Data analysis algorithm for the development of extracellular miRNA-based diagnostic systems for prostate cancer
| Autor: | M. Yu. Konoshenko, S. V. Yarmoschuk, O. A. Pashkovskaya, E. A. Lekchnov, A. A. Zheravin, E. V. Amelina, Ivan A. Zaporozhchenko, Pavel P. Laktionov, Svetlana V. Pak, E. Yu. Rykova, O. E. Bryzgunova |
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| Rok vydání: | 2019 |
| Předmět: |
Male
Science Prostatic Hyperplasia Urine Biology Diagnostic system Extracellular vesicles Extracellular Vesicles Prostate cancer Text mining microRNA Biomarkers Tumor Extracellular medicine Humans Gene Regulatory Networks Aged Aged 80 and over Multidisciplinary business.industry Prostatic Neoplasms Middle Aged medicine.disease MicroRNAs Expression data Case-Control Studies Data Interpretation Statistical Medicine business Algorithm Algorithms |
| Zdroj: | PLoS ONE, Vol 14, Iss 4, p e0215003 (2019) |
| ISSN: | 1932-6203 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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