What question did this study set out to answer?

This research aims to identify key molecular drivers and prognostic biomarkers in glioblastoma using integrated bioinformatics approaches.

February 26, 2026

Molecular dissection of glioblastoma: Identification of prognostic hub genes through integrated bioinformatics analysis

Key Points

This research aims to identify key molecular drivers and prognostic biomarkers in glioblastoma using integrated bioinformatics approaches.
Retrieved GBM-related genes from GeneCards and DisGeNET.
Constructed protein-protein interaction networks and analyzed centrality in Cytoscape.
Performed functional enrichment analyses and assessed gene expression profiles from TCGA datasets.
Conducted Kaplan–Meier survival analysis for hub genes and evaluated promoter methylation and immune cell correlations.
Identified 22 hub genes from 464 GBM-related genes through network centrality metrics.
Found 14 genes significantly dysregulated in GBM compared to normal brain tissue.
Promoter methylation analysis revealed interleukin-1 beta hypomethylation and tumor necrosis factor hypermethylation.
Four core hub genes (ACTB, CASP3, ERBB2, GAPDH) were overexpressed and associated with worse survival outcomes.

Abstract

Objectives: Glioblastoma multiforme (GBM) is the deadliest and therapeutically challenging primary brain malignancies, with dismal prognosis. Identifying robust molecular drivers is critical to improving prognostic accuracy and developing targeted therapies. Material and Methods: An integrative bioinformatics pipeline was implemented, starting with the retrieval of GBM-associated genes from GeneCards and DisGeNET. Protein-protein interaction networks were constructed using STRING and analyzed in Cytoscape through CytoNCA for centrality measures. Functional enrichment analyses (Gene ontology, kyoto encyclopedia of genes and genomes) characterized biological roles. Gene expression profiles were assessed using the cancer genome atlas datasets. Kaplan–Meier survival analysis on 22 hub genes guided the selection of core genes. Core hub genes were evaluated for promoter methylation profiling, immune cell infiltration correlation, and genomic alteration mapping using cBioPortal and TISIDB as well as protein expression validation using Human Protein Atlas. Results: From 464 GBM-related genes, 22 hub genes were identified through network centrality metrics. Expression profiling revealed 14 significantly dysregulated genes in GBM versus normal brain tissue. Promoter methylation analysis indicated interleukin-1 beta hypomethylation and tumor necrosis factor hypermethylation. Four core hub genes – ACTB, CASP3, ERBB2 , and GAPDH – were overexpressed, associated with immune infiltration and genetic alterations. Kaplan–Meier analysis demonstrated significant prognostic impact: ACTB ( P = 0.029, hazard ratio HR =1.5), CASP3 ( P = 0.033, HR =1.5), ERBB2 ( P = 0.038, HR =1.5), and GAPDH ( P = 0.023, HR =1.5), linking high expression to reduced overall survival. Protein-level validation reinforced transcriptomic findings. Conclusion: ACTB, CASP3, ERBB2, and GAPDH emerge as key regulators in GBM progression, contributing to cytoskeletal remodeling, apoptosis resistance, immune modulation, and metabolic adaptation. These genes hold promise as prognostic biomarkers and therapeutic targets for GBM.

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