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MicroRNA expression profiling and bioinformatics analysis of dysregulated microRNAs in obstructive sleep apnea patients

Obstructive sleep apnea (OSA) is a common chronic obstructive sleep disease in clinic. The purpose of our study was to use bioinformatics analysis to identify microRNAs (miRNAs) that are differentially expressed between OSA patients and healthy controls. Serum samples were collected from OSA patient...

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Autores principales: Li, Kun, Wei, Peng, Qin, Yanwen, Wei, Yongxiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer Health 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5572039/
https://www.ncbi.nlm.nih.gov/pubmed/28834917
http://dx.doi.org/10.1097/MD.0000000000007917
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author Li, Kun
Wei, Peng
Qin, Yanwen
Wei, Yongxiang
author_facet Li, Kun
Wei, Peng
Qin, Yanwen
Wei, Yongxiang
author_sort Li, Kun
collection PubMed
description Obstructive sleep apnea (OSA) is a common chronic obstructive sleep disease in clinic. The purpose of our study was to use bioinformatics analysis to identify microRNAs (miRNAs) that are differentially expressed between OSA patients and healthy controls. Serum samples were collected from OSA patients and healthy controls. To better reveal the sample specificity of differentially expressed microRNAs, supervised hierarchical clustering was conducted. We used the microT-CDS and TargetScan databases to predict target genes of the differentially expressed microRNAs and selected the common genes. The Search Tool for the Retrieval of Interacting Genes (STRING) was used to evaluate many coexpression relationships. Moreover, we used these potential microRNA-target pairs and coexpression relationships to construct a regulatory coexpression network using Cytoscape software. Functional analysis of microRNA target genes was conducted with FunRich. A total of 104 microRNAs that were differentially expressed between OSA patients and healthy controls were identified. Supervised hierarchical clustering was conducted based on the expression of the 104 microRNAs in the OSA patients and healthy controls. Overall, 6621 potential target genes were predicted, and 119 target genes were screened based on coexpression coefficients in the STRING database. A regulatory coexpression network was constructed that included 23 differentially expressed microRNAs and 18 of the most related potential target genes. Metabolic signaling pathways were the most highly enriched category. Differentially expressed microRNAs, such as hsa-miR-485-5p, hsa-miR-107, hsa-miR-574-5p, and hsa-miR-199-3p, might participate in OSA. The target gene CAD might also be closely related to OSA. Our results may provide a basis for the pathogenesis of OSA and the study of disease diagnosis, prevention, and treatment. However, more experiments are needed to verify these predictions.
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spelling pubmed-55720392017-09-06 MicroRNA expression profiling and bioinformatics analysis of dysregulated microRNAs in obstructive sleep apnea patients Li, Kun Wei, Peng Qin, Yanwen Wei, Yongxiang Medicine (Baltimore) 6000 Obstructive sleep apnea (OSA) is a common chronic obstructive sleep disease in clinic. The purpose of our study was to use bioinformatics analysis to identify microRNAs (miRNAs) that are differentially expressed between OSA patients and healthy controls. Serum samples were collected from OSA patients and healthy controls. To better reveal the sample specificity of differentially expressed microRNAs, supervised hierarchical clustering was conducted. We used the microT-CDS and TargetScan databases to predict target genes of the differentially expressed microRNAs and selected the common genes. The Search Tool for the Retrieval of Interacting Genes (STRING) was used to evaluate many coexpression relationships. Moreover, we used these potential microRNA-target pairs and coexpression relationships to construct a regulatory coexpression network using Cytoscape software. Functional analysis of microRNA target genes was conducted with FunRich. A total of 104 microRNAs that were differentially expressed between OSA patients and healthy controls were identified. Supervised hierarchical clustering was conducted based on the expression of the 104 microRNAs in the OSA patients and healthy controls. Overall, 6621 potential target genes were predicted, and 119 target genes were screened based on coexpression coefficients in the STRING database. A regulatory coexpression network was constructed that included 23 differentially expressed microRNAs and 18 of the most related potential target genes. Metabolic signaling pathways were the most highly enriched category. Differentially expressed microRNAs, such as hsa-miR-485-5p, hsa-miR-107, hsa-miR-574-5p, and hsa-miR-199-3p, might participate in OSA. The target gene CAD might also be closely related to OSA. Our results may provide a basis for the pathogenesis of OSA and the study of disease diagnosis, prevention, and treatment. However, more experiments are needed to verify these predictions. Wolters Kluwer Health 2017-08-25 /pmc/articles/PMC5572039/ /pubmed/28834917 http://dx.doi.org/10.1097/MD.0000000000007917 Text en Copyright © 2017 the Author(s). Published by Wolters Kluwer Health, Inc. http://creativecommons.org/licenses/by-nd/4.0 This is an open access article distributed under the Creative Commons Attribution-NoDerivatives License 4.0, which allows for redistribution, commercial and non-commercial, as long as it is passed along unchanged and in whole, with credit to the author. http://creativecommons.org/licenses/by-nd/4.0
spellingShingle 6000
Li, Kun
Wei, Peng
Qin, Yanwen
Wei, Yongxiang
MicroRNA expression profiling and bioinformatics analysis of dysregulated microRNAs in obstructive sleep apnea patients
title MicroRNA expression profiling and bioinformatics analysis of dysregulated microRNAs in obstructive sleep apnea patients
title_full MicroRNA expression profiling and bioinformatics analysis of dysregulated microRNAs in obstructive sleep apnea patients
title_fullStr MicroRNA expression profiling and bioinformatics analysis of dysregulated microRNAs in obstructive sleep apnea patients
title_full_unstemmed MicroRNA expression profiling and bioinformatics analysis of dysregulated microRNAs in obstructive sleep apnea patients
title_short MicroRNA expression profiling and bioinformatics analysis of dysregulated microRNAs in obstructive sleep apnea patients
title_sort microrna expression profiling and bioinformatics analysis of dysregulated micrornas in obstructive sleep apnea patients
topic 6000
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5572039/
https://www.ncbi.nlm.nih.gov/pubmed/28834917
http://dx.doi.org/10.1097/MD.0000000000007917
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