UNRAVELING TUMOR HETEROGENEITY AND PROGNOSTIC BIOMARKERS IN GLIOBLASTOMA THROUGH INTEGRATIVE EXOMIC PROFILING AND SUPERVISED CLASSIFIERS: A PILOT TCGA COHORT ANALYSIS

Glioblastoma Multiforme (GBM) represents the most prevalent and highly aggressive primary malignant tumor of the brain, exhibiting substantial molecular heterogeneity, intricate signaling pathways, and poor clinical outcomes, high-with a median survival of approximately 12-18 months even following standard trimodal treatment. Conventional histopathological classification and single-gene-based clinical strategies are often insufficient to fully characterize the complex and multidimensional genomic architecture of GBM, emphasizing the importance of precision medicine approaches. This review and integrated computational study explore the potential of combing Next-Generation sequencing (NGS), particularly Whole-Exome Sequencing (WES), with Artificial Intelligence (AI) and Machine (ML) methodologies for systematic analysis of high-dimensional genomic data, reduction of background variation, and identification of clinically relevant pathogenic somatic driver mutations, including alterations in TP53, PTEN, EGFR, and IDH1. Clinical and genomic information from the Cancer Genome Atlas (TCGA-GBM; N=79) was integrated to analyze feature-selection strategies, principal component analysis (PCA), Kaplan-Meier survival analysis based on molecular biomarker status such as PTEN and regularized logistic regression models designed to address class imbalance and high-dimensional genomic sparsity. In addition, the study considers the influence of the tumor microenvironment, epigenetic factors such as MGMT promoter methylation, and emerging evidence of sex-associated molecular differences in GBM. Collectively, the findings emphasize the potential of interpretable and integrative multi-omics-based machine learning frameworks to improve molecular and prognostic characterization of GBM and support the development of more precise, biomarker-guided therapeutic strategies in neuro-oncology.