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

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.