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This study develops a CAFs-driven prognostic model that integrates CAF-related features to enable precise patient stratification and optimized treatment selection. Single-cell RNA sequencing analysis identifies prognostic CAF-related genes, which are used to build a risk score model and nomogram, then validated across independent cohorts using multi-dimensional clinical and immunological evaluations.",{"@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/glioblastoma-prognosis-and-therapeutic-response-predicted-by-a-cancer-associated-fibroblasts-risk-score-research-article/438157/",{"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/glioblastoma-prognosis-and-therapeutic-response-predicted-by-a-cancer-associated-fibroblasts-risk-score-research-article/438157.png","ImageObject",300,407,{"name":92,"@type":93},"awa","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-01","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":8},"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 study in glioblastoma?","Question",{"text":112,"@type":113},"To develop a CAFs-based prognostic model that predicts GBM patient outcomes and supports precision stratification for improved treatment decisions.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How was the CAFs risk score model constructed and validated?",{"text":117,"@type":113},"The study used GBM data from public databases and analyzed single-cell RNA-seq with Seurat to identify CAF phenotypes and prognostic CAF-related genes. Regression analysis produced the risk score, which was validated in multiple independent cohorts.",{"name":119,"@type":110,"acceptedAnswer":120},"Which findings connect the risk score to the immune microenvironment and treatment response?",{"text":121,"@type":113},"The computed risk score showed a statistically significant association with immune cell infiltration level, and the prognostic model demonstrated robust efficacy in predicting outcomes after conventional targeted therapies and immunotherapeutic interventions.","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},438157,1790818729,{"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":8,"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},3985747858093,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","Wiley  \nMediators of Inflammation  \nVolume 2025, Article ID 4342537, 31 pages [https://doi.org/10.1155/mi/4342537](https://doi.org/10.1155/mi/4342537)  \nResearch Article  \nGlioblastoma Prognosis and Therapeutic Response Predicted by a Cancer-Associated Fibroblasts Risk Score  \nHongyi Zhou , 1 Xi Yang , 1 Wen Zhao ,2 Yingqi Huang ,3 Zhixiang Zhang ,2 Jincheng Jiang ,2 Xinchen Jiang ,2 Bingxuan Ren ,2 and Kaixia Yang 2  \n1Department of Anus and Intestine Surgery, The Affiliated Lihuili Hospital of Ningbo University, Ningbo University, Ningbo, Zhejiang 315040, China  \n2Department of Neurology, The Affiliated Lihuili Hospital of Ningbo University, Ningbo University, Ningbo, Zhejiang 315040, China 3 Wenzhou Medical University, Wenzhou, Zhejiang 325027, China  \nCorrespondence should be addressed to Bingxuan Ren; renbx1114@126.com and Kaixia Yang; [2311140130@nbu.edu.cn](2311140130@nbu.edu.cn)  \nReceived 13 September 2025; Revised 9 November 2025; Accepted 24 November 2025  \nGuest Editor: Liu Jinhui  \nCopyright © 2025 Hongyi Zhou et al. Mediators of Inflammation published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \nBackground: Cancer-associated fibroblasts (CAFs), as a key component of the tumor microenvironment, have not been systematically elucidated in glioblastoma (GBM) . Our study aims to develop a prognostic model integrating CAFs-related features, with the goal of providing new insights for precise stratification and optimized treatment strategies for GBM patients.  \nMethods: Utilizing GBM-related data from reputable public databases, we utilized the Seurat package in R to analyze single-cell RNA sequencing (scRNA-seq) data for the characterization of CAFs in GBM. We identified CAFs phenotypes and screened for key CAFs-related genes significantly associated with patient prognosis. Using regression analysis, we constructed a CAFs-based risk score, which was subsequently validated in multiple independent cohorts. A nomogram integrating the risk score and clinicopathological features was also developed. Furthermore, we systematically evaluated the prognostic and therapeutic relevance of the model in GBM patients through multi-dimensional analyses, including gene mutation profiling, pathway enrichment analysis, immune infiltration, immunotherapy response, and drug sensitivity analysis.  \nResults: A total of six CAFs-related genes (FAM241B, LSM2, IGFBP2, LOXL1, OSMR, and STOX1) were identified as significantly associated with GBM prognosis. We used it to construct the CAFs-based risk score model, which demonstrated robust prognostic performance across multiple cohorts and served as an independent predictor of overall survival in GBM patients, efficiently categorizing groups into high and low risk. By integrating clinical features, the nomogram model significantly increased predictive accuracy and reliability. Analytical results indicated a statistically significant association between the computed risk score and the level of immune cell infiltration. Furthermore, the established prognostic model exhibited robust efficacy in predicting patient outcomes following conventional targeted treatments as well as immunotherapeutic interventions.  \nConclusions: This study introduces a GBM risk profiling framework and accompanying nomogram, offering exceptional accuracy in prognostic prediction for GBM. The framework and nomogram provide valuable insights into the roles of CAFs and key genes in GBM progression and immunity, and extend beyond classification by offering promising avenues for deciphering tumor mutations, mapping immune landscapes, refining drug predictions, and forecasting the efficacy of immunotherapeutic interventions. These findings have the potential to significantly improve personalized treatment strategies and patient outcomes.  \nKeywords: cancer-associated fibro","cbCaiqbBSYc5UhgU","https://ap.wps.com/l/cbCaiqbBSYc5UhgU","pdf",39821928,31,"English","# Introduction\n## Background on GBM and CAFs\n# Methods\n## Data sources and scRNA-seq processing\n## Gene screening and risk score construction\n## Nomogram development and validation\n# Results\n## Prognostic CAF-related genes and risk stratification\n## Immune infiltration association\n## Predicted outcomes for targeted and immunotherapeutic treatments\n# Conclusions\n## GBM risk profiling framework and clinical implications","[{\"question\":\"What is the main goal of the study in glioblastoma?\",\"answer\":\"To develop a CAFs-based prognostic model that predicts GBM patient outcomes and supports precision stratification for improved treatment decisions.\"},{\"question\":\"How was the CAFs risk score model constructed and validated?\",\"answer\":\"The study used GBM data from public databases and analyzed single-cell RNA-seq with Seurat to identify CAF phenotypes and prognostic CAF-related genes. Regression analysis produced the risk score, which was validated in multiple independent cohorts.\"},{\"question\":\"Which findings connect the risk score to the immune microenvironment and treatment response?\",\"answer\":\"The computed risk score showed a statistically significant association with immune cell infiltration level, and the prognostic model demonstrated robust efficacy in predicting outcomes after conventional targeted therapies and immunotherapeutic interventions.\"}]","Glioblastoma Prognosis and Therapeutic Response Predicted by a Cancer-Associated Fibroblasts Risk Score - Research Article | PDF",1790684448,78]