Omics study of ovarian malignancies: from urine metabolomic profile to minimally invasive microrna markers

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Abstract

One of the current trends in oncogynecology is the search for effective biomarkers of ovarian cancer. Metabolic profiling by ultra-high performance liquid chromatography and mass spectrometry allows obtaining information about the totality of all low molecular weight metabolites of patient's biological fluids sample. These metabolites can become potential disease markers, while their combination with microRNA level data significantly increases the diagnostic value. Therefore, the aim of the study was to analyze the metabolomic profile and microRNA transcripts level in urine of serous ovarian adenocarcinoma patients to identify potential non-invasive diagnostic markers of the disease. The study included 60 patients diagnosed with serous ovarian adenocarcinoma and 20 individuals without cancer history. Chromatographic separation of metabolites was performed on a Vanquish Flex UHPLC System chromatograph coupled to an Orbitrap Exploris 480 mass spectrometer. The search for gene regulators of metabolites and microRNA regulators of genes was carried out using the Random forest machine learning method. The microRNA transcripts level in urine was determined by real-time PCR. LASSO-penalized logistic regression was used to build predictive models. In patients with ovarian cancer, 26 compounds had an abnormal concentration compared to the control group (kynurenine, phenylalanyl-valine, lysophosphatidylcholines 18:3, 18:2, 20:4 and 14:0, alanyl-leucine, L-phenylalanine, phosphatidylinositol (34:1), 5-methoxytryptophan, 2-hydroxymyristic acid, 3-oxocholic acid, indoleacrylic acid, lysophosphatidylserine (20:4), L-beta-aspartyl-L-phenylalanine, myristic acid, decanoylcarnitine, aspartyl-glycine, malonylcarnitine, 3-hydroxybutyrylcarnitine, 3-methylxanthine, 2,6 dimethylheptanoylcarnitine, 3-oxododecanoic acid, N-acetylproline, L-octanoylcarnitine and capryloylglycine). Using the Random forest method, metabolite-gene regulator (47 genes) and metabolite-microRNA regulator (613 unique microRNA) relationships were established. The identified 85 microRNAs were validated by real-time PCR. Changes in the levels of miR-382-5p, miR-593-3p, miR-29a-5p, miR-2110, miR-30c-5p, miR-181a-5p, let-7b-5p, miR-27a-3p transcripts were detected. 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 with ovarian cancer relative to the control group was found. Thus, in ovarian serous adenocarcinoma patients a significant metabolomic imbalance of urine was found associated with changes in the levels of microRNAs that regulate the signaling pathways of these metabolites. At the same time, 26 compounds with abnormal concentration and levels of microRNA transcripts miR-33b-5p, miR-423-5p, miR-6843-3p, miR-4668-3p, miR-30c-5p, miR-6743-5p, miR-4742-5p, miR-1207-5p and miR-17-5p in urine can serve as non-invasive diagnostic markers for ovarian cancer.

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About the authors

D. S. Kutilin

National Medical Research Center of Oncology, Ministry of Health of the Russian Federation

Author for correspondence.
Email: k.denees@yandex.ru
Russian Federation, Rostov-on-Don

O. N. Guskova

National Medical Research Center of Oncology, Ministry of Health of the Russian Federation

Email: k.denees@yandex.ru
Russian Federation, Rostov-on-Don

F. E. Filippov

National Medical Research Center of Oncology, Ministry of Health of the Russian Federation

Email: k.denees@yandex.ru
Russian Federation, Rostov-on-Don

A. Yu. Maksimov

National Medical Research Center of Oncology, Ministry of Health of the Russian Federation

Email: k.denees@yandex.ru
Russian Federation, Rostov-on-Don

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Supplementary files

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2. Fig. 1. Design of a prospective study of ovarian cancer markers. *Ultra-high performance liquid chromatography and mass spectrometry.

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3. Fig. 2. Distribution of patients with ovarian cancer and conditionally healthy women by age.

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4. Fig. 3. Changes in the content of fatty acids and their derivatives, as well as acylcarnitines in urine in serous ovarian adenocarcinoma. Here and in Fig. 4, 5, the symbol * indicates a statistically significant (p < 0.05) change in the level of metabolites compared to the control group.

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5. Fig. 4. Changes in the concentration of phospholipids, amino acids and their derivatives in the urine of patients with serous ovarian adenocarcinoma.

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6. Fig. 5. Changes in the content of nitrogenous base derivatives and steroids in the urine of patients with serous ovarian adenocarcinoma.

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7. Fig. 6. Changes in the level of 85 microRNA transcripts in the urine of patients with serous ovarian adenocarcinoma. The symbol “*” indicates a statistically significant (p < 0.005) change in the level of transcripts compared to the control.

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8. Fig. 7. Changes in the level of microRNA transcripts in the urine of patients with serous ovarian adenocarcinoma of varying degrees of malignancy. The symbol “*” indicates a statistically significant (p < 0.005) difference in the level of transcripts in the urine in OC from the values ​​in the control group; ** — statistically significant changes in the level of transcripts (p < 0.005) between the groups.

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9. Fig. 8. Comparison of microRNA sets.

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10. Fig. 9. Ratio of the transcript levels of 92 microRNAs in the urine of patients with serous ovarian adenocarcinoma and conditionally healthy donors. Statistically significant (p < 0.05) changes in the transcript levels are marked with the symbol *.

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11. Fig. 10. ROC curves for classification of groups of patients with ovarian cancer (solid line) and conditionally healthy (dashed line) by the level of microRNA transcripts in urine. ROC curves with AUC ≥ 0.70 are presented.

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12. Fig. 11. LASSO-penalized logistic regression model of urinary microRNA transcript levels. a — Distribution of regression coefficients in bootstrap datasets. b — Importance of variables in bootstrap models. c — ROC curves for classifying samples using the optimized (continuous line) and non-optimized (dashed line) models.

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13. Fig. 12. Main nodes of myristic acid metabolism.

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14. Fig. 13. Scheme of oxododecanoic acid metabolism.

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15. Additional material
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