Glioblastoma Prognosis and Therapeutic Response Predicted by a Cancer-Associated Fibroblasts Risk Score - Research Article

Sep 29, 2026

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Cancer-associated fibroblasts (CAFs) are a key component of the glioblastoma tumor microenvironment, yet their prognostic value in GBM remains insufficiently characterized. 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.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages31

Reading Time1 h 18 min

AccessFree to download

Introduction

Background on GBM and CAFs

Methods

Data sources and scRNA-seq processing

Gene screening and risk score construction

Nomogram development and validation

Results

Prognostic CAF-related genes and risk stratification

Immune infiltration association

Predicted outcomes for targeted and immunotherapeutic treatments

Conclusions

GBM risk profiling framework and clinical implications

Wiley Mediators of Inflammation Volume 2025, Article ID 4342537, 31 pages [https://doi.org/10.1155/mi/4342537](https://doi.org/10.1155/mi/4342537) Research Article Glioblastoma Prognosis and Therapeutic Response Predicted by a Cancer-Associated Fibroblasts Risk Score Hongyi 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 1Department of Anus and Intestine Surgery, The Afﬁliated Lihuili Hospital of Ningbo University, Ningbo University, Ningbo, Zhejiang 315040, China 2Department of Neurology, The Afﬁliated Lihuili Hospital of Ningbo University, Ningbo University, Ningbo, Zhejiang 315040, China 3 Wenzhou Medical University, Wenzhou, Zhejiang 325027, China Correspondence should be addressed to Bingxuan Ren; renbx1114@126.com and Kaixia Yang; [2311140130@nbu.edu.cn](2311140130@nbu.edu.cn) Received 13 September 2025; Revised 9 November 2025; Accepted 24 November 2025 Guest Editor: Liu Jinhui Copyright © 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. Background: 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. Methods: 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. Results: 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. Conclusions: 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. Keywords: cancer-associated ﬁbro

FAQs

What is the main goal of the study in glioblastoma?

To develop a CAFs-based prognostic model that predicts GBM patient outcomes and supports precision stratification for improved treatment decisions.

How was the CAFs risk score model constructed and validated?

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.

Which findings connect the risk score to the immune microenvironment and treatment response?

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.

Sep 29, 2026

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