Abstract
Background: Despite recent advances in glioblastoma (GBM) therapies, patient survival remains dismal. Existing prognostic markers lack sufficient accuracy, and the resistance of GBM to chemotherapy underscores the need for both improved predictive models and new therapeutic targets. Methods: We analyzed transcriptomic data from GBM and non-tumor brain tissues in the TCGA and GTEx databases to identify differentially expressed genes (DEGs). A prognostic signature was then constructed via univariate Cox regression, LASSO selection, and multivariate Cox analysis. The resulting prognostic model was validated in an independent GEO GBM cohort using Kaplan-Meier and log-rank testing. We further compared mutation patterns and immune infiltration between high- and low-risk groups. Multi-omics integration highlighted candidate tumor suppressors, which were functionally assessed in GBM cell lines and an orthotopic mouse model for the effects on ferroptosis sensitivity. Results: A 14-gene prognostic signature was developed and robustly stratified GBM patients into high- and low-risk groups with significantly different overall survival. High-risk tumors showed elevated PTEN mutation frequency, enhanced immunosuppressive microenvironments with increased PD-L1 and regulatory T cells, and distinct co-mutation patterns. STOX1 and ZNF248 were remarkably downregulated in GBM tissues. Their overexpression in vitro suppressed GBM cell proliferation and sensitized cells to ferroptosis. In vivo, nude mice bearing STOX1-overexpressing GBM cells showed prolonged survival under ferroptosis-inducing conditions. Conclusion: We present a validated 14-gene prognostic model that accurately predicts GBM patient outcomes and reveals STOX1 and ZNF248 as novel tumor suppressors related to ferroptosis. Targeting STOX1 and ZNF248 may overcome the resistance of GBM to ferroptosis and improve therapeutic efficacy.