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The study develops a new HCC prognostic model based on TCRs using TCGA-LIHC and ICGC-LIRI-JP datasets. Differentially expressed TCRs and prognostic genes were derived via Cox regression, then evaluated with Kaplan–Meier and ROC analyses. Multivariate Cox modeling produced a risk model and nomogram, followed by immune, drug-sensitivity, clustering, and pathway enrichment investigations.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/prognostic-modeling-of-hepatocellular-carcinoma-based-on-t-cell-proliferation-regulators-a-bioinformatics-approach/329197/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/prognostic-modeling-of-hepatocellular-carcinoma-based-on-t-cell-proliferation-regulators-a-bioinformatics-approach/329197.png","ImageObject",300,407,{"name":92,"@type":93},"Sage","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What datasets were used to build the prognostic model?","Question",{"text":112,"@type":113},"The model was developed using TCGA-LIHC and ICGC-LIRI-JP datasets together with TCR information.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were prognostic genes and the risk model determined?",{"text":117,"@type":113},"Differentially expressed TCRs were identified by intersecting TCRs with differentially expressed genes, then prognostic genes were selected using Cox regression to construct a risk model.",{"name":119,"@type":110,"acceptedAnswer":120},"What major findings link the model to immune characteristics and survival?",{"text":121,"@type":113},"Six genes among differentially expressed TCRs predicted HCC prognosis; immune analyses showed differential expression of immune checkpoints between risk groups, with lower immunotherapy response in the high-risk group.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},329197,1790164448,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":36,"preview_page_count":4},687197207057,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","TYPE Original Research PUBLISHED 09 October 2024  \nDOI 10.3389/fimmu.2024.1444091  \nOPEN ACCESS  \nEDITED BY  \nWenlin Yang,  \nUniversity of Florida, United States  \nREVIEWED BY  \nYingze Wei,  \nNantong Tumor Hospital, China Liu Danyang,  \nFudan University, China  \n*CORRESPONDENCE  \nXiang-Chun Ding  \n [13619511768@163.com](13619511768@163.com)[ ](13619511768@163.com)Li-Na Ma  \n [13619511758@163.com](13619511758@163.com)  \n†These authors have contributed equally to this work  \nRECEIVED 05 June 2024  \nACCEPTED 23 September 2024  \nPUBLISHED 09 October 2024  \nCITATION  \nHai L, Bai X-Y, Luo X, Liu S-W,  \nMa Z-M, Ma L-N and Ding X-C (2024) Prognostic modeling of hepatocellular carcinoma based on T-cell proliferation regulators: a bioinformatics approach.  \nFront. Immunol. 15:1444091 .  \ndoi: 10.3389/fimmu.2024.1444091  \nCOPYRIGHT  \n© 2024 Hai, Bai, Luo, Liu, Ma, Ma and Ding. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nPrognostic modeling of hepatocellular carcinoma based on T-cell proliferation regulators: a bioinformatics approach  \nLong Hai 1†, Xiao-Yang Bai 1†, Xia Luo 1,2, Shuai-Wei Liu 1,2, Zi-Min Ma 3, Li-Na Ma 1,2* and Xiang-Chun Ding 1,2,4*  \n1 Department of Infectious Disease, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China, 2 Infectious Disease Clinical Research Center of Ningxia, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China, 3Weiluo Microbial Pathogens Monitoring Technology Co., Ltd. of Beijing, Beijing, China, 4 Department of Tropical Disease & Infectious Disease, The Second Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China  \nBackground: The prognostic value and immune significance of T-cell proliferation regulators (TCRs) in hepatocellular carcinoma (HCC) have not been previously reported. This study aimed to develop a new prognostic model based on TCRs in patients with HCC.  \nMethod: This study used The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) and International Cancer Genome Consortium-Liver Cancer-Riken, Japan (ICGC-LIRI-JP) datasets along with TCRs. Differentially expressed TCRs (DE-TCRs) were identified by intersecting TCRs and differentially expressed genes between HCC and non-cancerous samples. Prognostic genes were determined using Cox regression analysis and were used to construct a risk model for HCC. Kaplan-Meier survival analysis was performed to assess the difference in survival between high-risk and low-risk groups. Receiver operating characteristic curve was used to assess the validity of risk model, as well as for testing in the ICGC-LIRI-JP dataset. Additionally, independent prognostic factors were identified using multivariate Cox regression analysis and proportional hazards assumption, and they were used to construct a nomogram model. TCGA-LIHC dataset was subjected to tumor microenvironment analysis, drug sensitivity analysis, gene set variation analysis, and immune correlation analysis. The prognostic genes were analyzed using consensus clustering analysis, mutation analysis, copy number variation analysis, gene set enrichment analysis, and molecular prediction analysis.  \nResults: Among the 18 DE-TCRs, six genes (DCLRE1B, RAN, HOMER1, ADA, CDK1, and IL1RN) could predict the prognosis of HCC. A risk model that can accurately predict HCC prognosis was established based on these genes. An efficient nomogram model was also developed using clinical traits and risk scores . Immune-related analyses revealed that 39 immune checkpoints exhibited differential expression between the high-risk and low-risk groups. The rate ","cbCailwwKbYLRASa","https://ap.wps.com/l/cbCailwwKbYLRASa","pdf",5993902,16,"English","# Introduction\n# Method\n# Results\n# Conclusions","[{\"question\":\"What datasets were used to build the prognostic model?\",\"answer\":\"The model was developed using TCGA-LIHC and ICGC-LIRI-JP datasets together with TCR information.\"},{\"question\":\"How were prognostic genes and the risk model determined?\",\"answer\":\"Differentially expressed TCRs were identified by intersecting TCRs with differentially expressed genes, then prognostic genes were selected using Cox regression to construct a risk model.\"},{\"question\":\"What major findings link the model to immune characteristics and survival?\",\"answer\":\"Six genes among differentially expressed TCRs predicted HCC prognosis; immune analyses showed differential expression of immune checkpoints between risk groups, with lower immunotherapy response in the high-risk group.\"}]","Prognostic modeling of hepatocellular carcinoma based on T-cell proliferation regulators - a bioinformatics approach | PDF",1789983036]