Decoding Cancer through Exosomal Vibrational Fingerprints and AI: Recent Advances in Raman and Surface-Enhanced Raman Spectroscopy for Liquid Biopsy

Abstract Extracellular vesicles (EVs), including those commonly described as exosomes, carry proteins, lipids, and nucleic acids that reflect the molecular state of their parent cells, making them attractive biomarkers for liquid biopsy-based cancer diagnosis. Surface-enhanced Raman spectroscopy (SERS), which builds on conventional Raman spectroscopy (RS), can read this cargo as vibrational fingerprints, label-free or via targeted nanoprobes, with high sensitivity from small sample volumes. This review examines recent advances in SERS-based exosome/EV analysis for cancer diagnosis, following the workflow from sample to diagnostic decision. We discuss how EV isolation, characterization, heterogeneity, and coisolated lipoproteins and proteins shape the measured spectra and review plasmonic substrates, biosensor and microfluidic platforms, label-free versus label-based strategies, and Raman band assignments. Particular emphasis is placed on artificial intelligence (AI) and machine learning (ML) for decoding exosome/EV SERS fingerprints, from conventional classifiers to deep learning (DL) and explainable AI, together with common pitfalls such as small cohorts, spectrum-level data leakage, class imbalance, and a lack of external validation. Applications in cancer detection, classification, subtyping, and monitoring are summarized for prostate, lung, breast, and pancreatic cancers, and representative platforms are compared by analytical performance and clinical readiness and against circulating tumor DNA and circulating tumor cell assays. Finally, we offer perspectives on the standardization, reference materials, shared spectral databases, and multicenter validation needed for clinical translation, positioning AI/ML-assisted SERS as a complementary tool for cancer triage and monitoring.