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Bioinformatics Analysis of Differentially Expressed Genes in Rotator Cuff Tear

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Bioinformatics Analysis of Differentially Expressed Genes in Rotator Cuff Tear Ren et al. Journal of Orthopaedic Surgery and Research (2018) 13:284 https://doi.org/10.1186/s13018-018-0989-5 RESEARCHARTICLE Open Access Bioinformatics analysis of differentially expressed genes in rotator cuff tear patients using microarray data Yi-Ming Ren†, Yuan-Hui Duan†, Yun-Bo Sun†, Tao Yang and Meng-Qiang Tian* 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. Keywords: Rotator cuff muscle, Satellite cells, Differentially expressed genes, Bioinformatics analysis, Calcium signaling, Denervation Introduction atrophy, denervation, and fatty infiltration, which may ex- The rotator cuff muscle complex of the shoulder is com- plain the progressive loss of function after an acute injury prised of four distinct muscles (supraspinatus, infraspina- and also the high rate of surgical failure. However, the tus, teres minor, and subscapularis), which controls underlying mechanism is not well understood. essential shoulder movements [1, 2]. The rotator cuff tear Satellite cells (SCs) are mitotically quiescent muscle (RCT) is a common cause of impact pain, nocturnal pain stem cells located between the basal lamina and the and shoulder joint dysfunction, which seriously affect the muscle membrane, which are known to play a key role life and working ability of patients, and reduce the quality in the adaptive response of muscle to exercise, and in of life of patients [3, 4]. Most tears require reparative sur- the maintenance of the regenerative capacity of muscle. gery; however, recurrence of tears following surgery is Hepatocyte growth factor (HGF) and nitric oxide (NO) common, with failure rates ranging from 30 to 94% [5]. could regulate transit of a SC from the quiescent G0 Rotator cuff tendon tears are accompanied by secondary state into the G1 (activated) stage of the cell cycle [6]. changes in the rotator cuff muscles, including muscle Recently, Deanna et al. discovered possible supraspinatus denervation in RCT and suggested NO-donor treatment combined with stretching has potential to promote * Correspondence: [email protected] †Yi-Ming Ren, Yuan-Hui Duan and Yun-Bo Sun contributed equally to this work. growth in atrophic supraspinatus muscle after RCT and Department of Joint and Sport Medicine, Tianjin Union Medical Center, Jieyuan improve functional outcome [7, 8]. Lundgreen et al. Road 190, Hongqiao District, Tianjin 300121, People’sRepublicofChina © The Author(s). 2018 Open Access This 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. Ren et al. Journal of Orthopaedic Surgery and Research (2018) 13:284 Page 2 of 9 showed patients with full-thickness tears had a reduced DEGs in torn SSP samples may participate in the pro- density of SCs, fewer proliferating cells, and atrophy of gression of RCT. Firstly, DEGs list was submitted to the myofibers [9]. With muscle atrophy, fatty infiltration Search Tool for the Retrieval of Interacting Genes into skeletal muscles is thought to cause muscle degen- (STRING) database (http://www.string-db.org/), and an eration by impairing the myogenic function of SCs [10]. interaction network chart with a combined score > 0.4 Here, we downloaded the gene expression profile was saved and exported. Subsequently, the interaction GSE93661 from the Gene Expression Omnibus database regulatory network of RCT-associated genes was visual- (GEO) and made bioinformatics analysis to investigate ized using Cytoscape software version 3.4.0. The distri- differentially expressed genes (DEGs) of SCs that regu- bution of core genes in the interaction network was lated between torn supraspinatus (SSP) samples and in- made by NetworkAnalyzer in Cytoscape. Then, the plu- tact subscapularis (SSC) samples from RCT patients. By gin Molecular Complex Detection (MCODE) was ap- doing this, we hope that the key target genes and path- plied to screen the modules of the gene interaction ways involved in the pathological process of RCT could network in Cytoscape. Venn diagram was drawn using be identified and existing molecular mechanisms could Venny 2.1 (http://bioinfogp.cnb.csic.es/tools/venny/). be revealed. Result Materials and methods Identification of DEGs Gene expression microarray data The gene expression profile GSE93661 was downloaded The gene expression profile GSE93661 was downloaded from the GEO, and the GEO2R method was used to iden- from the Gene Expression Omnibus (GEO, www.ncbi.nlm. tify DEGs in torn SSP samples compared with intact SSC nih.gov/geo/). GSE93661 was based on Agilent-026652 samples. P value < 0.05, log FC > 2.0, or log FC < − 2.0 Whole Human Genome Microarray 4x44K v2 platform. were used as the cut-off criteria. After analyzing, differen- GSE93661 dataset contained four samples, including two tially expression gene profiles were obtained. Totally, 551 torn SSP samples, and two intact SSC samples. DEGs were identified including 272 upregulated DEGs and 279 downregulated DEGs screened in torn SSP sam- DEGs in torn SSP and intact SSC samples ples compared with intact SSC samples. Parts of DEGs The raw data files used for the analysis included TXT were listed in Table 1. files. The analysis was carried out using GEO2R, which can perform comparisons on original submitter-supplied GO term enrichment analysis of DEGs processed data tables using the GEO query and limma R Functional annotation of the 551 DEGs was clarified packages from Bioconductor project. The P value < 0.05 using the DAVID 6.8 online tool. GO analysis indicated and log fold change (FC) > 2.0 or log FC < − 2.0 were that these DEGs were significantly enriched in muscle used as the cut-off criteria. The DEGs with statistical contraction, aging, regulation of ion transmembrane significance between the torn SSP samples and intact transport, mesenchymal cell development, and other SSC samples were selected and identified. biological processes (Fig. 1). For MF, the DEGs were enriched in ion channel activity, calcium ion binding, GO and KEGG analysis of DEGs structural molecule activity, and others. In addition, GO Target genes list were submitted to the DAVID 6.8 CC analysis also showed that the DEGs were signifi- (https://david.ncifcrf.gov/tools.jsp) and ClueGO version cantly enriched in keratin filament, integral component 2.33 (based on Cytoscape software version 3.4.0 (www.cy of plasma membrane, axon, cornified envelope, cortical toscape.org)) to identify overrepresented GO categories cytoskeleton, and others. and pathway categories. Gene ontology (GO) analysis was used to predict the potential functions of the DEGs in bio- KEGG pathway analysis of DEGs logical process (BP), molecular function (MF), and cellular The result of KEGG pathway analysis revealed that target component (CC). The Kyoto Encyclopedia of Genes and genes were enriched in butanoate metabolism, ABC Genomes (KEGG) is a knowledge base for systematic ana- transporters, notch signaling pathway, arachidonic acid lysis of gene functions, linking genomic information with metabolism, hedgehog signaling pathway, cell adhesion higher-level systemic functions. Finally, the overrepre- molecules (CAMs), prolactin signaling pathway, neuroac- sented pathway categories were considered statistically tive ligand-receptor interaction, dopaminergic synapse, significant using KEGG pathway enrichment analysis. GABAergic synapse, calcium signaling pathway, cGMP-PKG signaling pathway, drug metabolism, B cell Gene interaction network construction receptor signaling pathway, NF-kappa B signaling path- A large number of DEGs we obtained may be way, estrogen signaling pathway, cAMP signaling pathway, RCT-associated genes, and it is suggested that these and others. These key pathways were showed
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