Cargando…
Data analysis algorithm for the development of extracellular miRNA-based diagnostic systems for prostate cancer
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 miRN...
Autores principales: | , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6457524/ https://www.ncbi.nlm.nih.gov/pubmed/30970027 http://dx.doi.org/10.1371/journal.pone.0215003 |
_version_ | 1783409912267669504 |
---|---|
author | 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. |
author_facet | 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. |
author_sort | Bryzgunova, O. E. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6457524 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-64575242019-05-03 Data analysis algorithm for the development of extracellular miRNA-based diagnostic systems for prostate cancer 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. PLoS One Research Article 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. Public Library of Science 2019-04-10 /pmc/articles/PMC6457524/ /pubmed/30970027 http://dx.doi.org/10.1371/journal.pone.0215003 Text en © 2019 Bryzgunova et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article 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. Data analysis algorithm for the development of extracellular miRNA-based diagnostic systems for prostate cancer |
title | Data analysis algorithm for the development of extracellular miRNA-based diagnostic systems for prostate cancer |
title_full | Data analysis algorithm for the development of extracellular miRNA-based diagnostic systems for prostate cancer |
title_fullStr | Data analysis algorithm for the development of extracellular miRNA-based diagnostic systems for prostate cancer |
title_full_unstemmed | Data analysis algorithm for the development of extracellular miRNA-based diagnostic systems for prostate cancer |
title_short | Data analysis algorithm for the development of extracellular miRNA-based diagnostic systems for prostate cancer |
title_sort | data analysis algorithm for the development of extracellular mirna-based diagnostic systems for prostate cancer |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6457524/ https://www.ncbi.nlm.nih.gov/pubmed/30970027 http://dx.doi.org/10.1371/journal.pone.0215003 |
work_keys_str_mv | AT bryzgunovaoe dataanalysisalgorithmforthedevelopmentofextracellularmirnabaseddiagnosticsystemsforprostatecancer AT zaporozhchenkoia dataanalysisalgorithmforthedevelopmentofextracellularmirnabaseddiagnosticsystemsforprostatecancer AT lekchnovea dataanalysisalgorithmforthedevelopmentofextracellularmirnabaseddiagnosticsystemsforprostatecancer AT amelinaev dataanalysisalgorithmforthedevelopmentofextracellularmirnabaseddiagnosticsystemsforprostatecancer AT konoshenkomyu dataanalysisalgorithmforthedevelopmentofextracellularmirnabaseddiagnosticsystemsforprostatecancer AT yarmoschuksv dataanalysisalgorithmforthedevelopmentofextracellularmirnabaseddiagnosticsystemsforprostatecancer AT pashkovskayaoa dataanalysisalgorithmforthedevelopmentofextracellularmirnabaseddiagnosticsystemsforprostatecancer AT zheravinaa dataanalysisalgorithmforthedevelopmentofextracellularmirnabaseddiagnosticsystemsforprostatecancer AT paksv dataanalysisalgorithmforthedevelopmentofextracellularmirnabaseddiagnosticsystemsforprostatecancer AT rykovaeyu dataanalysisalgorithmforthedevelopmentofextracellularmirnabaseddiagnosticsystemsforprostatecancer AT laktionovpp dataanalysisalgorithmforthedevelopmentofextracellularmirnabaseddiagnosticsystemsforprostatecancer |