Evaluation of Cell-Free Urine microRNAs Expression for the Use in Diagnosis of Ovarian and Endometrial Cancers. A Pilot Study

Luděk Záveský, Eva Jandáková, Radovan Turyna, Lucie Langmeierová, Vít Weinberger, Lenka Záveská Drábková, Martina Hůlková, Aleš Hořínek, Daniela Dušková, Jaroslav Feyereisl, Luboš Minář, Milada Kohoutová

Among gynaecological cancers, epithelial ovarian cancers are the most deadly cancers while endometrial cancers are the most common diseases. Efforts to establish relevant novel diagnostic, screening and prognostic markers are aimed to help reduce the high level of mortality, chemoresistance and recurrence, particularly in ovarian cancer. MicroRNAs, the class of post-transcriptional regulators, have emerged as the promising diagnostic and prognostic markers associated with various diseased states recently. Urine has been shown as the source of microRNAs several years ago; however, there has been lack of information on urine microRNA expression in ovarian and endometrial cancers till now. In this pilot study, we examined the expression of candidate cell-free urine microRNAs in ovarian cancer and endometrial cancer patients using quantitative real-time PCR. We compared the expression between pre- and post-surgery ovarian cancer samples, and between patients with ovarian and endometrial cancers and healthy controls, within three types of experiments. These experiments evaluated three different isolation methods of urine RNA, representing two supernatant and one exosome fractions of extracellular microRNA. In ovarian cancer, we found miR-92a significantly up-regulated, and miR-106b significantly down-regulated in comparison with control samples. In endometrial cancer, only miR-106b was found down-regulated significantly compared to control samples. Using exosome RNA, no significant de-regulations in microRNAs expression could be found in either of the cancers investigated. We propose that more research should now focus on confirming the diagnostic potential of urine microRNAs in gynaecological cancers using more clinical samples and large-scale expression profiling methods.