Volume 10 Issue 1

Integrative Multi-Omics Network Analysis Identifies Novel Drivers and Pathways in Glioblastoma Multiforme

Azeez

Abstract

The Cancer Genome Atlas (TCGA) datasets enable integrative analysis of multi-omics alterations in cancer, offering insights into glioblastoma multiforme (GBM) mechanisms. Here, we employ network analysis to identify molecular pathways and candidate drivers in GBM. Functional modules derived from edge-betweenness clustering of a protein interaction network, built from altered genes, revealed enrichment in both established and novel cancer-associated pathways in GBM. Among 72 genes with high-impact deleterious mutations (? 3 samples), several (for example, fatty acid synthases ACACA and ACACB) represent novel candidates in GBM, though previously implicated in other cancers. Additionally, 89 genes in copy number-altered regions were prioritized for functional relevance based on network interactions. These findings highlight novel genes and pathways with potential roles in GBM pathogenesis, providing candidates for mechanistic studies and targeted therapies.

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