Machine learning model on multi-omics data enables risk stratification and identifies molecular heterogeneity and therapeutic targets in glioblastoma.

Multimodal data integration reveals causal features often missed by single-modality analyses, offering a more comprehensive view of glioblastoma (GBM) complexity. We collected radiomic, pathomic, genomic, transcriptomic, and proteomic data from patients with IDH-wild-type GBM to construct a machine learning–based risk stratification model. While sample sizes varied across modalities, 147 patients with complete data across all five omics layers were used for integrative analysis. This approach identified two clinically distinct subgroups. The low-risk group, linked to favorable outcomes, showed enhanced neurodevelopmental signatures, increased neuronal infiltration, and more oligodendrocytes. In contrast, the high-risk group, associated with poor prognosis, exhibited strong proliferative signals and hyperactive cell cycle pathways. Downstream multi-omics analysis identified PDIA4, EIF3I, and RFT1 as potential prognostic biomarkers and therapeutic targets in high-risk GBM. These findings underscore the utility of multimodal machine learning in refining prognostic models, characterizing tumor heterogeneity, and informing personalized treatment strategies.