Urine transcriptomic profile in terms of malignant ovarian tumors

Purpose of the study. Bioinformatic search for transcriptomic markers (based on metabolomic data) and their validation in the urine of serous ovarian adenocarcinoma patients.

Materials and methods. The study included 70 patients with serous ovarian adenocarcinoma and 30 conditionally healthy individuals. The search for metabolite regulator genes and gene regulator microRNAs was performed using the Random forest machine learning method. Ribonucleic acid (RNA) was isolated using the RNeasy Plus Universal Kits. The level of microRNA transcripts in urine was determined by real-time PCR. Differences were assessed using the Mann-Whitney test with Bonferroni correction.

Results. Using the Random forest method, metabolite-regulator gene (47 genes) and metabolite-regulator microRNA (613 unique microRNA) relationships were established. The identified microRNAs were validated by real-time PCR. Changes in the levels of microRNA transcripts were detected: miR-382-5p, miR-593-3p, miR-29a-5p, miR-2110, miR-30c-5p, miR-181a-5p, let-7b-5p, miR-27a-3p, miR-370-3p, miR-6529-5p, miR-653-5p, miR-4742-5p, miR-2467-3p, miR-1909-5p, miR-6743-5p, miR-875-3p, miR-19a-3p, miR-208a-5p, miR-330-5p, miR-1207-5p, miR-4668-3p, miR-3193, miR-23a-3p, miR-12132, miR-765, miR-181b-5p, miR-4529-3p, miR-33b-5p, miR-17-5p, miR-6866-3p, miR-4753-5p, miR-103a-3p, miR-423-5p, miR-491-5p, miR-196b-5p, miR-6843-3p, miR-423-5p and miR-3184-5p in the urine of patients compared to conditionally healthy individuals.

Conclusion. Thus, urine transcriptome profiling allowed both to identify potential disease markers and to better understand the molecular mechanisms of changes underlying ovarian cancer development.

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