Cancer-associated fibroblasts (CAFs) critically regulate tumor progression, angiogenesis, metastasis, and therapeutic resistance. This study investigated the characteristics of CAFs in glioblastoma (GBM) and developed a CAF-based risk signature to predict patient prognosis. The single-cell RNA sequencing (scRNA-seq) data were sourced from the Gene Expression Omnibus (GEO) database, whereas the bulk RNA-seq datasets were retrieved from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA), respectively. The Seurat R package processed scRNA-seq data to identify CAF clusters using established markers. Prognostic genes were screened through univariate Cox regression, with Lasso regression constructing the final risk model. A nomogram incorporating clinical parameters was subsequently developed. Immunohistochemical validation was performed using the Human Protein Atlas (HPA) for the signature genes. The qRT-PCR validation was conducted in U251 and HA cells. ScRNA-seq analysis revealed five CAF clusters in GBM, including three prognostically relevant subtypes. Three key genes were refined to construct a risk signature functionally enriched in the the IL6JAKSTAT3 signaling, P53 pathway, and inflammatory response. The signature correlated strongly with stromal and immune scores. Multivariate analysis confirmed risk signature independent prognostic value (P < 0. 0001), followed by age (P = 0. 005). The CAF-derived nomogram demonstrated superior predictive accuracy for 1-/2-year survival compared to clinical factors alone. The signature genes were validated in the HPA database and experimental validation. This study proposes CAF-derived molecular signatures as potential predictors of glioblastoma prognosis worthy of clinical validation. Systematic characterization of CAF heterogeneity may offer insights for interpreting GBM immunotherapy responses, providing a foundation for future exploration of stroma-targeted therapeutic strategies.
Zhang et al. (2025) studied this question.