Identification of Potential Key Genes Associated with the Pathogenesis and Prognosis of Glioblastoma Based on Integrated Bioinformatics Analysis

Abstract Background: Despite striking advances in multimodality management, the low survival rate of Glioblastoma (GBM) patients has not been significantly improved and identifying novel diagnostic and prognostic biomarkers is urgently demanded. The present study aimed to identify potential key genes associated with the pathogenesis and prognosis of GBM.Methods: Differentially expressed genes between GBM and normal brain tissue samples were screened by an integrated analysis of multiple gene expression profile datasets. Key genes related to the pathogenesis and prognosis of GBM were identified by employing protein–protein interaction network and Cox proportional hazards model analyses.Results: We identified nine hub genes (TP53, FN1, EGFR, MYC, RRM2, EZH2, FOXM1, CD44 and MMP2) which might be closely associated with the pathogenesis of GBM. A prognostic gene signature consisted of RAB33B, KIAA1199, TEK, EVC, SOD2, CXCR4, hCG_40738, CHD9, GCSH, SUHW1, RPS6KA5, PDCD4, ZG16, KCNG1, DECR1, PPCS, SERPINF1, TMSB10, NAT10, HIC2, PIR and OR2W1 was constructed with a good performance in predicting overall survivals (OS).Conclusions: The findings of present study would provide certain reference for further predicting the diagnostic and prognostic biomarkers to facilitate the molecular targeting therapy of GBM.