Affiliation:
1. Department of Gynecologic Oncology Zhejiang Cancer Hospital Hangzhou Institute of Medicine (HIM) Chinese Academy of Sciences Hangzhou Zhejiang 310022 China
2. College of Chemistry and Chemical Engineering Qufu Normal University Qufu 273165 Shandong China
3. Department of Gynecologic Oncology Zhejiang Cancer Hospital Hangzhou 310004 Zhejiang China
4. Molecular Science and Biomedicine Laboratory (MBL) State Key Laboratory of Chemo/ Biosensing and Chemometrics College of Biology College of Chemistry and Chemical Engineering Aptamer Engineering Center of Hunan Province Hunan University Changsha Hunan 410082 China
5. Institute of Molecular Medicine (IMM) Renji Hospital School of Medicine College of Chemistry and Chemical Engineering Shanghai Jiao Tong University Shanghai 200240 China
Abstract
AbstractMolecular 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.
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Natural Science Foundation of Shandong Province
Subject
General Chemistry,Catalysis
Cited by
9 articles.
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