Exposure-aware multi-omics and artificial intelligence for biomarker discovery and precision prevention in diffuse...

Diffuse gliomas are now diagnosed and studied through integrated molecular classification, radiomics, single-cell biology, spatial profiling, proteogenomics, metabolomics, and artificial intelligence. Yet many precision-medicine models still begin at diagnosis and emphasize tumor-intrinsic molecular features, leaving environmental, occupational, lifestyle, microbiome, metabolic, immune, and treatment-related exposures at the margins. This review develops an exposome-informed view of diffuse glioma biomarker discovery. Biomarker discovery is separated from clinical prevention: current evidence does not justify population-level glioma screening based on environmental exposures, but it does support systematic integration of external exposures and internal exposure-related molecular states with tumor and host biology. The synthesis focuses on five linked dimensions: the limits of current artificial intelligence and multi-omics models when exposure biology is excluded; glioma-relevant exposure domains stratified by evidence strength and measurability; genotoxic, epigenetic, vascular, neuroimmune, and immunometabolic conduits through which exposures may shape tumor ecology; computational strategies for temporally anchored integration of geospatial, occupational, clinical, liquid-biopsy, imaging, tumor-omic, single-cell, spatial, microbiome, and metabolomic data; and clinically realistic applications in high-risk surveillance, recurrence-aware monitoring, treatment-toxicity reduction, and biomarker-guided trial stratification. By aligning exposome science with systems neuro-oncology, the review outlines a translational agenda for exposure-aware glioma biomarkers while maintaining a conservative boundary between established evidence, mechanistic hypotheses, and future clinical implementation.