[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-450298-105":3,"doc-detail-450298-en":80,"detail-sidebar-cat-0-en-105":98},{"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":73,"head_meta":75,"extra_data":77,"updated_unix":79},105,"en","ai-driven-multi-omics-integration-of-cancer-associated-fibroblasts-for-prognostic-modeling-and-therapeutic-target-discovery-in-head-and-neck-squamous-cell-carcinoma","AI-driven multi-omics integration of cancer-associated fibroblasts for prognostic modeling and therapeutic target discovery in head and neck squamous cell carcinoma","","AI-driven multi-omics integration is used to clarify cancer-associated fibroblast (CAF) heterogeneity mechanisms in head and neck squamous cell carcinoma (HNSCC) progression and therapy response. Bulk transcriptomic GEO data are intersected with curated CAF gene sets to derive CAF-related differentially expressed genes. A machine-learning LASSO-Cox model based on the TCGA-HNSCC cohort generates a fibroblast-associated prognostic signature, validated by Kaplan–Meier, time-dependent ROC, nomogram, DCA, and calibration. Mechanistic profiling includes immune infiltration, checkpoint correlations, single-cell mapping, TMB, MSI, and DNA methylation, while drug vulnerabilities are explored via cMAP and docking, identifying Epothilone B targeting HBEGF.",{"@graph":14,"@context":72},[15,34,55],{"@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/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/ai-driven-multi-omics-integration-of-cancer-associated-fibroblasts-for-prognostic-modeling-and-therapeutic-target-discovery-in-head-and-neck-squamous-cell-carcinoma/450298/",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/ai-driven-multi-omics-integration-of-cancer-associated-fibroblasts-for-prognostic-modeling-and-therapeutic-target-discovery-in-head-and-neck-squamous-cell-carcinoma/450298.png","ImageObject",300,407,{"name":42,"@type":43},"Genevieve","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-07","2026-09-30",true,{"@type":52,"interactionType":53,"userInteractionCount":33},"InteractionCounter",{"@type":54},"ViewAction",{"@type":56,"mainEntity":57},"FAQPage",[58,64,68],{"name":59,"@type":60,"acceptedAnswer":61},"How were CAF-related differentially expressed genes (CAFs-DEGs) identified?","Question",{"text":62,"@type":63},"Bulk transcriptomic data from the GEO dataset are intersected with curated CAF gene sets. CAF-related genes are taken from GeneCards using the “Cancer-Associated Fibroblast” keyword and filtered by relevance score, then intersected with GEO-derived DEGs.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"What model was used to build the fibroblast-associated prognostic signature?",{"text":67,"@type":63},"A machine-learning LASSO-Cox regression model was developed using the TCGA-HNSCC cohort to generate an outcome-predicting framework based on the identified CAF-associated genes.",{"name":69,"@type":60,"acceptedAnswer":70},"How were therapeutic vulnerabilities and targets explored?",{"text":71,"@type":63},"Therapeutic vulnerabilities were assessed by integrating drug sensitivity prediction, AI-assisted cMAP screening, and molecular docking validation, which led to the identification of Epothilone B as a promising agent targeting HBEGF.","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},450298,1790813999,{"code":4,"msg":81,"data":82},"success",{"doc_id":78,"user_id":83,"nickname":42,"user_avatar":84,"doc_module":4,"category_id":85,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":86,"file_id":87,"file_url":88,"file_type":89,"file_size":90,"view_count":33,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":91,"language":92,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":93,"faqs":94,"seo_title":95,"seo_description":12,"update_tm":96,"read_time":97},1374391974585,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"npj | precision oncology Article  \nPublished in partnership with The Hormel Institute, University of Minnesota  \n[https://doi.org/10.1038/s41698-025-01196-x](https://doi.org/10.1038/s41698-025-01196-x)  \nAI-driven multi-omics integration of cancer-associated fi broblasts for prognostic modeling and therapeutic target discovery in head and necksquamous cell carcinoma  \n Check for updates  \n\n| Ning Zhao1,4, Jingru Zhang1,4, Tianyi Sun2,4, Xinyue Zhang3,4, Jingyang Liu3, Hanbing Yu1  & Hongyang Zhang1  |  |\n| --- | --- |\n| The Head and Neck Squamous Cell Carcinoma (HNSCC), arising from the mucosal epithelium of the oral cavity, pharynx, and larynx, continuestorepresent a major worldwide health burden due to its high mortality rates and late-stage diagnosis. A contribution of this study is the focus on the heterogeneity of CAFs, which directly impacts therapeutic response and resistance. To address this, we applied an AI-driven, multi-omics integration strategy to elucidate CAF-mediated mechanisms in HNSCC progression and therapy. Bulk transcriptomic data from Gene Expression Omnibus (GEO) were intersected with curated CAF gene sets to identify CAF-related differentially expressed genes (CAFsDEGs). To create a fibroblast-associated prognosis signature, a machine learning-based LASSO-Cox regression model has been developed using theTCGA-HNSCC cohort. Prognostic performance was validated through Kaplan–Meier survival analysis, time-dependent ROC, nomogram, Decision Curve Analysis (DCA), and calibration curves. To provide mechanistic insights, immune infiltration profiling, checkpoint correlations, single-cell expression mapping, tumor mutational burden (TMB), microsatellite instability (MSI), and DNA methylation analyses were performed. Furthermore, therapeutic vulnerabilities were explored by integrating drug sensitivity prediction, AI-assisted cMAP screening, and molecular docking validation, which identified Epothilone B as a promising agent targeting HBEGF. Overall, this research shows that understanding the heterogeneity of CAFs withAIenabled multi-omics modeling can reveal prognostic biomarkers and therapeutic targets for overcoming resistance, with the ultimate goal of improving precision oncology for HNSCC. |  |\n| The squamous epithelium that lines the oral cavity, pharynx, and larynx is the source of a wide variety of neoplasms known as Head and Neck Squamous Cell Carcinoma (HNSCC). HNSCC is an important health issue globally, resulting in ~600,000 new cases annually, and accounts for a significant share ofglobal cancer deaths1. Current available treatments, such as surgery, radiotherapy, and chemotherapy, face limitations in their | effectiveness, thus underlining the urgent need to discover new predictive biomarkers with predictive value and develop more effective forms of treatment2,3. These issues highlight the pressing need for dependable\u003Cbr>prognostic biomarkers, as well as the need for better targeted therapies.\u003Cbr>In small to medium-sized cohort studies, the tumor microenvironment has been studied and has shown evidence that non-malignant stromal |\n\n1Department of Otolaryngology, The First Hospital of China Medical University, Shenyang, Liaoning, China. 2Department of Youth League Committee, The First  \nHospital of China Medical University, Shenyang, Liaoning, China. 3China Medical University, Shenyang, Liaoning, China. 4These authors contributed equally: Ning Zhao, Jingru Zhang, Tianyi Sun, Xinyue Zhang.  \n[e-mail:](e-mail: yyyhhhbbb888@163.com)[ yyyhhhbbb888@163.com](e-mail: yyyhhhbbb888@163.com); [hyzhang91@cmu.edu.cn](hyzhang91@cmu.edu.cn)  \ncomponents, and specifically CAFs, significantly impact the initiation, progression, and response to treatment for HNSCC. CAFs are capable of generating heterogeneous cytokines that impact both tumor growth and metastatic spread. Furthermore, CAFs retain the ability of self-renewal and differentiation, contributing to tumor heterogeneity as well as tumor recurrence1,4,5. Combined, t","cbCair2CTMDfXq4I","https://ap.wps.com/l/cbCair2CTMDfXq4I","pdf",6617074,14,"English","# Introduction\n## Background and clinical challenges in HNSCC\n## Role of CAF heterogeneity and multi-omics gaps\n# Methods\n## Data sources and CAF-related DEGs identification\n## LASSO-Cox prognostic signature construction and validation\n## Immune, molecular, single-cell, and epigenetic analyses\n## Drug sensitivity prediction, cMAP screening, and molecular docking\n# Results and Applications\n## Prognostic performance metrics and clinical utility\n## Therapeutic vulnerability discovery and target identification","[{\"question\":\"How were CAF-related differentially expressed genes (CAFs-DEGs) identified?\",\"answer\":\"Bulk transcriptomic data from the GEO dataset are intersected with curated CAF gene sets. CAF-related genes are taken from GeneCards using the “Cancer-Associated Fibroblast” keyword and filtered by relevance score, then intersected with GEO-derived DEGs.\"},{\"question\":\"What model was used to build the fibroblast-associated prognostic signature?\",\"answer\":\"A machine-learning LASSO-Cox regression model was developed using the TCGA-HNSCC cohort to generate an outcome-predicting framework based on the identified CAF-associated genes.\"},{\"question\":\"How were therapeutic vulnerabilities and targets explored?\",\"answer\":\"Therapeutic vulnerabilities were assessed by integrating drug sensitivity prediction, AI-assisted cMAP screening, and molecular docking validation, which led to the identification of Epothilone B as a promising agent targeting HBEGF.\"}]","AI-driven multi-omics integration of cancer-associated fibroblasts for prognostic modeling and therapeutic target discovery in head and neck squamous cell carcinoma | PDF",1790732803,35,{"code":4,"msg":81,"data":99},[100,104,108,112,117,122,127,130,135,138,142],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":101,"show_sort_weight":102,"slug":103},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":105,"show_sort_weight":106,"slug":107},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":109,"show_sort_weight":110,"slug":111},"Exam",70,"exam",{"id":113,"doc_module":4,"doc_module_name":25,"category_name":114,"show_sort_weight":115,"slug":116},5,"Comic",60,"comic",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":119,"show_sort_weight":120,"slug":121},6,"Technology",50,"technology",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":125,"slug":126},7,"Healthcare",40,"healthcare",{"id":85,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":128,"slug":129},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":113,"slug":145},19,"General","general"]