Abstract
Abstract
Pediatric glioblastoma (pGBM) is an aggressive central nervous system (CNS) tumor whose pathological progression is significantly influenced by exosomal signaling. This study integrated and analyzed transcriptomic datasets (GSE50161, GSE35493) from the Gene Expression Omnibus (GEO), focusing on the intersection between exosome-related genes (ERGs) and disease-associated differentially expressed genes (DEGs). Key biomarkers, FGF9, TGFBR1, and PLCB4, were identified using a machine learning model. The results demonstrated that TGFBR1 expression was up-regulated in the tumor microenvironment, whereas FGF9 and PLCB4 were down-regulated. These genes were closely associated with the γ/α (IFN-γ/α) signaling pathway and E2F target activation. Immune profiling revealed that low expression of FGF9 and PLCB4 correlated with a reduction in central memory CD4
+
T cells, while high TGFBR1 expression was associated with an increase in memory B cells. Further regulatory network analysis uncovered potential epigenetic regulatory mechanisms. In silico drug screening and molecular docking suggested that the histone deacetylase inhibitor Trichostatin A may hold therapeutic potential. This study provides novel biomarkers and insights for the diagnosis and targeted therapy of pGBM.