Comparison of benign peritoneal fluid- and ovarian cancer ascites-derived extracellular vesicle RNA biomarkers

Abstract

Background: Extracellular vesicles (EVs) are considered as a new class of resources for potential biomarkers. We analyzed expression of specific mRNA and miRNA in EVs derived from ovarian cancer ascites and the ideal controls, peritoneal fluids from benign patients for potential early detection and prognostic biomarkers. Methods: Fluids were collected from subjects with benign cysts or endometrioma (n = 10), or low/high grade serous ovarian carcinoma (n = 8). EV particles were captured using primarily ExoComplete filterplate or ultracentrifugation and analyzed by nanoparticle tracking analysis, ELISA, and scanning electron microscopy. EV RNAs extracted from two ascites and three peritoneal fluids were submitted for next-generation sequencing. The expression of 34 mRNA and 18 miRNAs in the EVs isolated from patient fluids and cell line media was determined using qPCR. Results: EVs isolated from patient samples had concentrations greater than 1010 EV particles/mL and 30% were EpCAM-positive based on ELISA. EV particle sizes averaged 113 ± 11.5 nm. The qPCR studies identified five mRNA (CA11, MEDAG, LAMA4, SPINT2, NANOG) and six miRNA (let-7b, miR23b, miR29a, miR30d, miR205, miR720) that were significantly differentially expressed between cancer ascites and peritoneal fluids. In addition, CA11 mRNA was decreased to 0.5-fold and SPINT2 and NANOG mRNA were significantly increased up to 100-fold in conditioned media of cancer cells compared to immortalized ovarian surface and fallopian tube epithelial cell lines, the hypothesized cells of origin for ovarian cancer development. Conclusions: This study indicates that EV mRNA profiles can reflect the disease stage and may provide a potentially novel source for discovery of biomarkers in ovarian cancer. Electronic supplementary material The online version of this article (10.1186/s13048-018-0391-2) contains supplementary material, which is available to authorized users

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This paper was published in Harvard University - DASH.

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