Uncovering Latent Structure in Gliomas Using Multi-Omics Factor Analysis

Abstract

BackgroundGliomas are the most common malignant brain tumors in adults, characterized by a poor prognosis. Although the current World Health Organization (WHO) classification provides clear guidelines for classifying oligodendroglioma, astrocytoma, and glioblastoma patients, significant heterogeneity persists within each class, limiting the effectiveness of current treatment strategies. With the increase of large-scale multi-omics datasets due to advancements in sequencing technologies, and online databases that provide them, such as The Cancer Genome Atlas (TCGA), it is now possible to investigate these tumors at multiple molecular levels. MethodsIn this work, we apply integrative multi-omics analysis to explore the interplay between genomic (mutations), epigenomic (DNA methylation), and transcriptomic (mRNA and miRNA) layers. Our approach relies on Multi-Omics Factor Analysis (MOFA), a Bayesian latent factor analysis model designed to capture sources of variation across different omics types. ResultsOur results highlight distinct molecular profiles across the three glioma types and identify potential relationships between methylation and genetic expression. In particular, we uncover novel candidate biomarkers with prognostic value, as well as a transcriptional profile associated with neural system development. ConclusionsThese findings may contribute to more personalized therapeutic strategies, potentially enhancing treatment effectiveness and improving survival outcomes for this disease.