A fibroblast-specific gene signature as a therapeutic target for glioblastoma developed based on the characteristics of tumor microenvironment.

ABSTRACT:

Background

This study identified fibroblast-specific genes to develop a RiskScore model to improve prognostic accuracy and guide personalized treatment in glioblastoma (GBM).

Methods

We analyzed fibroblast-specific signatures in the GSE273274 cohort using "Seurat" R package for scRNA-seq data processing. Fibroblast-related gene modules were identified via WGCNA, and functional enrichment was assessed with "clusterProfiler" package. A RiskScore model was established using univariate, Lasso Cox regression analysis, and "survival" package, validated by "timeROC" for receiver operator characteristic (ROC) curve. Finally, immune infiltration and drug sensitivity was evaluated applying "ESTIMATE," "TIMER," "MCPcounter," and "pRRophetic" packages. Experimental validation included q

SUBMITTER: Liu N 

PROVIDER: S-EPMC12750583 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature