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Cheng Z, Li L, Zhang Y, Ren Y, Gu J, Wang X, Zhao H, Lu H. HBV-infected hepatocellular carcinoma can be robustly classified into three clinically relevant subgroups by a novel analytical protocol. Brief Bioinform 2023; 24:7025463. [PMID: 36736372 DOI: 10.1093/bib/bbac601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 11/01/2022] [Accepted: 11/29/2022] [Indexed: 02/05/2023] Open
Abstract
Liver cancer is the third leading cause of cancer-related death worldwide, and hepatocellular carcinoma (HCC) accounts for a relatively large proportion of all primary liver malignancies. Among the several known risk factors, hepatitis B virus (HBV) infection is one of the important causes of HCC. In this study, we demonstrated that the HBV-infected HCC patients could be robustly classified into three clinically relevant subgroups, i.e. Cluster1, Cluster2 and Cluster3, based on consistent differentially expressed mRNAs and proteins, which showed better generalization. The proposed three subgroups showed different molecular characteristics, immune microenvironment and prognostic survival characteristics. The Cluster1 subgroup had near-normal levels of metabolism-related proteins, low proliferation activity and good immune infiltration, which were associated with its good liver function, smaller tumor size, good prognosis, low alpha-fetoprotein (AFP) levels and lower clinical stage. In contrast, the Cluster3 subgroup had the lowest levels of metabolism-related proteins, which corresponded with its severe liver dysfunction. Also, high proliferation activity and poor immune microenvironment in Cluster3 subgroup were associated with its poor prognosis, larger tumor size, high AFP levels, high incidence of tumor thrombus and higher clinical stage. The characteristics of the Cluster2 subgroup were between the Cluster1 and Cluster3 groups. In addition, MCM2-7, RFC2-5, MSH2, MSH6, SMC2, SMC4, NCPAG and TOP2A proteins were significantly upregulated in the Cluster3 subgroup. Meanwhile, abnormally high phosphorylation levels of these proteins were associated with high levels of DNA repair, telomere maintenance and proliferative features. Therefore, these proteins could be identified as potential diagnostic and prognostic markers. In general, our research provided a novel analytical protocol and insights for the robust classification, treatment and prevention of HBV-infected HCC.
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Affiliation(s)
- Zhiwei Cheng
- State Key Lab of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University
| | - Leijie Li
- State Key Lab of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University
| | - Yuening Zhang
- State Key Lab of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University
| | - Yongyong Ren
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University
| | - Jianlei Gu
- Department of Biostatistics, Yale University, New Haven, CT, United States
| | - Xinbo Wang
- State Key Lab of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, New Haven, CT, United States
| | - Hui Lu
- State Key Lab of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University
- Department of General Surgery, Xinhua Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Wang X, Zhang Z, Qin W, Liu S, Liu C, Genchev GZ, Hui L, Zhao H, Lu H. RePhine: An Integrative Method for Identification of Drug Response-related Transcriptional Regulators. GENOMICS PROTEOMICS & BIOINFORMATICS 2021; 19:534-548. [PMID: 33713851 PMCID: PMC9040019 DOI: 10.1016/j.gpb.2019.09.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 08/04/2019] [Accepted: 10/23/2019] [Indexed: 11/25/2022]
Abstract
Transcriptional regulators (TRs) participate in essential processes in cancer pathogenesis and are critical therapeutic targets. Identification of drug response-related TRs from cell line-based compound screening data is often challenging due to low mRNA abundance of TRs, protein modifications, and other confounders (CFs). In this study, we developed a regression-based pharmacogenomic and ChIP-seq data integration method (RePhine) to infer the impact of TRs on drug response through integrative analyses of pharmacogenomic and ChIP-seq data. RePhine was evaluated in simulation and pharmacogenomic data and was applied to pan-cancer datasets with the goal of biological discovery. In simulation data with added noises or CFs and in pharmacogenomic data, RePhine demonstrated an improved performance in comparison with three commonly used methods (including Pearson correlation analysis, logistic regression model, and gene set enrichment analysis). Utilizing RePhine and Cancer Cell Line Encyclopedia data, we observed that RePhine-derived TR signatures could effectively cluster drugs with different mechanisms of action. RePhine predicted that loss-of-function of EZH2/PRC2 reduces cancer cell sensitivity toward the BRAF inhibitor PLX4720. Experimental validation confirmed that pharmacological EZH2 inhibition increases the resistance of cancer cells to PLX4720 treatment. Our results support that RePhine is a useful tool for inferring drug response-related TRs and for potential therapeutic applications. The source code for RePhine is freely available at https://github.com/coexps/RePhine.
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Affiliation(s)
- Xujun Wang
- SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zhengtao Zhang
- State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Shanghai 200031, China
| | - Wenyi Qin
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA; Department of Genetics, School of Medicine, Yale University, New Haven, CT 06511, USA
| | - Shiyi Liu
- SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Cong Liu
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA; Department of Medical Informatics, Columbia University, New York, NY 10032, USA
| | - Georgi Z Genchev
- SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; Bulgarian Institute for Genomics and Precision Medicine, Sofia 1000, Bulgaria
| | - Lijian Hui
- State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Shanghai 200031, China
| | - Hongyu Zhao
- SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; Department of Biostatistics, Yale School of Public Health, New Haven, CT 06511, USA.
| | - Hui Lu
- SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA; Department of Biostatistics, Yale School of Public Health, New Haven, CT 06511, USA; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; Center for Biomedical informatics, Shanghai Children's Hospital, Shanghai 200040, China.
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3
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Xie Y, Wu L, Wang M, Cheng A, Yang Q, Wu Y, Jia R, Zhu D, Zhao X, Chen S, Liu M, Zhang S, Wang Y, Xu Z, Chen Z, Zhu L, Luo Q, Liu Y, Yu Y, Zhang L, Chen X. Alpha-Herpesvirus Thymidine Kinase Genes Mediate Viral Virulence and Are Potential Therapeutic Targets. Front Microbiol 2019; 10:941. [PMID: 31134006 PMCID: PMC6517553 DOI: 10.3389/fmicb.2019.00941] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Accepted: 04/12/2019] [Indexed: 12/20/2022] Open
Abstract
Alpha-herpesvirus thymidine kinase (TK) genes are virulence-related genes and are nonessential for viral replication; they are often preferred target genes for the construction of gene-deleted attenuated vaccines and genetically engineered vectors for inserting and expressing foreign genes. The enzymes encoded by TK genes are key kinases in the nucleoside salvage pathway and have significant substrate diversity, especially the herpes simplex virus 1 (HSV-1) TK enzyme, which phosphorylates four nucleosides and various nucleoside analogues. Hence, the HSV-1 TK gene is exploited for the treatment of viral infections, as a suicide gene in antitumor therapy, and even for the regulation of stem cell transplantation and treatment of parasitic infection. This review introduces the effects of α-herpesvirus TK genes on viral virulence and infection in the host and classifies and summarizes the current main application domains and potential uses of these genes. In particular, mechanisms of action, clinical limitations, and antiviral and antitumor therapy development strategies are discussed.
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Affiliation(s)
- Ying Xie
- Institute of Preventive Veterinary Medicine, Sichuan Agricultural University, Chengdu, China.,Key Laboratory of Animal Disease and Human Health of Sichuan Province, Sichuan Agricultural University, Chengdu, China.,Avian Disease Research Center, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, China
| | - Liping Wu
- Institute of Preventive Veterinary Medicine, Sichuan Agricultural University, Chengdu, China.,Key Laboratory of Animal Disease and Human Health of Sichuan Province, Sichuan Agricultural University, Chengdu, China.,Avian Disease Research Center, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, China
| | - Mingshu Wang
- Institute of Preventive Veterinary Medicine, Sichuan Agricultural University, Chengdu, China.,Key Laboratory of Animal Disease and Human Health of Sichuan Province, Sichuan Agricultural University, Chengdu, China.,Avian Disease Research Center, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, China
| | - Anchun Cheng
- Institute of Preventive Veterinary Medicine, Sichuan Agricultural University, Chengdu, China.,Key Laboratory of Animal Disease and Human Health of Sichuan Province, Sichuan Agricultural University, Chengdu, China.,Avian Disease Research Center, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, China
| | - Qiao Yang
- Institute of Preventive Veterinary Medicine, Sichuan Agricultural University, Chengdu, China.,Key Laboratory of Animal Disease and Human Health of Sichuan Province, Sichuan Agricultural University, Chengdu, China.,Avian Disease Research Center, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, China
| | - Ying Wu
- Institute of Preventive Veterinary Medicine, Sichuan Agricultural University, Chengdu, China.,Key Laboratory of Animal Disease and Human Health of Sichuan Province, Sichuan Agricultural University, Chengdu, China.,Avian Disease Research Center, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, China
| | - Renyong Jia
- Institute of Preventive Veterinary Medicine, Sichuan Agricultural University, Chengdu, China.,Key Laboratory of Animal Disease and Human Health of Sichuan Province, Sichuan Agricultural University, Chengdu, China.,Avian Disease Research Center, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, China
| | - Dekang Zhu
- Key Laboratory of Animal Disease and Human Health of Sichuan Province, Sichuan Agricultural University, Chengdu, China.,Avian Disease Research Center, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, China
| | - XinXin Zhao
- Institute of Preventive Veterinary Medicine, Sichuan Agricultural University, Chengdu, China.,Key Laboratory of Animal Disease and Human Health of Sichuan Province, Sichuan Agricultural University, Chengdu, China.,Avian Disease Research Center, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, China
| | - Shun Chen
- Institute of Preventive Veterinary Medicine, Sichuan Agricultural University, Chengdu, China.,Key Laboratory of Animal Disease and Human Health of Sichuan Province, Sichuan Agricultural University, Chengdu, China.,Avian Disease Research Center, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, China
| | - Mafeng Liu
- Institute of Preventive Veterinary Medicine, Sichuan Agricultural University, Chengdu, China.,Key Laboratory of Animal Disease and Human Health of Sichuan Province, Sichuan Agricultural University, Chengdu, China.,Avian Disease Research Center, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, China
| | - Shaqiu Zhang
- Institute of Preventive Veterinary Medicine, Sichuan Agricultural University, Chengdu, China.,Key Laboratory of Animal Disease and Human Health of Sichuan Province, Sichuan Agricultural University, Chengdu, China.,Avian Disease Research Center, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, China
| | - Yin Wang
- Key Laboratory of Animal Disease and Human Health of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Zhiwen Xu
- Key Laboratory of Animal Disease and Human Health of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Zhengli Chen
- Key Laboratory of Animal Disease and Human Health of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Ling Zhu
- Key Laboratory of Animal Disease and Human Health of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Qihui Luo
- Key Laboratory of Animal Disease and Human Health of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Yunya Liu
- Institute of Preventive Veterinary Medicine, Sichuan Agricultural University, Chengdu, China.,Key Laboratory of Animal Disease and Human Health of Sichuan Province, Sichuan Agricultural University, Chengdu, China.,Avian Disease Research Center, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, China
| | - Yanling Yu
- Institute of Preventive Veterinary Medicine, Sichuan Agricultural University, Chengdu, China.,Key Laboratory of Animal Disease and Human Health of Sichuan Province, Sichuan Agricultural University, Chengdu, China.,Avian Disease Research Center, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, China
| | - Ling Zhang
- Institute of Preventive Veterinary Medicine, Sichuan Agricultural University, Chengdu, China.,Key Laboratory of Animal Disease and Human Health of Sichuan Province, Sichuan Agricultural University, Chengdu, China.,Avian Disease Research Center, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, China
| | - Xiaoyue Chen
- Key Laboratory of Animal Disease and Human Health of Sichuan Province, Sichuan Agricultural University, Chengdu, China.,Avian Disease Research Center, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, China
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Qin W, Wang X, Zhao H, Lu H. A Novel Joint Gene Set Analysis Framework Improves Identification of Enriched Pathways in Cross Disease Transcriptomic Analysis. Front Genet 2019; 10:293. [PMID: 31031796 PMCID: PMC6473067 DOI: 10.3389/fgene.2019.00293] [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] [Received: 12/21/2018] [Accepted: 03/19/2019] [Indexed: 12/25/2022] Open
Abstract
Motivation: Gene set enrichment analysis is a widely accepted expression analysis tool which aims at detecting coordinated expression change within a pre-defined gene sets rather than individual genes. The benefit of gene set analysis over individual differentially expressed (DE) gene analysis includes more reproducible and interpretable results and detecting small but consistent change among gene set which could not be detected by DE gene analysis. There have been many successful gene set analysis applications in human diseases. However, when the sample size of a disease study is small and no other public data sets of the same disease are available, it will lead to lack of power to detect pathways of importance to the disease. Results: We have developed a novel joint gene set analysis statistical framework which aims at improving the power of identifying enriched gene sets through integrating multiple similar disease data sets. Through comprehensive simulation studies, we demonstrated that our proposed frameworks obtained much better AUC scores than single data set analysis and another meta-analysis method in identification of enriched pathways. When applied to two real data sets, the proposed framework could retain the enriched gene sets identified by single data set analysis and exclusively obtained up to 200% more disease-related gene sets demonstrating the improved identification power through information shared between similar diseases. We expect that the proposed framework would enable researchers to better explore public data sets when the sample size of their study is limited.
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Affiliation(s)
- Wenyi Qin
- Center for Biomedical Informatics, Shanghai Children's Hospital, Shanghai Jiaotong University, Shanghai, China
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States
- Department of Genetics, School of Medicine, Yale University, New Haven, CT, United States
| | - Xujun Wang
- Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiaotong University, Shanghai, China
| | - Hongyu Zhao
- Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiaotong University, Shanghai, China
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, United States
| | - Hui Lu
- Center for Biomedical Informatics, Shanghai Children's Hospital, Shanghai Jiaotong University, Shanghai, China
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States
- Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiaotong University, Shanghai, China
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, United States
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