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Bioinformatics analysis of differentially expressed genes in rotator cuff tear patients using microarray data

BACKGROUND: Rotator cuff tear (RCT) is a common shoulder disorder in the elderly. Muscle atrophy, denervation and fatty infiltration exert secondary injuries on torn rotator cuff muscles. It has been reported that satellite cells (SCs) play roles in pathogenic process and regenerative capacity of hu...

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Autores principales: Ren, Yi-Ming, Duan, Yuan-Hui, Sun, Yun-Bo, Yang, Tao, Tian, Meng-Qiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6234628/
https://www.ncbi.nlm.nih.gov/pubmed/30424787
http://dx.doi.org/10.1186/s13018-018-0989-5
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author Ren, Yi-Ming
Duan, Yuan-Hui
Sun, Yun-Bo
Yang, Tao
Tian, Meng-Qiang
author_facet Ren, Yi-Ming
Duan, Yuan-Hui
Sun, Yun-Bo
Yang, Tao
Tian, Meng-Qiang
author_sort Ren, Yi-Ming
collection PubMed
description BACKGROUND: Rotator cuff tear (RCT) is a common shoulder disorder in the elderly. Muscle atrophy, denervation and fatty infiltration exert secondary injuries on torn rotator cuff muscles. It has been reported that satellite cells (SCs) play roles in pathogenic process and regenerative capacity of human RCT via regulating of target genes. This study aims to complement the differentially expressed genes (DEGs) of SCs that regulated between the torn supraspinatus (SSP) samples and intact subscapularis (SSC) samples, identify their functions and molecular pathways. METHODS: The gene expression profile GSE93661 was downloaded and bioinformatics analysis was made. RESULTS: Five hundred fifty one DEGs totally were identified. Among them, 272 DEGs were overexpressed, and the remaining 279 DEGs were underexpressed. Gene ontology (GO) and pathway enrichment analysis of target genes were performed. We furthermore identified some relevant core genes using gene–gene interaction network analysis such as GNG13, GCG, NOTCH1, BCL2, NMUR2, PMCH, FFAR1, AVPR2, GNA14, and KALRN, that may contribute to the understanding of the molecular mechanisms of secondary injuries in RCT. We also discovered that GNG13/calcium signaling pathway is highly correlated with the denervation atrophy pathological process of RCT. CONCLUSION: These genes and pathways provide a new perspective for revealing the underlying pathological mechanisms and therapy strategy of RCT.
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spelling pubmed-62346282018-11-23 Bioinformatics analysis of differentially expressed genes in rotator cuff tear patients using microarray data Ren, Yi-Ming Duan, Yuan-Hui Sun, Yun-Bo Yang, Tao Tian, Meng-Qiang J Orthop Surg Res Research Article BACKGROUND: Rotator cuff tear (RCT) is a common shoulder disorder in the elderly. Muscle atrophy, denervation and fatty infiltration exert secondary injuries on torn rotator cuff muscles. It has been reported that satellite cells (SCs) play roles in pathogenic process and regenerative capacity of human RCT via regulating of target genes. This study aims to complement the differentially expressed genes (DEGs) of SCs that regulated between the torn supraspinatus (SSP) samples and intact subscapularis (SSC) samples, identify their functions and molecular pathways. METHODS: The gene expression profile GSE93661 was downloaded and bioinformatics analysis was made. RESULTS: Five hundred fifty one DEGs totally were identified. Among them, 272 DEGs were overexpressed, and the remaining 279 DEGs were underexpressed. Gene ontology (GO) and pathway enrichment analysis of target genes were performed. We furthermore identified some relevant core genes using gene–gene interaction network analysis such as GNG13, GCG, NOTCH1, BCL2, NMUR2, PMCH, FFAR1, AVPR2, GNA14, and KALRN, that may contribute to the understanding of the molecular mechanisms of secondary injuries in RCT. We also discovered that GNG13/calcium signaling pathway is highly correlated with the denervation atrophy pathological process of RCT. CONCLUSION: These genes and pathways provide a new perspective for revealing the underlying pathological mechanisms and therapy strategy of RCT. BioMed Central 2018-11-13 /pmc/articles/PMC6234628/ /pubmed/30424787 http://dx.doi.org/10.1186/s13018-018-0989-5 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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 Article
Ren, Yi-Ming
Duan, Yuan-Hui
Sun, Yun-Bo
Yang, Tao
Tian, Meng-Qiang
Bioinformatics analysis of differentially expressed genes in rotator cuff tear patients using microarray data
title Bioinformatics analysis of differentially expressed genes in rotator cuff tear patients using microarray data
title_full Bioinformatics analysis of differentially expressed genes in rotator cuff tear patients using microarray data
title_fullStr Bioinformatics analysis of differentially expressed genes in rotator cuff tear patients using microarray data
title_full_unstemmed Bioinformatics analysis of differentially expressed genes in rotator cuff tear patients using microarray data
title_short Bioinformatics analysis of differentially expressed genes in rotator cuff tear patients using microarray data
title_sort bioinformatics analysis of differentially expressed genes in rotator cuff tear patients using microarray data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6234628/
https://www.ncbi.nlm.nih.gov/pubmed/30424787
http://dx.doi.org/10.1186/s13018-018-0989-5
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