Multi-Omics Integration for Advancing Glioma Precision Medicine

Gliomas are among the most malignant and aggressive tumors of the central nervous system, characterized by the absence of early diagnostic markers, poor prognosis, and a lack of effective treatments. Advances in high-throughput technologies have facilitated a refined molecular classification of gliomas, incorporating genetic features. However, diagnosis and clinical management based on isolated genetic data often fail to capture the full histological and molecular complexity of these tumors, posing significant challenges. In the era of computational methodologies and artificial intelligence, the integration of multiple omics layers-genomics, transcriptomics (including sex-dependent differential expression patterns), epigenomics, proteomics, metabolomics, radiomics, single-cell analysis, and spatial omics-into a comprehensive framework holds the potential to deepen our understanding of glioma biology and enhance diagnostic precision, prognostic accuracy, and treatment efficacy. Herein, we provide a comprehensive overview of multi-omics strategies used to decipher the adult-type diffuse glioma molecular taxonomy and describe how the integration of multilayer data combined with machine-learning-based algorithms is paving the way for advancements in patient prognosis and the development of personalized, targeted therapeutic interventions.