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Glioblastoma Prognosis and Therapeutic Response Predicted by a Cancer-Associated Fibroblasts Risk Score.
Hongyi Zhou1, Xi Yang1, Wen Zhao2
1Department of Anus and Intestine Surgery, The Affiliated Lihuili Hospital of Ningbo University, Ningbo University, Ningbo, Zhejiang, 315040, China, nbu.edu.cn.
Mediators of Inflammation
|March 24, 2026
Summary
This study developed a novel glioblastoma (GBM) risk model using cancer-associated fibroblast (CAF) genes. The model accurately predicts patient survival and guides personalized treatment strategies for GBM.
Area of Science:
- Oncology
- Cancer Biology
- Bioinformatics
Background:
- Cancer-associated fibroblasts (CAFs) are crucial in the tumor microenvironment but underexplored in glioblastoma (GBM).
- Systematic characterization of CAFs is needed for improved GBM patient stratification and treatment.
Purpose of the Study:
- To develop a prognostic model integrating CAFs-related features for GBM.
- To provide new insights for precise stratification and optimized treatment strategies for GBM patients.
Main Methods:
- Analyzed single-cell RNA sequencing data of GBM using R and Seurat package.
- Identified CAFs phenotypes and key prognostic genes, constructing a CAFs-based risk score and a nomogram.
- Validated the model's prognostic and therapeutic relevance through multi-dimensional analyses.
Main Results:
- Identified six CAFs-related genes (FAM241B, LSM2, IGFBP2, LOXL1, OSMR, STOX1) significantly associated with GBM prognosis.
- Developed a robust CAFs-based risk score and nomogram model independently predicting overall survival and improving predictive accuracy.
- Demonstrated association between risk score, immune cell infiltration, and efficacy of targeted and immunotherapies.
Conclusions:
- Introduced an accurate GBM risk profiling framework and nomogram based on CAFs.
- Provided insights into CAFs' roles in GBM progression and immunity, aiding tumor mutation deciphering and immune landscape mapping.
- Offered potential for improved personalized treatment strategies and patient outcomes in GBM.
