Molecular profiling of protein markers on small extracellular vesicles (sEVs) is a promising strategy for the precise detection and classification of ovarian cancers. The accurate diagnosis, targeted therapy, and enhanced molecular understanding of ovarian cancer depend on its precise molecular detection and classification.
However, this strategy is challenging owing to the lack of simple and practical detection methods.
A research team led by Prof. Fengli Qu from the Hangzhou Institute of Medicine of Chinese Academy of Sciences has developed an aptamer-based nanoflow cytometry method for the molecular detection and classification of ovarian cancers through profiling of tumor markers on sEVs.
The study was published in Angewandte Chemie.
In this study, the researchers developed a simple, rapid, and non-invasive method for the molecular detection and classification of ovarian cancers via the phenotypic profiling of tumor-associated markers on sEVs. They firstly evaluated the capability of profiling protein markers on sEVs using the aptamer-based nanoflow cytometry (nFCM) detection strategy for profiling seven ovarian cancer-associated protein markers, including CA125, STIP1, CD24, EpCAM, EGFR, MUC1, and HER2, on sEVs. The results confirmed that this novel strategy-based profiling these seven protein markers enabled the precise detection of ovarian cancer with a high accuracy of 94.2%.
Furthermore, the researchers combined with machine learning algorithms, such as linear discriminant analysis (LDA) and random forest (RF) and found that the molecular classifications of ovarian cancer cell lines and subtypes were achieved with overall accuracies of 82.9% and 55.4%,
This simple, rapid, and non-invasive method shows potential for the molecular detection and classification of ovarian cancers in clinical practice and can be extended to detect and monitor other cancers.
Media Contact:
Xiaoman Zhai
Email: zhaixiaoman@him.cas.cn
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An Aptamer-Based Nanoflow Cytometry Method for the Molecular Detection and Classification of Ovarian Cancers through Profiling of Tumor Markers on Small Extracellular Vesicles
Schematic diagram of the molecular detection and classification of ovarian cancers.
