Asian Journal of Research and Reports in Neurology

Background: Glioblastoma Multiforme (GBM) presents significant diagnostic and therapeutic challenges due to its complex molecular pathogenesis. Current diagnostic methods often fail to detect early molecular signatures critical for timely intervention. This study integrates microarray and RNA-Seq datasets from both serum and tissue samples to explore differentially expressed genes (DEGs) and protein-protein interaction networks, aiming to identify robust biomarkers and understand the molecular underpinnings of GBM.

Methods: Utilizing microarray datasets (GSE116520, GSE90604) and RNA-Seq datasets (GSE165595, GSE228512) from both brain tissue and serum samples, this study conducted integrative differential gene expression analysis using limma and DESeq2 packages. Functional annotation and gene ontology analyses were performed with DAVID and ShinyGO tools. Protein-Protein Interaction (PPI) networks were constructed using the STRING database and analysed via Cytoscape to identify central hub genes.

Results: The analysis and cross-technology validation highlighted 1,051 common DEGs across tissue datasets, with 87 upregulated and 255 downregulated. Notably, three genes, MAST3, ADAM11, and PTPRK, were consistent across tissue and serum datasets, suggesting their utility as non-invasive biomarkers. Functional annotation identified critical biological processes and pathways disrupted in GBM, such as cell division, angiogenesis, and cell adhesion. The PPI network analysis identified central hub genes, offering insights into the molecular interactions contributing to GBM pathophysiology.

Conclusion: This study underscores a complex network of molecular interactions pivotal to GBM pathophysiology. The identified DEGs and pathways provide a foundation for developing diagnostic panels and therapeutic targets, emphasizing the need for further research to translate these biomarkers from bench to bedside.