Journal Article

Detecting ovarian cancer using extracellular vesicles: Progress and possibilities


Ovarian cancer (OC) is the deadliest gynecological malignancy. Most patients are diagnosed when they are already in the later stages of the disease. Earlier detection of OC dramatically improves the overall survival, but this is rarely achieved as there is a lack of clinically implemented biomarkers of early disease. Extracellular vesicles (EVs) are small cell-derived vesicles that have been extensively studied in recent years. They contribute to various aspects of cancer pathology, including tumour growth, angiogenesis and metastasis. EVs are released from all cell types and the macromolecular cargo they carry reflects the content of the cells from which they were derived. Cancer cells release EVs with altered cargo into biofluids, and so they represent an excellent potential source of novel biomarkers for the disease. In this review we describe the latest developments in EVs as potential biomarkers for earlier detection of OC. The field is still relatively young, but a number of studies have shown that EVs and the cargo they carry, including miRNAs and proteins, can be used to detect OC. They could also give insight into the stage of the disease and predict the likely therapeutic outcome. There remain a number of challenges to the use of EVs as biomarkers, but through ongoing research and innovation in this exciting field there is great potential for the development of diagnostic assays in the clinic that could improve patient outcome.

Attached files


Carollo, Emanuela
Paris, Bianca
Samuel, Priya
Pantazi, Paschalia
Bartelli, Thais Fernanda
Dias-Neto, Emmanuel
Brooks, Susan A.
Pink, Ryan C.
Carter, David R.F.

Oxford Brookes departments

Faculty of Health and Life Sciences\Department of Biological and Medical Sciences


Year of publication: 2019
Date of RADAR deposit: 2019-06-25

Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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