A Glioma Stem Cell-Associated Transcriptomic Program Predicts Survival Across Adult and Pediatric High Grade Gliomas

High-grade gliomas (HGGs), including adult glioblastoma (GBM) and pediatric diffuse intrinsic pontine gliomas (DIPGs), are sustained by glioma stem cells (GSCs) that drive tumor initiation, therapeutic resistance, and recurrence. Although numerous prognostic models have been proposed, few are directly grounded in the core biology of GSCs across both adult and pediatric HGGs. In this study, we defined a GSC-associated gene signature by integrating transcriptomic profiles from patient-derived GSCs and their differentiated counterparts (in-house DIPG13 RNA-seq and public GSE54791 dataset). The biological relevance of this signature was confirmed through functional enrichment and protein-protein interaction analyses. To assess its prognostic value, we applied machine learning-based modeling in a large training cohort (Chinese Glioma Genome Atlas, CGGA) and validated the resulting model across three independent datasets (Gravendeel, Rembrandt, and an integrated pediatric HGG cohort), demonstrating consistent predictive performance. To enhance clinical applicability, we developed a nomogram that integrates the gene signature-derived risk score with key clinical factors (age, sex, race, and radiation therapy status), enabling individualized survival prediction. Collectively, this study establishes a biologically grounded, GSC-centered prognostic model for HGG that improves patient stratification and may inform personalized therapeutic strategies.