[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-362027-105":3,"doc-detail-362027-en":81,"detail-sidebar-cat-0-en-105":99},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","building-simplified-cancer-subtyping-and-prediction-models-with-glycan-gene-signatures","Building simplified cancer subtyping and prediction models with glycan gene signatures","","Kai et al. develop precise glyco-gene signatures for cancer types and subtypes and use them to build classifier models that cluster tumors more accurately than approaches relying on much larger gene sets. A 71-glycosyltransferase panel distinguishes tumors from healthy tissue with 97% accuracy and clusters 27 cancers with 94% accuracy in external validation. Breast cancer subtyping shows about 2-fold improvements over PAM-50, and a glioma prognostic model uses only four GT genes.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/building-simplified-cancer-subtyping-and-prediction-models-with-glycan-gene-signatures/362027/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/building-simplified-cancer-subtyping-and-prediction-models-with-glycan-gene-signatures/362027.png","ImageObject",300,407,{"name":42,"@type":43},"Oliver","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-29","2026-09-23",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",6,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What is the main goal of this study?","Question",{"text":63,"@type":64},"To create simplified, validated cancer subtyping and prediction models using glycan gene signatures as diagnostic biomarkers.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How many glycosyltransferase genes are used in the proposed panel?",{"text":68,"@type":64},"The study identifies a gene panel comprising 71 glycosyltransferases that alter cancer-cell glycan patterns as tumors become more virulent.",{"name":70,"@type":61,"acceptedAnswer":71},"How does the approach perform in external testing and clinical subtyping?",{"text":72,"@type":64},"It distinguishes tumors from healthy tissue with 97% accuracy and clusters 27 cancers with 94% accuracy in external validation; breast cancer subtyping shows nearly 2-fold improvements over PAM-50 in independent testing.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},362027,1790421536,{"code":4,"msg":82,"data":83},"success",{"doc_id":79,"user_id":84,"nickname":42,"user_avatar":85,"doc_module":4,"category_id":86,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":87,"file_id":88,"file_url":89,"file_type":90,"file_size":91,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":92,"language":93,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":94,"faqs":95,"seo_title":96,"seo_description":12,"update_tm":97,"read_time":98},8796095461610,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Article  \nBuilding simplified cancer subtyping and prediction models with glycan gene signatures  \nGraphical abstract  \nHighlights  \n• We develop precise glyco-gene signatures of each cancer type and subtype  \nAuthors  \nJing Kai, Luyao Yang, Ayman F. AbuElela, ...,  \nDavid Gomez-Cabrero, Xin Gao, Jasmeen S. Merzaban  \nCorrespondence  \n[xin.gao@kaust.edu.sa](xin.gao@kaust.edu.sa) (X.G.),  \n[jasmeen.merzaban@kaust.edu.sa](jasmeen.merzaban@kaust.edu.sa) (J.S.M.)  \nIn brief  \nKai et al. discover each cancer’s unique glyco-gene signature and build aclassifier based on these biomarkers that clusters cancer types and subtypes more accurately than models with much larger gene sets. Relying on 71 genes directly linked to metastasis, the approach reveals the untapped potential of glycanbased diagnostics.  \n• Classifiers built on 71 glyco-genes outperform algorithms with larger gene sets  \n• Cancer-type classifier identifies 27 cancers with 94% accuracy in external testing  \n• Breast cancer subtyping shows 2-fold accuracy gains over clinical standard PAM-50  \nKai et al., 2025, Cell Reports Methods 5, 101140  \nAugust 18, 2025 © 2025 The Author(s) . Published by Elsevier Inc.  \n[https://doi.org/10.1016/j.crmeth.2025.101140](https://doi.org/10.1016/j.crmeth.2025.101140)  \nll  \nll  \nOPEN ACCESS  \nArticle  \nBuilding simplified cancer subtyping and prediction models  \nwith glycan gene signatures  \nJing Kai,1,7 Luyao Yang,2,7 Ayman F. AbuElela,1,8 Alyaa M. Abdel-Haleem,2,8 Asma S. AlAmoodi,1 Abdulghani A. Bin Nafisah,3 Alfadel Alshaibani,4 Ali S. Alzahrani,3 Vincenzo Lagani,1,6 David Gomez-Cabrero,1 Xin Gao,2,5,* and Jasmeen S. Merzaban1,5,9,*  \n1Bioscience Program, King Abdullah University of Science and Technology (KAUST), Biological and Environmental Sciences and Engineering (BESE) Division, Thuwal 23955-6900, Saudi Arabia  \n2Computer Science Program, King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Centre (CBRC), Thuwal 23955-6900, Saudi Arabia  \n3Department of Medicine and Department of Molecular Oncology, King Faisal Specialist Hospital & Research Centre, Riyadh 11211, Saudi Arabia  \n4Department of Hematology, SCT and Cellular Therapy, King Faisal Specialist Hospital & Research Centre, Riyadh 11211, Saudi Arabia  \n5KAUST Center of Excellence for Smart Health (KCSH), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia  \n6Institute of Chemical Biology, Ilia State University, Tbilisi 0179, Georgia  \n7These authors contributed equally  \n8These authors contributed equally  \n9Lead contact  \n*[Correspondence:](Correspondence: xin.gao@kaust.edu.sa)[ xin.gao@kaust.edu.sa](Correspondence: xin.gao@kaust.edu.sa) (X.G.), [jasmeen.merzaban@kaust.edu.sa](jasmeen.merzaban@kaust.edu.sa) (J.S. M.) [https://doi.org/10.1016/j.crmeth.2025.101](https://doi.org/10.1016/j.crmeth.2025.101)140  \nMOTIVATION Decades of research have established that glycan patterns on cancer cells change in characteristic ways as the disease worsens, yet the potential for using glyco-genes as biomarkers for pan-cancer detection is largely untapped. A significant challenge is the lack of simple, validated diagnostic tools leveraging these changes. Current approaches often require large gene sets or lack robust external validation, hindering their clinical translation for streamlined cancer diagnosis, typing, and subtyping.  \nSUMMARY  \nWe identified a gene panel comprising 71 glycosyltransferases (GTs) that alter glycan patterns on cancer cells as they become more virulent. When these cancer-pattern GTs (CPGTs) were run through an algorithm trained on The Cancer Genome Atlas, they differentiated tumors from healthy tissue with 97% accuracy and clustered 27 cancers with 94% accuracy in external validation, revealing each variety’s ‘‘biometric glycan ID.’’ Using machine learning, we built four models for cancer classification, including two for detecting the molecular subtypes of breast cancer and gliom","cbCaieFIJvrXJqgX","https://ap.wps.com/l/cbCaieFIJvrXJqgX","pdf",6023174,21,"English","# Highlights\n## Motivation\n## Summary\n## Introduction","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"To create simplified, validated cancer subtyping and prediction models using glycan gene signatures as diagnostic biomarkers.\"},{\"question\":\"How many glycosyltransferase genes are used in the proposed panel?\",\"answer\":\"The study identifies a gene panel comprising 71 glycosyltransferases that alter cancer-cell glycan patterns as tumors become more virulent.\"},{\"question\":\"How does the approach perform in external testing and clinical subtyping?\",\"answer\":\"It distinguishes tumors from healthy tissue with 97% accuracy and clusters 27 cancers with 94% accuracy in external validation; breast cancer subtyping shows nearly 2-fold improvements over PAM-50 in independent testing.\"}]","Building simplified cancer subtyping and prediction models with glycan gene signatures | PDF",1790146293,53,{"code":4,"msg":82,"data":100},[101,105,109,113,118,122,125,130,135,138,142],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":106,"show_sort_weight":107,"slug":108},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":110,"show_sort_weight":111,"slug":112},"Exam",70,"exam",{"id":114,"doc_module":4,"doc_module_name":25,"category_name":115,"show_sort_weight":116,"slug":117},5,"Comic",60,"comic",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":119,"show_sort_weight":120,"slug":121},"Technology",50,"technology",{"id":86,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":123,"slug":124},40,"healthcare",{"id":126,"doc_module":4,"doc_module_name":25,"category_name":127,"show_sort_weight":128,"slug":129},8,"Research & Report",30,"research-report",{"id":131,"doc_module":4,"doc_module_name":25,"category_name":132,"show_sort_weight":133,"slug":134},9,"Religion & Spirituality",20,"religion-spirituality",{"id":133,"doc_module":4,"doc_module_name":25,"category_name":136,"show_sort_weight":133,"slug":137},"World Cup","world-cup",{"id":139,"doc_module":4,"doc_module_name":25,"category_name":140,"show_sort_weight":139,"slug":141},10,"Lifestyle","lifestyle",{"id":143,"doc_module":4,"doc_module_name":25,"category_name":144,"show_sort_weight":114,"slug":145},19,"General","general"]