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

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.