[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-329729-105":59,"doc-detail-329729-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","the-human-pathology-atlas-for-deciphering-the-prognostic-features-of-human-cancers","The Human Pathology Atlas for deciphering the prognostic features of human cancers","","Cancer remains a leading cause of death worldwide, and the study develops a stronger molecular basis for personalized oncology. The updated analysis links gene expression patterns to patient survival by integrating 6918 patients across 21 cancer types from 10 independent cohorts. A systems biology workflow identifies prognostic genes and constructs regulatory networks for KIRC and LIHC, revealing tumor-type specific survival associations. Findings support the Human Pathology Atlas as a foundation for precision diagnostics and targeted treatment strategies.",{"@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":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/the-human-pathology-atlas-for-deciphering-the-prognostic-features-of-human-cancers/329729/",{"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/the-human-pathology-atlas-for-deciphering-the-prognostic-features-of-human-cancers/329729.png","ImageObject",300,407,{"name":92,"@type":93},"Theodora","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main goal of the updated Human Pathology Atlas study?","Question",{"text":112,"@type":113},"To establish a robust association between gene expression and patient survival outcomes, improving the atlas’ usefulness for cancer research and precision oncology.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were the prognostic genes and survival associations derived?",{"text":117,"@type":113},"The study integrated gene expression profiles from 6918 patients across 21 cancer types using data from 10 independent cancer cohorts, then applied systems biology methods to identify prognostic genes and networks.",{"name":119,"@type":110,"acceptedAnswer":120},"Which cancer types were used to construct prognostic regulatory networks?",{"text":121,"@type":113},"Kidney renal clear cell carcinoma (KIRC) and liver hepatocellular carcinoma (LIHC) were used to build prognostic regulatory networks tied to patient survival.","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},329729,1790129570,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"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},687197207919,"https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552","Articles   \nThe Human Pathology Atlas for deciphering the prognostic features of human cancers  \nMeng Yuan,a Cheng Zhang,a Kalle Von Feilitzen,a Martin Zwahlen,a Mengnan Shi,a Xiangyu Li,b Hong Yang,a Xiya Song,a Hasan Turkez,c Mathias Uhlén,a and Adil Mardinoglua,d,∗  \naScience for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden bGuangzhou National Laboratory, Guangzhou, Guangdong Province 510005, China cDepartment of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum, Turkey  \ndCentre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London SE1 9RT, UK  \nSummary  \nBackground Cancer is one of the leading causes of mortality worldwide, highlighting the urgent need for a deeper molecular understanding and the development of personalized treatments. The present study aims to establish a solid association between gene expression and patient survival outcomes to enhance the utility of the Human Pathology Atlas for cancer research.  \nMethods In this updated analysis, we examined the expression profiles of 6918 patients across 21 cancer types. We integrated data from 10 independent cancer cohorts, creating a cross-validated, reliable collection of prognostic genes. We applied systems biology approach to identify the association between gene expression profiles and patient survival outcomes. We further constructed prognostic regulatory networks for kidney renal clear cell carcinoma (KIRC) and liver hepatocellular carcinoma (LIHC), which elucidate the molecular underpinnings associated with patient survival in these cancers.  \nFindings We observed that gene expression during the transition from normal to tumorous tissue exhibited diverse shifting patterns in their original tissue locations. Significant correlations between gene expression and patient survival outcomes were identified in KIRC and LIHC among the major cancer types. Additionally, the prognostic regulatory network established for these two cancers showed the indicative capabilities of the Human Pathology Atlas and provides actionable insights for cancer research.  \nInterpretation The updated Human Pathology Atlas provides a significant foundation for precision oncology and the formulation of personalized treatment strategies. These findings deepen our understanding of cancer biology and have the potential to advance targeted therapeutic approaches in clinical practice.  \nFunding The Knut and Alice Wallenberg Foundation (72110), the China Scholarship Council (Grant No. 202006940003) .  \nCopyright © 2024 The Author(s) . Published by Elsevier B.V. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)).  \nKeywords: Cancer; Transcriptomics; Survival; Systems biology  \nIntroduction  \nCancer remains a significant global health challenge, with recent estimates indicating approximately 19.3 million new cases and almost 10 million deaths annually.1 In Europe, breast, colorectal, lung, and prostate cancers are the most frequently diagnosed cancers, collectively representing over half of all cases.2 Of particular concern is the high rate of premature mortality associated with cancer, imposing substantial societal and economic burdens.3 Extensive efforts have been  \ninvested in cancer research to develop effective treatment options and improve prognostic outcomes. However, universally effective and resilient treatments remain limited due to the heterogeneity of cancer.4–7 This highlights the urgent need for a deeper understanding of the molecular mechanisms driving cancer pathogenesis and for the development of more effective, targeted and personalized treatment strategies. Cancer research has experienced significant evolution with advancements in computational power and the emergence  \neBioMedicine 2025;111: 105495 Published Online xxx [https://doi.org/10](https://doi.org/10) . 1016/j.ebiom.2024 .","cbCaiivRj6ze4IPv","https://ap.wps.com/l/cbCaiivRj6ze4IPv","pdf",3499036,16,"English","# Summary\n## Background\n## Methods\n## Findings\n## Interpretation\n## Funding\n# Research in context\n## Evidence before this study\n## Added value of this study\n## Implications of all the available evidence","[{\"question\":\"What is the main goal of the updated Human Pathology Atlas study?\",\"answer\":\"To establish a robust association between gene expression and patient survival outcomes, improving the atlas’ usefulness for cancer research and precision oncology.\"},{\"question\":\"How were the prognostic genes and survival associations derived?\",\"answer\":\"The study integrated gene expression profiles from 6918 patients across 21 cancer types using data from 10 independent cancer cohorts, then applied systems biology methods to identify prognostic genes and networks.\"},{\"question\":\"Which cancer types were used to construct prognostic regulatory networks?\",\"answer\":\"Kidney renal clear cell carcinoma (KIRC) and liver hepatocellular carcinoma (LIHC) were used to build prognostic regulatory networks tied to patient survival.\"}]","The Human Pathology Atlas for deciphering the prognostic features of human cancers | PDF",1789985387]