Recent advances in machine learning-enhanced extracellular vesicle omics for oncology

Extracellular vesicles (EVs) are nanoscale, membrane-bound particles that carry nucleic acids, proteins, metabolites, and lipids. Their omics profiles can reflect tumor and microenvironmental states, making EVs a promising source of liquid biopsy biomarkers. Machine learning (ML) is well suited to EV omics because it can learn predictive signatures from high-dimensional, correlated, and sparse features and integrate complementary modalities. However, clinical translation is often hindered by EV-specific issues-including heterogeneous vesicle populations, isolation-dependent recovery of subtypes, and co-isolated particles. Few reviews comprehensively synthesize ML studies across EV transcriptomics, proteomics, metabolomics, and lipidomics in oncology. This review introduces EV fundamentals and provides a structured, in-depth evaluation of recent advances in ML-enhanced EV omics for early cancer detection, molecular subtyping, prognosis, and treatment response prediction. Key challenges-including data quality, model generalizability, algorithmic interpretability, ethical considerations, and standardization issues-are critically examined and distilled into practical recommendations for study design, validation, and reporting. Emerging directions include single-vesicle omics, higher-resolution EV profiling, interpretable multimodal fusion, and end-to-end pipelines that integrate EV multi-omics with ML. Together, these advances could help translate EV-based liquid biopsy into clinically useful tools for precision oncology.