GSC-associated gene signature predicts survival in adult and pediatric high-grade glioma patients.

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 using in-house DIPG13 RNA-seq and the public GSE54791 dataset. The biological relevance of this signature was supported by 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 integrating the gene signature-derived risk score with key clinical factors, including age, race, and radiation therapy status, enabling individualized survival prediction. To further support the biological basis of the model, we experimentally examined FAM86B1, one of the five genes in the final signature and a gene not previously characterized in glioma biology, and found that the closely related FAM86B1/FAM86B2 axis was enriched in GSCs and overexpressed in GBM tissues, while suppression of this axis impaired GSC maintenance in both GBM and DIPG GSCs. Collectively, this study establishes a biologically grounded, GSC-centered prognostic model for HGG that improves patient stratification and may inform personalized therapeutic strategies.