Bioinformatics analysis of differentially expressed genes in rotator cuff tear patients using microarray data
Abstract 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 capaci...
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| Format: | Article |
| Language: | English |
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BMC
2018-11-01
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| Series: | Journal of Orthopaedic Surgery and Research |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13018-018-0989-5 |
| _version_ | 1811213006648901632 |
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| author | Yi-Ming Ren Yuan-Hui Duan Yun-Bo Sun Tao Yang Meng-Qiang Tian |
| author_facet | Yi-Ming Ren Yuan-Hui Duan Yun-Bo Sun Tao Yang Meng-Qiang Tian |
| author_sort | Yi-Ming Ren |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-12T05:38:54Z |
| format | Article |
| id | doaj.art-86c51e0070c349929585278ff1ed2eb3 |
| institution | Directory Open Access Journal |
| issn | 1749-799X |
| language | English |
| last_indexed | 2024-04-12T05:38:54Z |
| publishDate | 2018-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Orthopaedic Surgery and Research |
| spelling | doaj.art-86c51e0070c349929585278ff1ed2eb32022-12-22T03:45:44ZengBMCJournal of Orthopaedic Surgery and Research1749-799X2018-11-011311910.1186/s13018-018-0989-5Bioinformatics analysis of differentially expressed genes in rotator cuff tear patients using microarray dataYi-Ming Ren0Yuan-Hui Duan1Yun-Bo Sun2Tao Yang3Meng-Qiang Tian4Department of Joint and Sport Medicine, Tianjin Union Medical CenterDepartment of Joint and Sport Medicine, Tianjin Union Medical CenterDepartment of Joint and Sport Medicine, Tianjin Union Medical CenterDepartment of Joint and Sport Medicine, Tianjin Union Medical CenterDepartment of Joint and Sport Medicine, Tianjin Union Medical CenterAbstract 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.http://link.springer.com/article/10.1186/s13018-018-0989-5Rotator cuff muscleSatellite cellsDifferentially expressed genesBioinformatics analysisCalcium signalingDenervation |
| spellingShingle | Yi-Ming Ren Yuan-Hui Duan Yun-Bo Sun Tao Yang Meng-Qiang Tian Bioinformatics analysis of differentially expressed genes in rotator cuff tear patients using microarray data Journal of Orthopaedic Surgery and Research Rotator cuff muscle Satellite cells Differentially expressed genes Bioinformatics analysis Calcium signaling Denervation |
| 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 | Rotator cuff muscle Satellite cells Differentially expressed genes Bioinformatics analysis Calcium signaling Denervation |
| url | http://link.springer.com/article/10.1186/s13018-018-0989-5 |
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