Integrating machine learning with mendelian randomization for unveiling causal gene networks in glioblastoma multiforme.

ABSTRACT:

Background

Glioblastoma multiforme (GBM) is a highly aggressive brain cancer with poor prognosis and limited treatment options. Despite advances in understanding its molecular mechanisms, effective therapeutic strategies remain elusive due to the tumor's genetic complexity and heterogeneity.

Methods

This study employed a comprehensive analysis approach integrating 113 machine learning algorithms with Mendelian Randomization (MR) analysis to investigate the molecular underpinnings of GBM. Five publicly available gene expression datasets were analyzed to identify differentially expressed genes (DEGs) associated with GBM. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify GBM-related gene modules. Further, gene set enrichment and variation analyses were c

SUBMITTER: Du L 

PROVIDER: S-EPMC11730047 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature