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Genome-wide analysis of NGS data to compile cancer-specific panels of miRNA biomarkers

MicroRNAs are small non-coding RNAs that influence gene expression by binding to the 3’ UTR of target mRNAs in order to repress protein synthesis. Soon after discovery, microRNA dysregulation has been associated to several pathologies. In particular, they have often been reported as differentially e...

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Autores principales: Bhowmick, Shib Sankar, Saha, Indrajit, Bhattacharjee, Debotosh, Genovese, Loredana M., Geraci, Filippo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6061989/
https://www.ncbi.nlm.nih.gov/pubmed/30048452
http://dx.doi.org/10.1371/journal.pone.0200353
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author Bhowmick, Shib Sankar
Saha, Indrajit
Bhattacharjee, Debotosh
Genovese, Loredana M.
Geraci, Filippo
author_facet Bhowmick, Shib Sankar
Saha, Indrajit
Bhattacharjee, Debotosh
Genovese, Loredana M.
Geraci, Filippo
author_sort Bhowmick, Shib Sankar
collection PubMed
description MicroRNAs are small non-coding RNAs that influence gene expression by binding to the 3’ UTR of target mRNAs in order to repress protein synthesis. Soon after discovery, microRNA dysregulation has been associated to several pathologies. In particular, they have often been reported as differentially expressed in healthy and tumor samples. This fact suggested that microRNAs are likely to be good candidate biomarkers for cancer diagnosis and personalized medicine. With the advent of Next-Generation Sequencing (NGS), measuring the expression level of the whole miRNAome at once is now routine. Yet, the collaborative effort of sharing data opens to the possibility of population analyses. This context motivated us to perform an in-silico study to distill cancer-specific panels of microRNAs that can serve as biomarkers. We observed that the problem of finding biomarkers can be modeled as a two-class classification task where, given the miRNAomes of a population of healthy and cancerous samples, we want to find the subset of microRNAs that leads to the highest classification accuracy. We fulfill this task leveraging on a sensible combination of data mining tools. In particular, we used: differential evolution for candidate selection, component analysis to preserve the relationships among miRNAs, and SVM for sample classification. We identified 10 cancer-specific panels whose classification accuracy is always higher than 92%. These panels have a very little overlap suggesting that miRNAs are not only predictive of the onset of cancer, but can be used for classification purposes as well. We experimentally validated the contribution of each of the employed tools to the selection of discriminating miRNAs. Moreover, we tested the significance of each panel for the corresponding cancer type. In particular, enrichment analysis showed that the selected miRNAs are involved in oncogenesis pathways, while survival analysis proved that miRNAs can be used to evaluate cancer severity. Summarizing: results demonstrated that our method is able to produce cancer-specific panels that are promising candidates for a subsequent in vitro validation.
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spelling pubmed-60619892018-08-03 Genome-wide analysis of NGS data to compile cancer-specific panels of miRNA biomarkers Bhowmick, Shib Sankar Saha, Indrajit Bhattacharjee, Debotosh Genovese, Loredana M. Geraci, Filippo PLoS One Research Article MicroRNAs are small non-coding RNAs that influence gene expression by binding to the 3’ UTR of target mRNAs in order to repress protein synthesis. Soon after discovery, microRNA dysregulation has been associated to several pathologies. In particular, they have often been reported as differentially expressed in healthy and tumor samples. This fact suggested that microRNAs are likely to be good candidate biomarkers for cancer diagnosis and personalized medicine. With the advent of Next-Generation Sequencing (NGS), measuring the expression level of the whole miRNAome at once is now routine. Yet, the collaborative effort of sharing data opens to the possibility of population analyses. This context motivated us to perform an in-silico study to distill cancer-specific panels of microRNAs that can serve as biomarkers. We observed that the problem of finding biomarkers can be modeled as a two-class classification task where, given the miRNAomes of a population of healthy and cancerous samples, we want to find the subset of microRNAs that leads to the highest classification accuracy. We fulfill this task leveraging on a sensible combination of data mining tools. In particular, we used: differential evolution for candidate selection, component analysis to preserve the relationships among miRNAs, and SVM for sample classification. We identified 10 cancer-specific panels whose classification accuracy is always higher than 92%. These panels have a very little overlap suggesting that miRNAs are not only predictive of the onset of cancer, but can be used for classification purposes as well. We experimentally validated the contribution of each of the employed tools to the selection of discriminating miRNAs. Moreover, we tested the significance of each panel for the corresponding cancer type. In particular, enrichment analysis showed that the selected miRNAs are involved in oncogenesis pathways, while survival analysis proved that miRNAs can be used to evaluate cancer severity. Summarizing: results demonstrated that our method is able to produce cancer-specific panels that are promising candidates for a subsequent in vitro validation. Public Library of Science 2018-07-26 /pmc/articles/PMC6061989/ /pubmed/30048452 http://dx.doi.org/10.1371/journal.pone.0200353 Text en © 2018 Bhowmick 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
Bhowmick, Shib Sankar
Saha, Indrajit
Bhattacharjee, Debotosh
Genovese, Loredana M.
Geraci, Filippo
Genome-wide analysis of NGS data to compile cancer-specific panels of miRNA biomarkers
title Genome-wide analysis of NGS data to compile cancer-specific panels of miRNA biomarkers
title_full Genome-wide analysis of NGS data to compile cancer-specific panels of miRNA biomarkers
title_fullStr Genome-wide analysis of NGS data to compile cancer-specific panels of miRNA biomarkers
title_full_unstemmed Genome-wide analysis of NGS data to compile cancer-specific panels of miRNA biomarkers
title_short Genome-wide analysis of NGS data to compile cancer-specific panels of miRNA biomarkers
title_sort genome-wide analysis of ngs data to compile cancer-specific panels of mirna biomarkers
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6061989/
https://www.ncbi.nlm.nih.gov/pubmed/30048452
http://dx.doi.org/10.1371/journal.pone.0200353
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