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Analysis of miRNA expression profiles in breast cancer using biclustering
BACKGROUND: MicroRNAs (miRNAs) are important key regulators in multiple cellular functions, due to their a crucial role in different physiological processes. MiRNAs are differentially expressed in specific tissues, during specific cell status, or in different diseases as tumours. RNA sequencing (RNA...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4347621/ https://www.ncbi.nlm.nih.gov/pubmed/25734576 http://dx.doi.org/10.1186/1471-2105-16-S4-S7 |
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author | Fiannaca, Antonino La Rosa, Massimo La Paglia, Laura Rizzo, Riccardo Urso, Alfonso |
author_facet | Fiannaca, Antonino La Rosa, Massimo La Paglia, Laura Rizzo, Riccardo Urso, Alfonso |
author_sort | Fiannaca, Antonino |
collection | PubMed |
description | BACKGROUND: MicroRNAs (miRNAs) are important key regulators in multiple cellular functions, due to their a crucial role in different physiological processes. MiRNAs are differentially expressed in specific tissues, during specific cell status, or in different diseases as tumours. RNA sequencing (RNA-seq) is a Next Generation Sequencing (NGS) method for the analysis of differential gene expression. Using machine learning algorithms, it is possible to improve the functional significance interpretation of miRNA in the analysis and interpretation of data from RNA-seq. Furthermore, we tried to identify some patterns of deregulated miRNA in human breast cancer (BC), in order to give a contribution in the understanding of this type of cancer at the molecular level. RESULTS: We adopted a biclustering approach, using the Iterative Signature Algorithm (ISA) algorithm, in order to evaluate miRNA deregulation in the context of miRNA abundance and tissue heterogeneity. These are important elements to identify miRNAs that would be useful as prognostic and diagnostic markers. Considering a real word breast cancer dataset, the evaluation of miRNA differential expressions in tumours versus healthy tissues evidenced 12 different miRNA clusters, associated to specific groups of patients. The identified miRNAs were deregulated in breast tumours compared to healthy controls. Our approach has shown the association between specific sub-class of tumour samples having the same immuno-histo-chemical and/or histological features. Biclusters have been validated by means of two online repositories, MetaMirClust database and UCSC Genome Browser, and using another biclustering algorithm. CONCLUSIONS: The obtained results with biclustering algorithm aimed first of all to give a contribute in the differential expression analysis in a cohort of BC patients and secondly to support the potential role that these non-coding RNA molecules could play in the clinical practice, in terms of prognosis, evolution of tumour and treatment response. |
format | Online Article Text |
id | pubmed-4347621 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-43476212015-03-19 Analysis of miRNA expression profiles in breast cancer using biclustering Fiannaca, Antonino La Rosa, Massimo La Paglia, Laura Rizzo, Riccardo Urso, Alfonso BMC Bioinformatics Research BACKGROUND: MicroRNAs (miRNAs) are important key regulators in multiple cellular functions, due to their a crucial role in different physiological processes. MiRNAs are differentially expressed in specific tissues, during specific cell status, or in different diseases as tumours. RNA sequencing (RNA-seq) is a Next Generation Sequencing (NGS) method for the analysis of differential gene expression. Using machine learning algorithms, it is possible to improve the functional significance interpretation of miRNA in the analysis and interpretation of data from RNA-seq. Furthermore, we tried to identify some patterns of deregulated miRNA in human breast cancer (BC), in order to give a contribution in the understanding of this type of cancer at the molecular level. RESULTS: We adopted a biclustering approach, using the Iterative Signature Algorithm (ISA) algorithm, in order to evaluate miRNA deregulation in the context of miRNA abundance and tissue heterogeneity. These are important elements to identify miRNAs that would be useful as prognostic and diagnostic markers. Considering a real word breast cancer dataset, the evaluation of miRNA differential expressions in tumours versus healthy tissues evidenced 12 different miRNA clusters, associated to specific groups of patients. The identified miRNAs were deregulated in breast tumours compared to healthy controls. Our approach has shown the association between specific sub-class of tumour samples having the same immuno-histo-chemical and/or histological features. Biclusters have been validated by means of two online repositories, MetaMirClust database and UCSC Genome Browser, and using another biclustering algorithm. CONCLUSIONS: The obtained results with biclustering algorithm aimed first of all to give a contribute in the differential expression analysis in a cohort of BC patients and secondly to support the potential role that these non-coding RNA molecules could play in the clinical practice, in terms of prognosis, evolution of tumour and treatment response. BioMed Central 2015-02-23 /pmc/articles/PMC4347621/ /pubmed/25734576 http://dx.doi.org/10.1186/1471-2105-16-S4-S7 Text en Copyright © 2015 Fiannaca et al.; licensee BioMed Central Ltd. 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 work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Fiannaca, Antonino La Rosa, Massimo La Paglia, Laura Rizzo, Riccardo Urso, Alfonso Analysis of miRNA expression profiles in breast cancer using biclustering |
title | Analysis of miRNA expression profiles in breast cancer using biclustering |
title_full | Analysis of miRNA expression profiles in breast cancer using biclustering |
title_fullStr | Analysis of miRNA expression profiles in breast cancer using biclustering |
title_full_unstemmed | Analysis of miRNA expression profiles in breast cancer using biclustering |
title_short | Analysis of miRNA expression profiles in breast cancer using biclustering |
title_sort | analysis of mirna expression profiles in breast cancer using biclustering |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4347621/ https://www.ncbi.nlm.nih.gov/pubmed/25734576 http://dx.doi.org/10.1186/1471-2105-16-S4-S7 |
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