Molecular profiling of protein markers on small extracellular vesicles (sEVs) is a promising strategy for the precise detection and classification of ovarian cancers. However, this strategy is challenging owing to the lack of simple and practical detection methods. In this work, using an aptamer-based nanoflow cytometry (nFCM) detection strategy, a simple and rapid method for the molecular profiling of multiple protein markers on sEVs was developed. The protein markers can be easily labeled with aptamer probes and then rapidly profiled by nFCM. Seven cancer-associated protein markers, including CA125, STIP1, CD24, EpCAM, EGFR, MUC1, and HER2, on plasma sEVs were profiled for the molecular detection and classification of ovarian cancers. Profiling these seven protein markers enabled the precise detection of ovarian cancer with a high accuracy of 94.2 %. In addition, combined with machine learning algorithms, such as linear discriminant analysis (LDA) and random forest (RF), the molecular classifications of ovarian cancer cell lines and subtypes were achieved with overall accuracies of 82.9 % and 55.4 %, respectively. Therefore, this simple, rapid, and non-invasive method exhibited considerable potential for the auxiliary diagnosis and molecular classification of ovarian cancers in clinical practice.
Keywords: Aptamer; Classification; Detection; Ovarian Cancer; Small Extracellular Vesicles.
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