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.

Metastatic spread of a cancer to secondary sites is a coordinated, non-random process. Cancer cell-secreted vesicles, especially exosomes, have recently been implicated in the guidance of metastatic dissemination, with specific surface composition determining some aspects of organ-specific localization. Nevertheless, whether the tumor microenvironment influences exosome biodistribution has yet to be investigated. Here, we show that microenvironmental cytokines, particularly CCL2, decorate cancer exosomes via binding to surface glycosaminoglycan side chains of proteoglycans, causing exosome accumulation in specific cell subsets and organs. Exosome retention results in changes in the immune landscape within these organs, coupled with a higher metastatic burden. Strikingly, CCL2-decorated exosomes are directed to a subset of cells that express the CCL2 receptor CCR2, demonstrating that exosome-bound cytokines are a crucial determinant of exosome-cell interactions. In addition to the finding that cytokine-conjugated exosomes are detected in the blood of cancer patients, we discovered that healthy subjects derived exosomes are also associated with cytokines. Although displaying a different profile from exosomes isolated from cancer patients, it further indicates that specific combinations of cytokines bound to exosomes could likewise affect other physiological and disease settings.

Extracellular vesicles (EVs) have been widely investigated as promising biomarkers for the liquid biopsy of diseases, owing to their countless roles in biological systems. Furthermore, with the notable progress of exosome research, the use of label-free surface-enhanced Raman spectroscopy (SERS) to identify and distinguish disease-related EVs has emerged. Even in the absence of specific markers for disease-related EVs, label-free SERS enables the identification of unique patterns of disease-related EVs through their molecular fingerprints. In this review, we describe label-free SERS approaches for disease-related EV pattern identification in terms of substrate design and signal analysis strategies. We first describe the general characteristics of EVs and their SERS signals. We then present recent works on applied plasmonic nanostructures to sensitively detect EVs and notable methods to interpret complex spectral data. This review also discusses current challenges and future prospects of label-free SERS-based disease-related EV pattern identification.