Recent advances in biosensing platforms utilizing exosomal biomarker profiling for cancer diagnosis

Tumor-derived exosomes carry multidimensional molecular cargo, including surface proteins, microRNAs, and lipids that encode tumor identity and disease dynamics. These features support their application as biomarkers for liquid biopsy-based cancer diagnostics. Circulating tumor DNA undergoes rapid nuclease-mediated degradation, whereas exosomes retain stable molecular information that reflects the proteomic, transcriptomic, and metabolic states of parent tumor cells. However, clinical translation of exosome-based sensing remains limited by variability in isolation, biological heterogeneity, and the analytical difficulty of detecting low-abundance biomarkers in clinical samples. In this review, we examine cancer-specific exosomal signatures across breast, lung, colorectal, and gastric cancers and evaluate biosensing platforms for exosomal biomarker profiling. We integrate engineering principles, clinical performance metrics, and AI-assisted analysis across complementary biosensing modalities to establish a cross-platform analytical framework. We compare optical platforms based on surface plasmon resonance, localized surface plasmon resonance, and surface-enhanced Raman scattering with photoluminescence- and electrochemical-based platforms in terms of sensitivity, clinical compatibility, and translational potential. Furthermore, we examine artificial intelligence (AI)-assisted biosensing frameworks, including classical machine learning classifiers, deep convolutional networks, ensemble models, explainable AI methods, and large language model interfaces. We evaluate how each framework addresses high-dimensional spectral complexity, nonlinear relationships among signals, and inter-patient variability in exosomal data. Finally, we identify remaining challenges, such as the lack of standardized isolation protocols and the absence of large-scale clinical validation. We further highlight minimal residual disease monitoring and early-stage cancer detection as important and underexplored directions for AI-integrated exosomal biosensing in precision oncology.