Data analysis algorithm for the development of extracellular miRNA-based diagnostic systems for prostate cancer

• Published in: PloS one Vol. 14; no. 4; p. e0215003
• Main Authors: Bryzgunova, O. E., Zaporozhchenko, I. A., Lekchnov, E. A., Amelina, E. V., Konoshenko, M. Yu, Yarmoschuk, S. V., Pashkovskaya, O. A., Zheravin, A. A., Pak, S. V., Rykova, E. Yu, Laktionov, P. P.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 10.04.2019; Public Library of Science (PLoS)
• Subjects: Aged; Aged, 80 and over; Algorithms; Biology and life sciences; Biomarkers, Tumor - genetics; Biomarkers, Tumor - urine; Cancer cells; Care and treatment; Case-Control Studies; Data Interpretation, Statistical; Development and progression; Diagnosis; Extracellular Vesicles - genetics; Gene expression; Gene Regulatory Networks; Humans; Information management; Male; Medicine and Health Sciences; MicroRNA; MicroRNAs - genetics; MicroRNAs - urine; Middle Aged; Physical Sciences; Prostate cancer; Prostatic Hyperplasia - diagnosis; Prostatic Hyperplasia - genetics; Prostatic Hyperplasia - urine; Prostatic Neoplasms - diagnosis; Prostatic Neoplasms - genetics; Prostatic Neoplasms - urine; Research and Analysis Methods; Tumors
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0215003

Urine of prostate cancer (PCa) carries miRNAs originated from prostate cancer cells as a part of both nucleoprotein complexes and cell-secreted extracellular vesicles. The analysis of such miRNA-markers in urine can be a convenient option for PCa screening. The aims of this study were to reveal miRNA-markers of PCa in urine and design a robust and precise diagnostic test, based on miRNA expression analysis. The expression analysis of the 84 miRNAs in paired urine extracellular vesicles (EVs) and cell free urine supernatant samples from healthy donors, patients with benign and malignant prostate tumours was done using miRCURY LNA miRNA qPCR Panels (Exiqon, Denmark). Sets of miRNAs differentially expressed between the donor groups were found in urine EVs and urine supernatant. Diagnostically significant miRNAs were selected and algorithm of data analysis, based on expression data on 24-miRNA in urine and obtained using 17 analytical systems, was designed. The developed algorithm of data analysis describes a series of steps necessary to define cut-off values and sequentially analyze miRNA expression data according to the cut-offs to facilitate classification of subjects in case/control groups and allows to detect PCa patients with 97.5% accuracy.