Early cancer detection has significant clinical value, but there remains no single method that can comprehensively identify multiple types of early-stage cancer. Here, we report the diagnostic accuracy of simultaneous detection of 6 types of early-stage cancers (lung, breast, colon, liver, pancreas, and stomach) by analyzing surface-enhanced Raman spectroscopy profiles of exosomes using artificial intelligence in a retrospective study design. It includes classification models that recognize signal patterns of plasma exosomes to identify both their presence and tissues of origin. Using 520 test samples, our system identified cancer presence with an area under the curve value of 0.970. Moreover, the system classified the tumor organ type of 278 early-stage cancer patients with a mean area under the curve of 0.945. The final integrated decision model showed a sensitivity of 90.2% at a specificity of 94.4% while predicting the tumor organ of 72% of positive patients. Since our method utilizes a non-specific analysis of Raman signatures, its diagnostic scope could potentially be expanded to include other diseases.

Exosomes are a class of extracellular vesicles, with a size of about 100 nm, secreted by most cells and carrying various bioactive molecules such as nucleic acids, proteins, and lipids, and reflect the biological status of parent cells. Exosomes have natural advantages such as high biocompatibility and low immunogenicity for efficient delivery of therapeutic agents such as chemotherapeutic drugs, nucleic acids, and proteins. In this review, we introduce the latest explorations of exosome-based drug delivery systems for cancer therapy, with particular focus on the targeted delivery of various types of cargoes.