medical University, Shunde, Guangdong, china
| | - Zhiqiao Zhang
- Department of Internal Medicine, The Affiliated Chencun Hospital of Shunde Hospital, Southern Medical University, Shunde, Guangdong, china
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15
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Zhang Z, He T, Huang L, Li J, Wang P. Immune gene prognostic signature for disease free survival of gastric cancer: Translational research of an artificial intelligence survival predictive system. Comput Struct Biotechnol J 2021; 19:2329-2346. [PMID: 34025929 PMCID: PMC8111455 DOI: 10.1016/j.csbj.2021.04.025] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 04/09/2021] [Accepted: 04/09/2021] [Indexed: 12/13/2022] Open
Abstract
The progress of artificial intelligence algorithms and massive data provide new ideas and choices for individual mortality risk prediction for cancer patients. The current research focused on depict immune gene related regulatory network and develop an artificial intelligence survival predictive system for disease free survival of gastric cancer. Multi-task logistic regression algorithm, Cox survival regression algorithm, and Random survival forest algorithm were used to develop the artificial intelligence survival predictive system. Nineteen transcription factors and seventy immune genes were identified to construct a transcription factor regulatory network of immune genes. Multivariate Cox regression identified fourteen immune genes as prognostic markers. These immune genes were used to construct a prognostic signature for gastric cancer. Concordance indexes were 0.800, 0.809, and 0.856 for 1-, 3- and 5- year survival. An interesting artificial intelligence survival predictive system was developed based on three artificial intelligence algorithms for gastric cancer. Gastric cancer patients with high risk score have poor survival than patients with low risk score. The current study constructed a transcription factor regulatory network and developed two artificial intelligence survival prediction tools for disease free survival of gastric cancer patients. These artificial intelligence survival prediction tools are helpful for individualized treatment decision.
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Key Words
- AJCC, the American Joint Committee on Cancer
- CI, confidence interval
- DCA, decision curve analysis
- DFS, disease free survival
- Disease free survival
- GC, gastric cancer
- GEO, the Gene Expression Omnibus
- Gastric cancer
- HR, hazard ratio
- Immune gene
- Prognostic signature
- ROC, receiver operating characteristic
- SD, standard deviation
- TCGA, The Cancer Genome Atlas
- Transcription factor
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Affiliation(s)
- Zhiqiao Zhang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, Guangdong, China
| | - Tingshan He
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, Guangdong, China
| | - Liwen Huang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, Guangdong, China
| | - Jing Li
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, Guangdong, China
| | - Peng Wang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, Guangdong, China
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16
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Ge JN, Yan D, Ge CL, Wei MJ. LncRNA C9orf139 can regulate the growth of pancreatic cancer by mediating the miR-663a/Sox12 axis. World J Gastrointest Oncol 2020; 12:1272-1287. [PMID: 33250960 PMCID: PMC7667452 DOI: 10.4251/wjgo.v12.i11.1272] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 09/24/2020] [Accepted: 10/12/2020] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Recent studies have proved the important role of many oncogenic long non-coding RNAs (lncRNAs) in the progression of pancreatic cancer, but little is known about the mechanisms of tumor suppression in pancreatic cancer. AIM To evaluate the function of tumor suppressor lncRNA C9orf139 in pancreatic cancer progression and to study the underlying mechanism. METHODS We assigned 54 patients with pancreatic ductal adenocarcinoma treated at our hospital to the patient group and 30 normal subjects undergoing physical examination to the control group. RT-qPCR was used to measure the relative expression of C9orf139 in the tissue and serum of patients, in an attempt to investigate the prognostic value of C9orf139 in pancreatic cancer patients. The luciferase reporter gene assay was performed to determine the interaction between C9orf139 and miR-663a. The biological function of C9orf139 was assessed by in vitro assays and in vivo subcutaneous tumor formation tests in animal models. To figure out the molecular mechanism of C9orf139 to act on miR-663a/Sox12, RNA pull-down, Western blot assay, RNA immunoprecipitation assay, and co-immunoprecipitation assay were performed. RESULTS C9orf139 level significantly increased in the tissue and serum of patients, which had clinical diagnostic value for pancreatic cancer. Patients with high C9orf139 expression had a higher risk of progressing to stage III + IV, lymph node metastasis, and poor differentiation. Cox regression analysis suggested that C9orf139, tumor-node-metastasis stage, and lymph node metastasis were independent prognostic factors in patients. The underlying mechanism of C9orf139 was that it promoted the growth of pancreatic cancer cells by modulating the miR-663a/Sox12 axis. CONCLUSION C9orf139 is highly expressed in pancreatic cancer, qualified to be used as a potential diagnostic and prognostic marker for pancreatic cancer. Its promotion of pancreatic cancer cell growth is achieved by mediating the miR-663a/Sox12 axis.
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Affiliation(s)
- Jin-Nian Ge
- Department of General Surgery, The First Affiliated Hospital of China Medical University, Shenyang 110001, Liaoning Province, China
| | - Di Yan
- Intensive Care Unit, The Central Affiliated Hospital of Shenyang Medical College, Shenyang 110024, Liaoning Province, China
| | - Chun-Lin Ge
- Department of General Surgery, The First Affiliated Hospital of China Medical University, Shenyang 110001, Liaoning Province, China
| | - Min-Jie Wei
- Department of Pharmacology, School of Pharmacy, China Medical University, Shenyang 110122, Liaoning Province, China
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17
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Glaß M, Dorn A, Hüttelmaier S, Haemmerle M, Gutschner T. Comprehensive Analysis of LincRNAs in Classical and Basal-Like Subtypes of Pancreatic Cancer. Cancers (Basel) 2020; 12:cancers12082077. [PMID: 32727085 PMCID: PMC7464731 DOI: 10.3390/cancers12082077] [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: 05/19/2020] [Revised: 07/09/2020] [Accepted: 07/23/2020] [Indexed: 02/07/2023] Open
Abstract
Pancreatic ductal adenocarcinomas (PDAC) belong to the deadliest malignancies in the western world. Mutations in TP53 and KRAS genes along with some other frequent polymorphisms occur almost universally and are major drivers of tumour initiation. However, these mutations cannot explain the heterogeneity in therapeutic responses and differences in overall survival observed in PDAC patients. Thus, recent classifications of PDAC tumour samples have leveraged transcriptome-wide gene expression data to account for epigenetic, transcriptional and post-transcriptional mechanisms that may contribute to this deadly disease. Intriguingly, long intervening RNAs (lincRNAs) are a special class of long non-coding RNAs (lncRNAs) that can control gene expression programs on multiple levels thereby contributing to cancer progression. However, their subtype-specific expression and function as well as molecular interactions in PDAC are not fully understood yet. In this study, we systematically investigated the expression of lincRNAs in pancreatic cancer and its molecular subtypes using publicly available data from large-scale studies. We identified 27 deregulated lincRNAs that showed a significant different expression pattern in PDAC subtypes suggesting context-dependent roles. We further analyzed these lincRNAs regarding their common expression patterns. Moreover, we inferred clues on their functions based on correlation analyses and predicted interactions with RNA-binding proteins, microRNAs, and mRNAs. In summary, we identified several PDAC-associated lincRNAs of prognostic relevance and potential context-dependent functions and molecular interactions. Hence, our study provides a valuable resource for future investigations to decipher the role of lincRNAs in pancreatic cancer.
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Affiliation(s)
- Markus Glaß
- Institute of Molecular Medicine, Section for Cell Biology, Medical Faculty, Martin-Luther University Halle-Wittenberg, 06120 Halle/Saale, Germany; (M.G.); (S.H.)
| | - Agnes Dorn
- Institute of Pathology, Section for Experimental Pathology, Medical Faculty, Martin-Luther University Halle-Wittenberg, 06120 Halle/Saale, Germany;
| | - Stefan Hüttelmaier
- Institute of Molecular Medicine, Section for Cell Biology, Medical Faculty, Martin-Luther University Halle-Wittenberg, 06120 Halle/Saale, Germany; (M.G.); (S.H.)
| | - Monika Haemmerle
- Institute of Pathology, Section for Experimental Pathology, Medical Faculty, Martin-Luther University Halle-Wittenberg, 06120 Halle/Saale, Germany;
- Correspondence: (M.H.); (T.G.)
| | - Tony Gutschner
- Junior Research Group ‘RNA Biology and Pathogenesis’, Medical Faculty, Martin-Luther University Halle-Wittenberg, 06120 Halle/Saale, Germany
- Correspondence: (M.H.); (T.G.)
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Zhang X, Zhang M, Zhang X, Zhu X, Wang J. A prognostic index based on a fourteen long non-coding RNA signature to predict the recurrence-free survival for muscle-invasive bladder cancer patients. BMC Med Inform Decis Mak 2020; 20:136. [PMID: 32646427 PMCID: PMC7346316 DOI: 10.1186/s12911-020-1115-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Background Bladder cancer (BC) is regarded as one of the most fatal cancer around the world. Nevertheless, there still lack of sufficient markers to predict the prognosis of BC patients. Herein, we aim to establish a prognosis predicting signature based on long-noncoding RNA (lncRNA) for the invasive BC patients. Methods The lncRNA expression profile was downloaded from The Cancer Genome Atlas (TCGA) database, along with the correlated clinicopathological information. The univariate Cox regression test was employed to screen out the recurrence-free survival (RFS)-related lncRNAs. Then, the LASSO method was conducted to construct the signature based on these RFS-related lncRNA candidates. Genes correlated with these fourteen lncRNAs were extracted from the mRNA expression profile, with the Pearson correlation coefficient > 0.60 or < − 0.40. Subsequently, the Proteomap pathway enrichment analyses were conducted to classify the function of these correlated genes. Furthermore, the multivariate analyses were executed to reveal the independent role of the proposed signature with the clinicopathological features. Results We established an lncRNA-based RFS predicting signature by the LASSO Cox regression test, and proved its usage and stability on both the training and validation cohorts by the Kaplan-Meier and receiver operating characteristic (ROC) curves. Notably, the multivariate Cox regression analysis found that our classifier was an independent indicator for muscle-invasive BC patients rather than sex, age and tumor grade, with higher predictive value than the existing ones. Besides, we did the pathway analyses for these genes that highly correlated with the proposed fourteen lncRNAs, as well as the differentially expressed genes (DEGs) derived from the high-risk vs. low-risk groups, and the recurrence vs. non-recurrence groups, respectively. Notably, these results were consistent, and these genes were mostly enriched in the transcription factors, G protein-coupled receptors, MAPK signaling pathways, which were proved significantly associated with tumor progression and drug resistance. Conclusions Our results suggested that the fourteen-lncRNA-based RFS predicting signature is an independent indicator for BC patients. Further prospective studies with more samples are needed to verify our findings.
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Affiliation(s)
- Xiaolong Zhang
- School of Computer Science and Technology, Shaanxi Engineering Research Center of Medical and Health Big Data, Xi'an Jiaotong University, Xi'an, China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,School of Medicine, Shenzhen University, Shenzhen, China
| | - Meng Zhang
- School of Medicine, Shenzhen University, Shenzhen, China.,Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xuanping Zhang
- School of Computer Science and Technology, Shaanxi Engineering Research Center of Medical and Health Big Data, Xi'an Jiaotong University, Xi'an, China
| | - Xiaoyan Zhu
- School of Computer Science and Technology, Shaanxi Engineering Research Center of Medical and Health Big Data, Xi'an Jiaotong University, Xi'an, China
| | - Jiayin Wang
- School of Computer Science and Technology, Shaanxi Engineering Research Center of Medical and Health Big Data, Xi'an Jiaotong University, Xi'an, China.
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19
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Li M, Li H, Chen Q, Wu W, Chen X, Ran L, Si G, Tan X. A Novel and Robust Long Noncoding RNA Panel to Predict the Prognosis of Pancreatic Cancer. DNA Cell Biol 2020; 39:1282-1289. [DOI: 10.1089/dna.2019.5241] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Affiliation(s)
- Mengying Li
- School of Health Sciences, Wuhan University, Wuhan, China
| | - Hang Li
- Department of Cardiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, China
| | - Qi Chen
- School of Health Sciences, Wuhan University, Wuhan, China
| | - Wenwen Wu
- School of Health Sciences, Wuhan University, Wuhan, China
| | - Xuyu Chen
- School of Health Sciences, Wuhan University, Wuhan, China
| | - Li Ran
- School of Health Sciences, Wuhan University, Wuhan, China
| | - Guanglin Si
- School of Health Sciences, Wuhan University, Wuhan, China
| | - Xiaodong Tan
- School of Health Sciences, Wuhan University, Wuhan, China
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20
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Wang Y, Yang Y, Wang Y, Li X, Xiao Y, Wang W. High Cancer Susceptibility Candidate 8 Expression Is Associated With Poor Prognosis of Pancreatic Adenocarcinoma: Validated Analysis Based on Four Cancer Databases. Front Cell Dev Biol 2020; 8:392. [PMID: 32582694 PMCID: PMC7287184 DOI: 10.3389/fcell.2020.00392] [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] [Received: 10/03/2019] [Accepted: 04/29/2020] [Indexed: 12/19/2022] Open
Abstract
Objective The aim of this study was to explore the association between the expression of a long non-coding RNA (lncRNA), cancer susceptibility candidate 8 (CASC8), and pancreatic adenocarcinoma (PAAD). Materials and Methods starBase database was used to perform differential expression, survival, and competing endogenous RNA (ceRNA) network and H19/miR-671 correlation analyses for CASC8 in 178 PAAD samples. Using the cBioPortal database website, we analyzed the alteration in CASC8 expression and its correlation with the overall survival in PAAD. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were also performed using the circlncRNAnet database. Analysis of CASC8 polymorphisms was performed using the UCSC Xena database. Finally, the expression of CASC8 in Chinese PAAD tissues was validated by qPCR. Results The expression of CASC8 was observed to be high in 178 PAAD samples [fold change = 8.71, P = 0.0014, false discovery rate (FDR) = 0.04] and was related with poor prognosis, but not in pancreatic neuroendocrine tumor (pNET). CASC8 amplification was noted in 6% of the PAAD patients; however, the gene amplification did not affect the expression of CASC8 but was involved with the overall survival time of PAAD patients. Network analysis indicated that H19 is the ceRNA pair of CASC8 and that CASC8 competitively binds to miR-671 and might participate in the process of epithelial-to-mesenchymal transition (EMT). The correlation analysis showed that CASC8 was significantly negatively correlated with SMAD7. The analysis of CASC8 polymorphism showed that high copy number segment (CNS) of CASC8 is associated with low survival. Validation using PAAD tissues from Chinese patients was consistent with the in silico findings. Conclusion CASC8 is specifically expressed at a high level in PAAD and associated with poor prognosis, which might be through its interaction with H19, miR-671, and SMAD7. These results indicate that CASC8 could serve as a novel marker for predicting the prognosis and as a potential target for the therapy of PAAD.
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Affiliation(s)
- Yingyi Wang
- Department of Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuemei Yang
- Molecular Pathology Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yanfeng Wang
- Department of Pathology, Heilongjiang Province Land Reclamation Headquarter General Hospital, Harbin, China
| | - Xiaoou Li
- Department of Pathology, Daxing Hospital Affiliated to Capital Medical University, Beijing, China
| | - Yu Xiao
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenze Wang
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Xu T, Lei T, Li SQ, Mai EH, Ding FH, Niu B. DNAH17-AS1 promotes pancreatic carcinoma by increasing PPME1 expression via inhibition of miR-432-5p. World J Gastroenterol 2020; 26:1745-1757. [PMID: 32351291 PMCID: PMC7183867 DOI: 10.3748/wjg.v26.i15.1745] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 02/07/2020] [Accepted: 03/19/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The incidence and mortality rates of pancreatic carcinoma (PC) are rapidly increasing worldwide. Long noncoding RNAs (lncRNAs) play critical roles during PC initiation and progression. Since the lncRNA DNAH17-AS1 is highly expressed in PC, the regulation of DNAH17-AS1 in PC was investigated in this study. AIM To investigate the expression and molecular action of lncRNA DNAH17-AS1 in PC cells. METHODS The PC expression data for the lncRNA DNAH17-AS1 was downloaded from The Cancer Genome Atlas database and used to examine its profile. Western blot and reverse transcription-quantitative PCR were employed to assess protein and mRNA expression. A subcellular fractionation assay was used to determine the location of DNAH17-AS1 in cells. In addition, the regulatory effects of DNAH17-AS1 on miR-432-5p, PPME1, and tumor activity were investigated using luciferase reporter assay, MTT viability analysis, flow cytometry, and transwell migration analysis. RESULTS DNAH17-AS1 was upregulated in PC cells and was associated with aggressive tumor behavior and poor prognosis for patients. Silencing DNAH17-AS1 promoted the apoptosis and reduced the viability, invasion, and migration of PC cells. In addition, DNAH17-AS1 served as a PC oncogene by downregulating miR-432-5p which normally directly targeted PPME1 to downregulate its expression. CONLUSION DNAH17-AS1 functions in PC as a tumor promoter by regulating the miR-432-5p/PPME1 axis. This finding may provide new insights for PC prognosis and therapy.
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Affiliation(s)
- Tao Xu
- Department of Hepatobiliary Surgery, Luoyang Central Hospital Affiliated to Zhengzhou University, Luoyang 471000, Henan Province, China
| | - Ting Lei
- Department of Hepatobiliary Surgery, Luoyang Central Hospital Affiliated to Zhengzhou University, Luoyang 471000, Henan Province, China
| | - Si-Qiao Li
- Department of Hepatobiliary Surgery, Luoyang Central Hospital Affiliated to Zhengzhou University, Luoyang 471000, Henan Province, China
| | - Er-Hui Mai
- Department of Hepatobiliary Surgery, Luoyang Central Hospital Affiliated to Zhengzhou University, Luoyang 471000, Henan Province, China
| | - Fei-Hu Ding
- Department of Hepatobiliary Surgery, Luoyang Central Hospital Affiliated to Zhengzhou University, Luoyang 471000, Henan Province, China
| | - Bin Niu
- Department of Gastrointestinal Surgery, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai 519000, Guangdong Province, China
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Zhang Z, Li J, He T, Ding J. Bioinformatics Identified 17 Immune Genes as Prognostic Biomarkers for Breast Cancer: Application Study Based on Artificial Intelligence Algorithms. Front Oncol 2020; 10:330. [PMID: 32296631 PMCID: PMC7137378 DOI: 10.3389/fonc.2020.00330] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 02/25/2020] [Indexed: 12/16/2022] Open
Abstract
An increasing body of evidence supports the association of immune genes with tumorigenesis and prognosis of breast cancer (BC). This research aims at exploring potential regulatory mechanisms and identifying immunogenic prognostic markers for BC, which were used to construct a prognostic signature for disease-free survival (DFS) of BC based on artificial intelligence algorithms. Differentially expressed immune genes were identified between normal tissues and tumor tissues. Univariate Cox regression identified potential prognostic immune genes. Thirty-four transcription factors and 34 immune genes were used to develop an immune regulatory network. The artificial intelligence survival prediction system was developed based on three artificial intelligence algorithms. Multivariate Cox analyses determined 17 immune genes (ADAMTS8, IFNG, XG, APOA5, SIAH2, C2CD2, STAR, CAMP, CDH19, NTSR1, PCDHA1, AMELX, FREM1, CLEC10A, CD1B, CD6, and LTA) as prognostic biomarkers for BC. A prognostic nomogram was constructed on these prognostic genes. Concordance indexes were 0.782, 0.734, and 0.735 for 1-, 3-, and 5- year DFS. The DFS in high-risk group was significantly worse than that in low-risk group. Artificial intelligence survival prediction system provided three individual mortality risk predictive curves based on three artificial intelligence algorithms. In conclusion, comprehensive bioinformatics identified 17 immune genes as potential prognostic biomarkers, which might be potential candidates of immunotherapy targets in BC patients. The current study depicted regulatory network between transcription factors and immune genes, which was helpful to deepen the understanding of immune regulatory mechanisms for BC cancer. Two artificial intelligence survival predictive systems are available at https://zhangzhiqiao7.shinyapps.io/Smart_Cancer_Survival_Predictive_System_16_BC_C1005/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_16_BC_C1005/. These novel artificial intelligence survival predictive systems will be helpful to improve individualized treatment decision-making.
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Affiliation(s)
- Zhiqiao Zhang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, China
| | - Jing Li
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, China
| | - Tingshan He
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, China
| | - Jianqiang Ding
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, China
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Rong MH, Zhu ZH, Guan Y, Li MW, Zheng JS, Huang YQ, Wei DM, Li YM, Wu XJ, Bu HP, Peng HL, Wei XL, Li GS, Li MX, Chen MH, Huang SN. Identification of prognostic splicing factors and exploration of their potential regulatory mechanisms in pancreatic adenocarcinoma. PeerJ 2020; 8:e8380. [PMID: 32095320 PMCID: PMC7020824 DOI: 10.7717/peerj.8380] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 12/10/2019] [Indexed: 12/24/2022] Open
Abstract
Pancreatic adenocarcinoma (PAAD), the most common subtype of pancreatic cancer, is a highly lethal disease. In this study, we integrated the expression profiles of splicing factors (SFs) of PAAD from RNA-sequencing data to provide a comprehensive view of the clinical significance of SFs. A prognostic index (PI) based on SFs was developed using the least absolute shrinkage and selection operator (LASSO) COX analysis. The PI exhibited excellent performance in predicting the status of overall survival of PAAD patients. We also used the percent spliced in (PSI) value obtained from SpliceSeq software to quantify different types of alternative splicing (AS). The prognostic value of AS events was explored using univariate COX and LASSO COX analyses; AS-based PIs were also proposed. The integration of prognosis-associated SFs and AS events suggested the potential regulatory mechanisms of splicing processes in PAAD. This study defined the markedly clinical significance of SFs and provided novel insight into their potential regulatory mechanisms.
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Affiliation(s)
- Min-Hua Rong
- Affiliated Cancer Hospital, Guangxi Medical University, Research Department, Nanning, Guangxi Zhuang Autonomous Region, P.R. China
| | - Zhan-Hui Zhu
- Affiliated Cancer Hospital, Guangxi Medical University, Research Department, Nanning, Guangxi Zhuang Autonomous Region, P.R. China
| | - Ying Guan
- Affiliated Cancer Hospital, Guangxi Medical University, Department of Radiotherapy, Nanning, Guangxi Zhuang Autonomous Region, P.R. China
| | - Mei-Wei Li
- Affiliated Cancer Hospital, Guangxi Medical University, Research Department, Nanning, Guangxi Zhuang Autonomous Region, P.R. China
| | - Jia-Shuo Zheng
- Affiliated Cancer Hospital, Guangxi Medical University, Research Department, Nanning, Guangxi Zhuang Autonomous Region, P.R. China
| | - Yue-Qi Huang
- Affiliated Cancer Hospital, Guangxi Medical University, Research Department, Nanning, Guangxi Zhuang Autonomous Region, P.R. China
| | - Dan-Ming Wei
- First Affiliated Hospital, Guangxi Medical University, Department of Pathology, Nanning, Guangxi Zhuang Autonomous Region, P.R. China
| | - Ying-Mei Li
- First Affiliated Hospital, Guangxi Medical University, Department of Pathology, Nanning, Guangxi Zhuang Autonomous Region, P.R. China
| | - Xiao-Ju Wu
- First Affiliated Hospital, Guangxi Medical University, Department of Pathology, Nanning, Guangxi Zhuang Autonomous Region, P.R. China
| | - Hui-Ping Bu
- Affiliated Cancer Hospital, Guangxi Medical University, Research Department, Nanning, Guangxi Zhuang Autonomous Region, P.R. China
| | - Hui-Liu Peng
- Affiliated Cancer Hospital, Guangxi Medical University, Research Department, Nanning, Guangxi Zhuang Autonomous Region, P.R. China
| | - Xiao-Lin Wei
- Affiliated Cancer Hospital, Guangxi Medical University, Research Department, Nanning, Guangxi Zhuang Autonomous Region, P.R. China
| | - Guo-Sheng Li
- Affiliated Cancer Hospital, Guangxi Medical University, Research Department, Nanning, Guangxi Zhuang Autonomous Region, P.R. China
| | - Ming-Xuan Li
- Affiliated Cancer Hospital, Guangxi Medical University, Research Department, Nanning, Guangxi Zhuang Autonomous Region, P.R. China
| | - Ming-Hui Chen
- Affiliated Cancer Hospital, Guangxi Medical University, Research Department, Nanning, Guangxi Zhuang Autonomous Region, P.R. China
| | - Su-Ning Huang
- Affiliated Cancer Hospital, Guangxi Medical University, Department of Radiotherapy, Nanning, Guangxi Zhuang Autonomous Region, P.R. China
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24
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Zhang Z, He T, Huang L, Ouyang Y, Li J, Huang Y, Wang P, Ding J. Two precision medicine predictive tools for six malignant solid tumors: from gene-based research to clinical application. J Transl Med 2019; 17:405. [PMID: 31796117 PMCID: PMC6891961 DOI: 10.1186/s12967-019-02151-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 11/19/2019] [Indexed: 12/24/2022] Open
Abstract
Background The current study aimed to construct competing endogenous RNA (ceRNA) regulation network and develop two precision medicine predictive tools for colorectal cancer (CRC). Methods Differentially expressed (DE) analyses were performed between CRC tissues and normal tissues. A ceRNA regulation network was constructed based on DElncRNAs, DEmiRNAs, and DEmRNAs. Results Fifteen mRNAs (ENDOU, MFN2, FASLG, SHOC2, VEGFA, ZFPM2, HOXC6, KLK10, DDIT4, LPGAT1, BEX4, DENND5B, PHF20L1, HSP90B1, and PSPC1) were identified as prognostic biomarkers for CRC by multivariate Cox regression. Then a Fifteen-mRNA signature was developed to predict overall survival for CRC patients. Concordance indexes were 0.817, 0.838, and 0.825 for 1-, 2- and 3-year overall survival. Patients with high risk scores have worse OS compared with patients with low risk scores. Conclusion The current study provided deeper understanding of prognosis-related ceRNA regulatory network for CRC. Two precision medicine predictive tools named Smart Cancer Survival Predictive System and Gene Survival Analysis Screen System were constructed for CRC. These two precision medicine predictive tools can provide valuable precious individual mortality risk prediction before surgery and improve the individualized treatment decision-making.
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Affiliation(s)
- Zhiqiao Zhang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, No. 1 Jiazi Road, Lunjiao, Shunde District, Foshan, 528308, Guangdong Province, China
| | - Tingshan He
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, No. 1 Jiazi Road, Lunjiao, Shunde District, Foshan, 528308, Guangdong Province, China
| | - Liwen Huang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, No. 1 Jiazi Road, Lunjiao, Shunde District, Foshan, 528308, Guangdong Province, China
| | - Yanling Ouyang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, No. 1 Jiazi Road, Lunjiao, Shunde District, Foshan, 528308, Guangdong Province, China
| | - Jing Li
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, No. 1 Jiazi Road, Lunjiao, Shunde District, Foshan, 528308, Guangdong Province, China
| | - Yiyan Huang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, No. 1 Jiazi Road, Lunjiao, Shunde District, Foshan, 528308, Guangdong Province, China
| | - Peng Wang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, No. 1 Jiazi Road, Lunjiao, Shunde District, Foshan, 528308, Guangdong Province, China
| | - Jianqiang Ding
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, No. 1 Jiazi Road, Lunjiao, Shunde District, Foshan, 528308, Guangdong Province, China.
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Ohandjo AQ, Liu Z, Dammer EB, Dill CD, Griffen TL, Carey KM, Hinton DE, Meller R, Lillard JW. Transcriptome Network Analysis Identifies CXCL13-CXCR5 Signaling Modules in the Prostate Tumor Immune Microenvironment. Sci Rep 2019; 9:14963. [PMID: 31628349 PMCID: PMC6802083 DOI: 10.1038/s41598-019-46491-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 06/26/2019] [Indexed: 12/12/2022] Open
Abstract
The tumor immune microenvironment (TIME) consists of multiple cell types that contribute to the heterogeneity and complexity of prostate cancer (PCa). In this study, we sought to understand the gene-expression signature of patients with primary prostate tumors by investigating the co-expression profiles of patient samples and their corresponding clinical outcomes, in particular “disease-free months” and “disease reoccurrence”. We tested the hypothesis that the CXCL13-CXCR5 axis is co-expressed with factors supporting TIME and PCa progression. Gene expression counts, with clinical attributes from PCa patients, were acquired from TCGA. Profiles of PCa patients were used to identify key drivers that influence or regulate CXCL13-CXCR5 signaling. Weighted gene co-expression network analysis (WGCNA) was applied to identify co-expression patterns among CXCL13-CXCR5, associated genes, and key genetic drivers within the CXCL13-CXCR5 signaling pathway. The processing of downloaded data files began with quality checks using NOISeq, followed by WGCNA. Our results confirmed the quality of the TCGA transcriptome data, identified 12 co-expression networks, and demonstrated that CXCL13, CXCR5 and associated genes are members of signaling networks (modules) associated with G protein coupled receptor (GPCR) responsiveness, invasion/migration, immune checkpoint, and innate immunity. We also identified top canonical pathways and upstream regulators associated with CXCL13-CXCR5 expression and function.
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Affiliation(s)
- Adaugo Q Ohandjo
- Department of Microbiology, Biochemistry & Immunology, Morehouse School of Medicine, Atlanta, GA, 30310, USA
| | - Zongzhi Liu
- R & D Bioinformatics, Sema4, Stamford, CT, 06902, USA
| | - Eric B Dammer
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Courtney D Dill
- Department of Microbiology, Biochemistry & Immunology, Morehouse School of Medicine, Atlanta, GA, 30310, USA
| | - Tiara L Griffen
- Department of Microbiology, Biochemistry & Immunology, Morehouse School of Medicine, Atlanta, GA, 30310, USA
| | - Kaylin M Carey
- Department of Microbiology, Biochemistry & Immunology, Morehouse School of Medicine, Atlanta, GA, 30310, USA
| | - Denise E Hinton
- Department of Microbiology, Biochemistry & Immunology, Morehouse School of Medicine, Atlanta, GA, 30310, USA
| | - Robert Meller
- Neuroscience Institute, Morehouse School of Medicine, Atlanta, GA, 30310, USA
| | - James W Lillard
- Department of Microbiology, Biochemistry & Immunology, Morehouse School of Medicine, Atlanta, GA, 30310, USA.
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Lian P, Wang Q, Zhao Y, Chen C, Sun X, Li H, Deng J, Zhang H, Ji Z, Zhang X, Huang Q. An eight-long non-coding RNA signature as a candidate prognostic biomarker for bladder cancer. Aging (Albany NY) 2019; 11:6930-6940. [PMID: 31479417 PMCID: PMC6756879 DOI: 10.18632/aging.102225] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 08/16/2019] [Indexed: 04/09/2023]
Abstract
UNLABELLED Backgroud: Bladder cancer (BLCA) is one of the most fatal types of cancer worldwide. However, there are limited methods for us to provide a prognostic prediction of BLCA patients. Therefore, we aimed at developing a lncRNA signature to improve the prognosis prediction of BLCA. RESULTS An eight-lncRNA signature was significantly associated with recurrence free survival in BLCA patients from both discovery and validation groups. Furthermore, genes involved in the signature were enriched in extracellular matrix organization pathway. Finally, functional experiments demonstrated that six out of the eight lncRNAs significantly regulated the invasion of BLCA cells. METHOD A total of 343 BLCA patients from The Cancer Genome Atlas (TCGA) were employed and randomly divided into training (n=172) and validating (n=171) groups. The lncRNA expression profiles of BLCA patients were screened and a risk-score formula were created and validated according to the Cox regression analysis. Next, WGCNA method was employed to cluster genes that highly correlated with the risk scores based on the profiling data of TCGA dataset and transwell assay was also performed to further investigate the role of these lncRNAs. CONCLUSIONS Our results suggested that the eight-lncRNA signature was a candidate prognostic biomarker for predicting tumor recurrence of patients with BLCA.
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Affiliation(s)
- Penghu Lian
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing 100730, China
| | - Qian Wang
- Department of General Surgery, Tangdu Hospital, Fourth Military Medical University, Xi’an 710032, China
| | - Ya Zhao
- Biomedicine Application Laboratory, School of Life Science and Technology, Xidian University, Xi’an 710038, China
| | - Cheng Chen
- State Key Laboratory of Cancer Biology and Experimental Teaching Center of Basic Medicine, Fourth Military Medical University, Xi’an 710032, China
| | - Xiacheng Sun
- State Key Laboratory of Cancer Biology and Experimental Teaching Center of Basic Medicine, Fourth Military Medical University, Xi’an 710032, China
| | - Hanzhong Li
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing 100730, China
| | - Jianhua Deng
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing 100730, China
| | - Hongmei Zhang
- Department of Clinical Oncology, Xijing Hospital, Fourth Military Medical University, Xi’an 710032, China
| | - Zhigang Ji
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing 100730, China
| | - Xuebin Zhang
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing 100730, China
| | - Qichao Huang
- State Key Laboratory of Cancer Biology and Experimental Teaching Center of Basic Medicine, Fourth Military Medical University, Xi’an 710032, China
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Zhang Z, Li J, He T, Ouyang Y, Huang Y, Liu Q, Wang P, Ding J. The competitive endogenous RNA regulatory network reveals potential prognostic biomarkers for overall survival in hepatocellular carcinoma. Cancer Sci 2019; 110:2905-2923. [PMID: 31335995 PMCID: PMC6726690 DOI: 10.1111/cas.14138] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 07/10/2019] [Accepted: 07/18/2019] [Indexed: 02/06/2023] Open
Abstract
The aim of the present study is to construct a competitive endogenous RNA (ceRNA) regulatory network by using differentially expressed long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs in patients with hepatocellular carcinoma (HCC), and to construct a prognostic model for predicting overall survival (OS) of HCC patients. Differentially expressed lncRNAs, miRNAs, and mRNAs were explored between HCC tissues and normal liver tissues. A prognostic model was built for predicting OS of HCC patients and receiver operating characteristic curves were used to evaluate the performance of the prognostic model. There were 455 differentially expressed lncRNAs, 181 differentially expressed miRNAs, and 5035 differentially expressed mRNAs. A ceRNA regulatory network was constructed based on 43 lncRNAs, 37 miRNAs, and 105 mRNAs. Eight mRNA biomarkers (H2AFX, SQSTM1, ITM2A, PFKP, TPD52L1, ACSL4, STRN3, and CPEB3) were identified as independent risk factors by multivariate Cox regression and were used to develop a prognostic model for OS. The C-indexes in the model group were 0.776 (95% confidence interval [CI], 0.730-0.822), 0.745 (95% CI, 0.699-0.791), and 0.789 (95% CI, 0.743-0.835) for 1-, 3-, and 5-year OS, respectively. The current study revealed potential molecular biological regulation pathways and prognostic biomarkers by the ceRNA regulatory network. A prognostic model based on prognostic mRNAs in the ceRNA network might be helpful to predict the individual mortality risk for HCC patients. The individual mortality risk calculator can be used by visiting the following URL: https://zhangzhiqiao.shinyapps.io/Smart_cancer_predictive_system_HCC/.
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Affiliation(s)
- Zhiqiao Zhang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, China
| | - Jing Li
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, China
| | - Tingshan He
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, China
| | - Yanling Ouyang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, China
| | - Yiyan Huang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, China
| | - Qingbo Liu
- Department of Hepatobiliary Surgery, Shunde Hospital, Southern Medical University, Shunde, China
| | - Peng Wang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, China
| | - Jianqiang Ding
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, China
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