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By integrating tumor transcriptome profiles with patient prognoses across cancer types, the study identifies dysregulated, prognosis-associated genes and organizes them in a cancer type-dependent manner. Using expression matrices from these genes, models quantitatively evaluate tumor malignant levels across cancers, supporting improved survival prediction beyond conventional staging. A hepatocellular carcinoma transcriptome-based molecular subtyping reveals three subtypes with distinct outcomes, mutation landscapes, immune microenvironments, and dysregulated pathways, enabling low-cost clinical implementation.",{"@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/generalizable-transcriptome-based-tumor-malignant-level-evaluation-and-molecular-subtyping-towards-precision-oncology-journal-article-abstract/330739/",{"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/generalizable-transcriptome-based-tumor-malignant-level-evaluation-and-molecular-subtyping-towards-precision-oncology-journal-article-abstract/330739.png","ImageObject",300,407,{"name":92,"@type":93},"Hazel","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How does the study generate malignant level evaluation across cancers?","Question",{"text":112,"@type":113},"It integrates transcriptome-derived expression matrices of dysregulated, prognosis-associated genes into quantitative models that evaluate tumor malignant levels across cancer types.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What does the transcriptome-based molecular subtyping in hepatocellular carcinoma reveal?",{"text":117,"@type":113},"It identifies three molecular subtypes with significantly different clinical outcomes, along with distinct mutation landscapes, immune microenvironments, and dysregulated pathways.",{"name":119,"@type":110,"acceptedAnswer":120},"Why is transcriptome profiling considered practical for clinical promotion in this work?",{"text":121,"@type":113},"Because tumor transcriptome is commonly profiled in clinical practice with low experimental complexity and cost, the proposed approaches are positioned for real-world implementation.","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},330739,1790197798,{"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":44,"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":145,"preview_page_count":4},137441390410,"https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984","Hu etal. Journal of Translational Medicine (2024) 22:512  \n[https://doi.org/10.1186/s12967-024-05326-0](https://doi.org/10.1186/s12967-024-05326-0)  \nJournal of Translational Medicine  \nMETHODOLOGY Open Access  \nGeneralizable transcriptome-based tumor malignant level evaluation and molecular subtyping towards precision oncology  \nDingxue Hu 1,7†, Ziteng Zhang2†, Xiaoyi Liu 1†, Youchun Wu2†, Yunyun An 1, Wanqiu Wang 1,7, Mengqi Yang 1, Yuqi Pan 1,3, Kun Qiao4, Changzheng Du 1,5,9, Yu Zhao6, Yan Li3,10, Jianqiang Bao7, Tao Qin8, Yue Pan8, Zhaohua Xia4*, Xin Zhao2* and Kun Sun 1*  \nAbstract  \nIn cancer treatment, therapeutic strategies that integrate tumor-specific characteristics (i. e., precision oncology) are widely implemented to provide clinical benefits for cancer patients. Here, through in-depth integration of tumor transcriptome and patients’ prognoses across cancers, we investigated dysregulated and prognosis-associated genes and catalogued such important genes in a cancer type-dependent manner. Utilizing the expression matrices of these genes, we built models to quantitatively evaluate the malignant levels of tumors across cancers, which could add value to the clinical staging system for improved prediction of patients’ survival. Furthermore, we performed a transcriptome-based molecular subtyping on hepatocellular carcinoma, which revealed three subtypes with significantly diversified clinical outcomes, mutation landscapes, immune microenvironment, and dysregulated pathways. As tumor transcriptome was commonly profiled in clinical practice with low experimental complexity and cost, this work proposed easy-to-perform approaches for practical clinical promotion towards better healthcare and precision oncology of cancer patients.  \nKeywords Oncogene, Tumor suppressor, Survival analysis, Hepatocellular carcinoma  \n†Dingxue Hu, Ziteng Zhang, Xiaoyi Liu and Youchun Wu contributed equally to this work.  \n*Correspondence: Zhaohua Xia  \ndaoren_0001 [13@126.com](13@126.com)  \nXin Zhao [drzhaoxin@126.com](drzhaoxin@126.com)[ ](drzhaoxin@126.com)Kun Sun [sunkun@szbl.ac.cn](sunkun@szbl.ac.cn)  \n1Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen 518132, China  \n2Hepato-Biliary Surgery Division, The Second Affiliated Hospital, Shenzhen Third People’s Hospital, Southern University of Science and Technology, Shenzhen 518100, China  \n3Department of Biology, Southern University of Science and Technology, Shenzhen 518055, China  \n4Thoracic Surgical Department, Shenzhen Third People’s Hospital, The Second Affiliated Hospital, Southern University of Science and Technology, Shenzhen 518100, China  \n5Department of Biochemistry, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China  \n6Molecular Cancer Research Center, School of Medicine, Shenzhen Campus of SunYat-sen University, SunYat-sen University,  \nShenzhen 518107, China  \n7Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China  \n8Guangdong Provincial Key Laboratory of Malignant Tumor Epigeneticsand Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, SunYat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China  \n9Beijing Tsinghua Changgung Hospital, Tsinghua University School of Medicine, Beijing 102218, China  \n10Integrative Microecology Clinical Center, Shenzhen Key Laboratory of Gastrointestinal Microbiota and Disease, Shenzhen Clinical Research Center for Digestive Disease, Shenzhen Technology Research Center of Gut Microbiota Transplantation, Shenzhen Hospital, Southern Medical University, Shenzhen 510086, China  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link t","cbCaiuAUAgy3fOSX","https://ap.wps.com/l/cbCaiuAUAgy3fOSX","pdf",5567540,15,"English","# Abstract\n# Introduction","[{\"question\":\"How does the study generate malignant level evaluation across cancers?\",\"answer\":\"It integrates transcriptome-derived expression matrices of dysregulated, prognosis-associated genes into quantitative models that evaluate tumor malignant levels across cancer types.\"},{\"question\":\"What does the transcriptome-based molecular subtyping in hepatocellular carcinoma reveal?\",\"answer\":\"It identifies three molecular subtypes with significantly different clinical outcomes, along with distinct mutation landscapes, immune microenvironments, and dysregulated pathways.\"},{\"question\":\"Why is transcriptome profiling considered practical for clinical promotion in this work?\",\"answer\":\"Because tumor transcriptome is commonly profiled in clinical practice with low experimental complexity and cost, the proposed approaches are positioned for real-world implementation.\"}]","Generalizable transcriptome-based tumor malignant level evaluation and molecular subtyping towards precision oncology - Journal article abstract | PDF",1789989893,38]