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<front>
<journal-meta>
<journal-id journal-id-type="nlm-ta">Molecular Medicine Reports</journal-id>
<journal-title-group>
<journal-title>Molecular Medicine Reports</journal-title></journal-title-group>
<issn pub-type="ppub">1791-2997</issn>
<issn pub-type="epub">1791-3004</issn>
<publisher>
<publisher-name>D.A. Spandidos</publisher-name></publisher></journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3892/mmr.2016.5703</article-id>
<article-id pub-id-type="publisher-id">mmr-14-04-3785</article-id>
<article-categories>
<subj-group>
<subject>Articles</subject></subj-group></article-categories>
<title-group>
<article-title>Bioinformatics analysis of gene expression profiles of dermatomyositis</article-title></title-group>
<contrib-group>
<contrib contrib-type="author">
<name><surname>Chen</surname><given-names>Liang-Yuan</given-names></name><xref rid="af1-mmr-14-04-3785" ref-type="aff">1</xref></contrib>
<contrib contrib-type="author">
<name><surname>Cui</surname><given-names>Zhao-Lei</given-names></name><xref rid="af2-mmr-14-04-3785" ref-type="aff">2</xref></contrib>
<contrib contrib-type="author">
<name><surname>Hua</surname><given-names>Fan-Cui</given-names></name><xref rid="af1-mmr-14-04-3785" ref-type="aff">1</xref></contrib>
<contrib contrib-type="author">
<name><surname>Yang</surname><given-names>Weng-Jing</given-names></name><xref rid="af1-mmr-14-04-3785" ref-type="aff">1</xref></contrib>
<contrib contrib-type="author">
<name><surname>Bai</surname><given-names>Ye</given-names></name><xref rid="af1-mmr-14-04-3785" ref-type="aff">1</xref></contrib>
<contrib contrib-type="author">
<name><surname>Lan</surname><given-names>Feng-Hua</given-names></name><xref rid="af1-mmr-14-04-3785" ref-type="aff">1</xref><xref ref-type="corresp" rid="c1-mmr-14-04-3785"/></contrib></contrib-group>
<aff id="af1-mmr-14-04-3785">
<label>1</label>Department of Clinical Genetics and Experimental Medicine, Fuzong Clinical School, Fujian Medical University, Fuzhou, Fujian 350025</aff>
<aff id="af2-mmr-14-04-3785">
<label>2</label>Department of Clinical Laboratory, Fujian Provincial Tumor Hospital, Fuzhou, Fujian 350014, P.R. China</aff>
<author-notes>
<corresp id="c1-mmr-14-04-3785">Correspondence to: Dr Feng-Hua Lan, Department of Clinical Genetics and Experimental Medicine, Fuzong Clinical School, Fujian Medical University, 156 Xier Huan Road, Gulou, Fuzhou, Fujian 350025, P.R. China, E-mail: <email>fhlan2014dc@163.com</email></corresp></author-notes>
<pub-date pub-type="ppub">
<month>10</month>
<year>2016</year></pub-date>
<pub-date pub-type="epub">
<day>02</day>
<month>09</month>
<year>2016</year></pub-date>
<volume>14</volume>
<issue>4</issue>
<fpage>3785</fpage>
<lpage>3790</lpage>
<history>
<date date-type="received">
<day>15</day>
<month>08</month>
<year>2015</year></date>
<date date-type="accepted">
<day>17</day>
<month>08</month>
<year>2016</year></date></history>
<permissions>
<copyright-statement>Copyright &#x000A9; 2016, Spandidos Publications</copyright-statement>
<copyright-year>2016</copyright-year></permissions>
<abstract>
<p>Dermatomyositis (DM) is a type of autoimmune inflammatory myopathy, which primarily affects the skin and muscle. The underlying mechanisms of DM remain poorly understood. The present study aimed to explore gene expression profile alterations, investigate the underlying mechanisms, and identify novel targets for DM. The GSE48280 dataset, which includes data from five DM and five normal muscle tissue samples, was obtained from the Gene Expression Omnibus. Firstly, differentially expressed genes (DEGs) were screened by limma package in R. Subsequently, functional and pathway enrichment analyses were performed using ClueGO from Cytoscape. Finally, protein-protein interaction (PPI) networks were constructed using STRING and Cytoscape, in order to identify hub genes. As a result, 180 upregulated and 21 downregulated genes were identified in the DM samples. The Gene Ontology enrichment analysis revealed that the type I interferon (IFN) signaling pathway was the most significantly enriched term within the DEGs. The Kyoto Encyclopedia of Genes and Genomes pathway analysis identified 27 significant pathways, the majority of which can be divided into the infectious diseases and immune system categories. Following construction of PPI networks, 24 hub genes were selected, all of which were associated with the type I IFN signaling pathway in DM. The findings of the present study indicated that type I IFNs may have a central role in the induction of DM. In addition, other DEGs, including chemokine (C-C motif) ligand 5, C-X-C motif chemokine 10, Toll-like receptor 3, DEXD/H-Box helicase 58, interferon induced with helicase C domain 1, interferon-stimulated gene 15 and MX dynamin-like GTPase 1, may be potential targets for DM diagnosis and treatment.</p></abstract>
<kwd-group>
<kwd>dermatomyositis</kwd>
<kwd>type I interferons</kwd>
<kwd>bioinformatics analysis</kwd>
<kwd>protein-protein interaction network</kwd>
<kwd>ClueGO</kwd></kwd-group></article-meta></front>
<body>
<sec sec-type="intro">
<title>Introduction</title>
<p>Dermatomyositis (DM) is a type of idiopathic inflammatory myopathy (IM), which affects the skin, muscle and other organs, and is associated with significant morbidity and mortality (<xref rid="b1-mmr-14-04-3785" ref-type="bibr">1</xref>). The incidence of DM is estimated at 9.63 cases per million individuals, according to a population-based study (<xref rid="b2-mmr-14-04-3785" ref-type="bibr">2</xref>). DM affects children and adults, and occurs more commonly in female patients (<xref rid="b3-mmr-14-04-3785" ref-type="bibr">3</xref>). DM is closely associated with an increased risk of malignancy (<xref rid="b4-mmr-14-04-3785" ref-type="bibr">4</xref>), including ovarian, lung, pancreatic, stomach and colorectal cancers, and non-Hodgkin's lymphoma (<xref rid="b5-mmr-14-04-3785" ref-type="bibr">5</xref>). The rarity of the disease, the different features of the multiple disease subsets, and the lethal complications associated with DM make it difficult to cure (<xref rid="b6-mmr-14-04-3785" ref-type="bibr">6</xref>).</p>
<p>Several factors are thought to contribute to DM, including environmental factors and genetic susceptibility. The association between DM and various environmental triggers, including medication, sunlight and infection, have been examined (<xref rid="b7-mmr-14-04-3785" ref-type="bibr">7</xref>). In addition, a genetic component may predispose individuals to DM; the most important genetic region associated with DM appears to be the major histocompatibility complex (MHC), and a few candidate genes, such as signal transducer and activator of transcription (STAT) 4 and tumor necrosis factor, have been reported to be relevant to the development of DM (<xref rid="b8-mmr-14-04-3785" ref-type="bibr">8</xref>). The first case of DM was reported in 1875 (<xref rid="b9-mmr-14-04-3785" ref-type="bibr">9</xref>); however, the underlying molecular mechanisms of this disease remain incompletely understood. Pathological studies of muscle tissue in DM have reported histological abnormalities in capillaries, and the infiltration of muscle by B cells, cluster of differentiation (CD)4<sup>+</sup> T cells, macrophages and plasma cells, and perifascicular atrophy (<xref rid="b10-mmr-14-04-3785" ref-type="bibr">10</xref>). Therefore, DM has classically been considered a humorally mediated autoimmune disease (<xref rid="b10-mmr-14-04-3785" ref-type="bibr">10</xref>). Recently, a microarray study reported that type 1 interferon (IFN)-mediated immune pathogenesis is likely to be involved in DM (<xref rid="b11-mmr-14-04-3785" ref-type="bibr">11</xref>). Identification of the involved molecular mechanisms may lead to the development of effective therapeutic approaches for the treatment of patients with DM.</p>
<p>The present study downloaded the GSE48280 dataset, and identified the differentially expressed genes (DEGs) between the DM and healthy control (HC) muscle tissue samples, in order to explore the molecular mechanisms underlying DM. In addition, functional and pathway enrichment analyses were conducted, and protein-protein interaction (PPI) networks of the DEGs were constructed to identify the key genes and signaling pathways in DM. The findings of the present study may be helpful to improve understanding regarding the pathogenesis of DM, and provide insight into the development of a novel therapeutic strategy.</p></sec>
<sec sec-type="methods">
<title>Data and methods</title>
<sec>
<title>Microarray data</title>
<p>The GSE48280 gene expression profile (<xref rid="b12-mmr-14-04-3785" ref-type="bibr">12</xref>) was downloaded from the Gene Expression Omnibus database (<ext-link xlink:href="http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE48280" ext-link-type="uri">http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE48280</ext-link>). The profile contained five patients with DM, five patients with polymyositis, four patients with inclusion body myositis and five HCs, the muscle samples from which were analyzed based on the Affymetrix Human Gene 1.0 ST Array platform (Affymetrix, Santa Clara, CA, USA). In the present study, only DM and HC samples were analyzed via a series of bioinformatics methods.</p></sec>
<sec>
<title>Data preprocessing and differential expression analysis</title>
<p>The original CEL files were transformed to probe-level data. If several probes mapped to a single gene, the mean of the probes was used to determine the gene expression value. The probe-level data were converted to gene symbols using the Perl procedure (version 5.24.0; <ext-link xlink:href="http://www.perl.org" ext-link-type="uri">www.perl.org</ext-link>). Following normalization, the limma package (<xref rid="b13-mmr-14-04-3785" ref-type="bibr">13</xref>) in R (version 3.2.2; <ext-link xlink:href="http://www.r-project.org" ext-link-type="uri">www.r-project.org</ext-link>) was conducted to identify DEGs between the DM and HC samples. Only genes with a fold change (FC) value (|log2FC|)&gt;1 and a P-value &lt;0.05 were regarded as DEGs, which were considered the signature genes of DM.</p></sec>
<sec>
<title>Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses</title>
<p>GO (<ext-link xlink:href="http://geneontology.org" ext-link-type="uri">geneontology.org</ext-link>) and KEGG (<ext-link xlink:href="http://www.genome.jp/kegg/" ext-link-type="uri">http://www.genome.jp/kegg/</ext-link>) pathway enrichment analyses for the identified DEGs were performed using Cytoscape software (version 3.2.1) with the ClueGO V2.1.7 plug-in (<xref rid="b14-mmr-14-04-3785" ref-type="bibr">14</xref>). The ClueGO plug-in generates functionally grouped GO annotation networks for a large number of genes. GO categories were divided into biological process, molecular function, cellular component and immune system process terms. The P-value was calculated by right-sided hypergeometric tests, and Benjamini-Hochberg adjustment was used for multiple test correction. GO terms with a P-value &lt;0.01, and KEGG pathways with a P-value &lt;0.05 were considered significant.</p></sec>
<sec>
<title>PPI network construction and hub gene identification</title>
<p>The Search Tool for the Retrieval of Interacting Genes (STRING; <ext-link xlink:href="http://string-db.org/" ext-link-type="uri">http://string-db.org/</ext-link>) database is an online tool, which has been designed as pre-computed global resource to evaluate PPI information (<xref rid="b15-mmr-14-04-3785" ref-type="bibr">15</xref>). In the present study, the STRING database was applied to construct PPI networks for the screened DEGs, with a combination score &gt;0.99 as the threshold. Subsequently, the PPI networks were visualized and analyzed using Cytoscape software (version 3.2.1) (<xref rid="b16-mmr-14-04-3785" ref-type="bibr">16</xref>) based on the STRING database.</p>
<p>According to a previous study regarding biological networks, the majority of PPI networks in the present study exhibited scale-free network properties (<xref rid="b17-mmr-14-04-3785" ref-type="bibr">17</xref>). PPI networks have a small number of highly connected protein nodes (known as hubs) and many poorly connected nodes. The connectivity degree was statistically analyzed by their combined scores based on the database algorithms. The important nodes in the PPI networks were identified and labeled as hub genes. Since the majority of the PPI networks obeyed the scale-free attribution, node degree &gt;20 was selected as the threshold to obtain hub genes in the present study.</p></sec></sec>
<sec sec-type="results">
<title>Results</title>
<sec>
<title>Identification of DEGs between DM and HC samples</title>
<p>According to the cut-off criteria of P&lt;0.05 and |log2FC| &gt;1.0, 201 DEGs were identified between the five DM and five HC samples using the limma package. A total of 180 DEGs were upregulated and 21 DEGs were downregulated.</p></sec>
<sec>
<title>GO and KEGG pathway analyses</title>
<p>All DEGs were subjected to GO and KEGG analysis. Following GO enrichment analysis using ClueGO, a total of 143 GO terms were associated with the DEGs. The top 20 significantly enriched GO terms are presented in <xref rid="tI-mmr-14-04-3785" ref-type="table">Table I</xref>. The top five significantly enriched GO terms were as follows: Type I IFN signaling pathway, defense response, response to cytokine, cytokine-mediated signaling pathway, and cellular response to cytokine stimulus.</p>
<p>Following the KEGG analysis, 27 KEGG pathways that were significantly enriched with DEGs were identified (<xref rid="tII-mmr-14-04-3785" ref-type="table">Table II</xref>). The top five significantly enriched KEGG pathways were as follows: Herpes simplex infection, influenza A, antigen processing and presentation, measles, and hepatitis C.</p></sec>
<sec>
<title>PPI network construction and hub gene identification</title>
<p>The PPI networks were constructed using Cytoscape based on the STRING database, and included 71 nodes and 389 edges with a combined score &gt;0.99 (<xref rid="f1-mmr-14-04-3785" ref-type="fig">Fig. 1</xref>). The degree of all nodes was analyzed and genes with a node degree &gt;20 are listed in <xref rid="tIII-mmr-14-04-3785" ref-type="table">Table III</xref>. Genes with a high degree of association included: ISG15 ubiquitin-like modifier (ISG15), interferon regulatory factor 1 (IRF1), interferon-induced protein with tetratricopeptide repeats 1 (IFIT1), MHC, class I, A (HLA-A), HLA-B, HLA-C, guanylate binding protein 2 (GBP2), IRF9, 2&#x02032;&#x02013;5&#x02032;-oligoadenylate synthetase-like (OASL), 2&#x02032;&#x02013;5&#x02032;-oligoadenylate synthetase 1 (OAS1), OAS2, OAS3, STAT2, IFIT2, IFIT3, MX dynamin-like GTPase1 (MX1), interferon induced transmembrane protein 1 (IFITM1), IFITM2, IFITM3, interferon, alpha-inducible protein 6 (IFI6), IFI27, IFI35, XIAP associated factor 1 (XAF1) and adenosine deaminase, RNA-specific (ADAR). Among those hub genes, ISG15 exhibited the highest degree (degree, 30).</p></sec></sec>
<sec sec-type="discussion">
<title>Discussion</title>
<p>DM is an autoimmune disease, which seriously impacts the quality of life of affected patients. The underlying pathogenesis of DM remains elusive. The present study analyzed gene expression profiles, identified the DEGs associated with DM, and enabled the identification of potential therapeutic targets. In the present study, 201 DEGs were identified, of which 180 were upregulated and 21 were downregulated. Following GO functional enrichment analysis using ClueGO, the type I IFN signaling pathway was revealed to be the most significantly enriched GO term within the DEGs. Previous studies have reported that patients with DM exhibit an activated type I IFN response, as demonstrated by increased type I IFN activity, and an IFN signature in the muscle, blood and skin (<xref rid="b1-mmr-14-04-3785" ref-type="bibr">1</xref>,<xref rid="b18-mmr-14-04-3785" ref-type="bibr">18</xref>,<xref rid="b19-mmr-14-04-3785" ref-type="bibr">19</xref>), which has also been described in patients with systemic lupus erythematosus, rheumatoid arthritis, Sj&#x000F6;gren's syndrome and systemic sclerosis (<xref rid="b20-mmr-14-04-3785" ref-type="bibr">20</xref>). In addition, type 1 IFN-producing plasmacytoid dendritic cells are abundant in DM muscle and skin (<xref rid="b21-mmr-14-04-3785" ref-type="bibr">21</xref>). When compared with other types of IM, the overproduction of IFN-I inducible transcripts and proteins in muscle tissue is a unique feature of DM (<xref rid="b22-mmr-14-04-3785" ref-type="bibr">22</xref>). Furthermore, the iatrogenic administration of recombinant IFNs has been reported to induce DM (<xref rid="b23-mmr-14-04-3785" ref-type="bibr">23</xref>). Taken together, these data indicated that the type I IFN signaling pathway may be important in the pathogenesis of DM.</p>
<p>A KEGG pathway enrichment analysis of the DEGs was conducted, and 27 significantly enriched pathways were identified. The majority of these pathways can be divided into two categories: Infectious diseases and the immune system. The enriched immune system-related pathways include antigen processing and presentation, complement and coagulation cascades pathway, RIG-I-like receptor (RLR) signaling pathway, Toll-like receptor (TLR) signaling pathway, and cytosolic DNA-sensing pathway. Among them, the TLR signaling, RLR signaling and cytosolic DNA-sensing pathways are responsible for detecting pathogens and generating innate immune responses. Type I IFNs are primarily induced by the activation of pattern-recognition receptors, such as TLRs, RLRs and cytoplasmic DNA sensor receptors (<xref rid="b24-mmr-14-04-3785" ref-type="bibr">24</xref>). In the present study, chemokine (C-C motif) ligand 5 (CCL5), CD14, C-X-C motif chemokine 10 (CXCL10), CXCL11, STAT1 and TLR3 DEGs were enriched in the TLR signaling pathway, whereas CXCL10, CYLD lysine 63 deubiquitinase, DEXD/H-Box helicase 58 (DDX58), interferon induced with helicase C domain 1 (IFIH1), ISG15 and tripartite motif-containing protein 25 DEGs were enriched in the RLR signaling pathway. Furthermore, ADAR, CCL5, CXCL10 and DDX58 DEGs participated in the cytosolic DNA-sensing pathway. CCL5 is a key proinflammatory chemokine, which may have a role in driving recruitment of leukocytes, angiogenesis and fibrosis in chronic inflammatory diseases. Furthermore, CCL5 blockade is able to prevent immunopathology (<xref rid="b25-mmr-14-04-3785" ref-type="bibr">25</xref>). CXCL10 and CXCL11 are members of the CXC subfamily of chemokines. Serum levels of CXCL10 are elevated in patients with active DM (<xref rid="b26-mmr-14-04-3785" ref-type="bibr">26</xref>). CXCL10 has also been reported to be secreted by human skeletal muscle cells, which promote inflammation (<xref rid="b27-mmr-14-04-3785" ref-type="bibr">27</xref>). A previous study using an animal model of C-protein-induced myositis demonstrated that suppressing CXCL10 with monoclonal antibodies was able to inhibit muscular inflammation (<xref rid="b28-mmr-14-04-3785" ref-type="bibr">28</xref>). TLR3 is a member of the TLR family, which has a fundamental role in pathogen recognition and activation of innate immunity. An <italic>in vitro</italic> study revealed that TLR3 is involved in the overexpression of MHC-I in IM (<xref rid="b29-mmr-14-04-3785" ref-type="bibr">29</xref>). Cappelletti <italic>et al</italic> reported that TLR3 is localized on vascular endothelial cells, muscle infiltrating cells and regenerating myofibers in DM (<xref rid="b30-mmr-14-04-3785" ref-type="bibr">30</xref>). In addition, Li <italic>et al</italic> (<xref rid="b31-mmr-14-04-3785" ref-type="bibr">31</xref>) demonstrated that TLR-3 and RIG-I are implicated in the formation of perifascicular atrophy in DM.</p>
<p>RLRs comprise DDX58 (RIG-I), IFIH1 &#x0005B;also known as melanoma differentiation-associated protein 5 (MDA5)&#x0005D; and LGP2, and are known as cytosolic RNA receptors that induce the expression of type I IFN genes. Sato <italic>et al</italic> (<xref rid="b32-mmr-14-04-3785" ref-type="bibr">32</xref>) detected anti-clinically amyopathic DM (CADM)-140 antibodies encoded by MDA5 in patients with amyopathic DM; of these patients, 50% had rapidly progressive interstitial lung disease (RP-ILD). In addition, it has been reported that anti-MDA5 autoantibodies may be useful for diagnosing DM, particularly the CADM and RP-ILD subsets among Asian cohorts (<xref rid="b33-mmr-14-04-3785" ref-type="bibr">33</xref>). Su&#x000E1;rez-Calvet <italic>et al</italic> (<xref rid="b12-mmr-14-04-3785" ref-type="bibr">12</xref>) reported that RIG-I was upregulated in pathological muscle fibers from patients with DM compared with other types of IM, as determined by immunohistochemistry. In addition, human myotubes were shown to produce IFN-&#x003B2; in response to RIG-I stimulation, and the autocrine effects of IFN-&#x003B2; could further amplify the expression of IFN genes. The results of the present KEGG analysis highlighted the role of the type I IFN signaling pathway in the pathogenesis of DM, and suggested that DEGs associated with these pathways, including CCL5, CXCL10, TLR3, DDX58 and IFIH1, may be used as potential targets for DM diagnosis and treatment.</p>
<p>Hub nodes have more complex interactions compared with other genes, thus indicating that they have important roles in the underlying mechanisms of disease (<xref rid="b34-mmr-14-04-3785" ref-type="bibr">34</xref>). Therefore, identification of hub genes may facilitate the development of effective therapeutic approaches for the treatment of patients with DM. The following DEGs: ISG15, IRF1, IFIT1, HLA-A, HLA-B, HLA-C, GBP2, IRF9, OASL, OAS1, OAS2, OAS3, STAT2, IFIT2, IFIT3, MX1, IFITM1, IFITM2, IFITM3, IFI6, IFI27, IFI35, XAF1 and ADAR were identified as hub genes in the PPI networks, thus indicating that these genes may have pivotal roles in DM development. Notably, all of these hub genes are involved in the type I IFN signaling pathway in DM. The ISG15 gene had the highest degree in the PPI networks (degree, 30). Salajegheh <italic>et al</italic> demonstrated that ISG15 is one of the most upregulated genes in DM muscle compared with normal muscle, and the ISG15 protein localizes to atrophic myofibers, as determined by immunohistochemistry (<xref rid="b23-mmr-14-04-3785" ref-type="bibr">23</xref>). ISG15 is a ubiquitin-like modifier (<xref rid="b35-mmr-14-04-3785" ref-type="bibr">35</xref>), which is believed to function as a possible mediator of muscle atrophy via protein conjugation, and may contribute to disease activity in DM. MX1, IFIT1 and IFIT3 have been detected in DM muscle, and have previously been identified as ISG15-conjugated in IFN-&#x003B2;-stimulated HeLa cells (<xref rid="b36-mmr-14-04-3785" ref-type="bibr">36</xref>). Furthermore, MX1 is an IFN-I-induced protein that provides innate defense against several viruses. A previous study reported that MX1 is abundant in DM muscle and skin, and is specifically expressed in DM but not in other types of IM (<xref rid="b37-mmr-14-04-3785" ref-type="bibr">37</xref>). In addition, MX1 RNA in juvenile DM peripheral blood mononuclear cells is positively correlated with muscle involvement (<xref rid="b38-mmr-14-04-3785" ref-type="bibr">38</xref>). Therefore, ISG15 and MX1 may contribute to disease activity, and may be considered potential molecular markers and effective treatment targets in DM.</p>
<p>Human leukocyte antigen (HLA-A, HLA-B, HLA-C) is the human-specific MHC. A previous study reported that MHC-I antigens were not detected in normal muscle, but were upregulated in IM muscle (<xref rid="b39-mmr-14-04-3785" ref-type="bibr">39</xref>). In transgenic mice, overexpression of MHC-I is sufficient to induce myositis (<xref rid="b40-mmr-14-04-3785" ref-type="bibr">40</xref>). The induction of MHC-I in IM may involve type I IFN and the 'IFN-I signature' is also induced in IMs (<xref rid="b10-mmr-14-04-3785" ref-type="bibr">10</xref>). These data suggested that MHC-I may be considered a useful adjunctive diagnostic test for DM.</p>
<p>Other hub genes associated with the type I IFN signaling pathway may also have an important role in the pathogenesis of DM. For example, the OAS family proteins, which are predominantly induced by IFN type I, consist of OAS1, OAS2, OAS3 and OASL. OAS levels are strongly associated with autoimmune diseases (<xref rid="b41-mmr-14-04-3785" ref-type="bibr">41</xref>). Sanayama <italic>et al</italic> (<xref rid="b42-mmr-14-04-3785" ref-type="bibr">42</xref>) proposed that OASL may be a biomarker for the response of patients with rheumatoid arthritis to tocilizumab, which is the most commonly used drug to treat this disease. Identification of these genes and their precise roles may clarify the mechanism and offer useful information regarding the treatment of DM.</p>
<p>In conclusion, the present study attempted to explore the potential molecular mechanisms underlying DM using bioinformatics analyses. The findings of the present study may contribute to our understanding regarding the molecular mechanisms underlying DM. The identified DEGs, including CCL5, CXCL10, TLR3, DDX58, IFIH1, ISG15 and MX1, may potentially be used as targets for DM diagnosis and treatment. However, further studies are required to further verify this hypothesis.</p></sec></body>
<back>
<ack>
<title>Acknowledgments</title>
<p>This work was partly supported by the Natural Science Foundation of China (grant no. 81170562); The abstract of the current study was partly presented at the 14<sup>th</sup> Chinese National Conference of Medical Genetics proceedings.</p></ack>
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<floats-group>
<fig id="f1-mmr-14-04-3785" position="float">
<label>Figure 1</label>
<caption>
<p>Constructed protein-protein interaction networks of differentially expressed genes. Nodes represent proteins, edges represent interactions between two proteins.</p></caption>
<graphic xlink:href="MMR-14-04-3785-g00.jpg"/></fig>
<table-wrap id="tI-mmr-14-04-3785" position="float">
<label>Table I</label>
<caption>
<p>Top 20 Gene Ontology (GO) functional enrichment analysis of differentially expressed genes (P&lt;0.01).</p></caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th valign="middle" align="left">Category</th>
<th valign="middle" align="center">Term</th>
<th valign="middle" align="center">Count</th>
<th valign="middle" align="center">P-value</th></tr></thead>
<tbody>
<tr>
<td valign="top" align="left">Immune system process</td>
<td valign="top" align="left">GO:0060337: Type I interferon signaling pathway</td>
<td valign="top" align="center">31</td>
<td valign="top" align="center">3.45E-41</td></tr>
<tr>
<td valign="top" align="left">Biological process</td>
<td valign="top" align="left">GO:0006952: Defense response</td>
<td valign="top" align="center">84</td>
<td valign="top" align="center">1.34E-37</td></tr>
<tr>
<td valign="top" align="left">Biological process</td>
<td valign="top" align="left">GO:0034097: Response to cytokine</td>
<td valign="top" align="center">57</td>
<td valign="top" align="center">1.95E-37</td></tr>
<tr>
<td valign="top" align="left">Biological process</td>
<td valign="top" align="left">GO:0019221: Cytokine-mediated signaling pathway</td>
<td valign="top" align="center">47</td>
<td valign="top" align="center">5.39E-35</td></tr>
<tr>
<td valign="top" align="left">Biological process</td>
<td valign="top" align="left">GO:0071345: Cellular response to cytokine stimulus</td>
<td valign="top" align="center">49</td>
<td valign="top" align="center">3.39E-32</td></tr>
<tr>
<td valign="top" align="left">Biological process</td>
<td valign="top" align="left">GO:0009615: Response to virus</td>
<td valign="top" align="center">38</td>
<td valign="top" align="center">3.46E-27</td></tr>
<tr>
<td valign="top" align="left">Immune system process</td>
<td valign="top" align="left">GO:0034341: Response to interferon-gamma</td>
<td valign="top" align="center">26</td>
<td valign="top" align="center">6.14E-26</td></tr>
<tr>
<td valign="top" align="left">Biological process</td>
<td valign="top" align="left">GO:0051707: Response to other organism</td>
<td valign="top" align="center">48</td>
<td valign="top" align="center">9.91E-25</td></tr>
<tr>
<td valign="top" align="left">Biological process</td>
<td valign="top" align="left">GO:0098542: Defense response to other organism</td>
<td valign="top" align="center">38</td>
<td valign="top" align="center">2.95E-23</td></tr>
<tr>
<td valign="top" align="left">Immune system process</td>
<td valign="top" align="left">GO:0071346: Cellular response to interferon-gamma</td>
<td valign="top" align="center">22</td>
<td valign="top" align="center">6.58E-22</td></tr>
<tr>
<td valign="top" align="left">Immune system process</td>
<td valign="top" align="left">GO:0060333: Interferon-gamma-mediated signaling pathway</td>
<td valign="top" align="center">19</td>
<td valign="top" align="center">3.28E-20</td></tr>
<tr>
<td valign="top" align="left">Biological process</td>
<td valign="top" align="left">GO:0043901: Negative regulation of multi-organism process</td>
<td valign="top" align="center">20</td>
<td valign="top" align="center">4.95E-19</td></tr>
<tr>
<td valign="top" align="left">Biological process</td>
<td valign="top" align="left">GO:0019079: Viral genome replication</td>
<td valign="top" align="center">17</td>
<td valign="top" align="center">1.89E-17</td></tr>
<tr>
<td valign="top" align="left">Biological process</td>
<td valign="top" align="left">GO:0050792: Regulation of viral process</td>
<td valign="top" align="center">20</td>
<td valign="top" align="center">4.17E-17</td></tr>
<tr>
<td valign="top" align="left">Biological process</td>
<td valign="top" align="left">GO:0043900: Regulation of multi-organism process</td>
<td valign="top" align="center">29</td>
<td valign="top" align="center">1.63E-16</td></tr>
<tr>
<td valign="top" align="left">Biological process</td>
<td valign="top" align="left">GO:0031347: Regulation of defense response</td>
<td valign="top" align="center">37</td>
<td valign="top" align="center">1.86E-16</td></tr>
<tr>
<td valign="top" align="left">Immune system process</td>
<td valign="top" align="left">GO:0045088: Regulation of innate immune response</td>
<td valign="top" align="center">27</td>
<td valign="top" align="center">1.31E-15</td></tr>
<tr>
<td valign="top" align="left">Immune system process</td>
<td valign="top" align="left">GO:0050778: Positive regulation of immune response</td>
<td valign="top" align="center">36</td>
<td valign="top" align="center">4.79E-15</td></tr>
<tr>
<td valign="top" align="left">Biological process</td>
<td valign="top" align="left">GO:0044403: Symbiosis, encompassing mutualism through parasitism</td>
<td valign="top" align="center">36</td>
<td valign="top" align="center">1.26E-14</td></tr>
<tr>
<td valign="top" align="left">Biological process</td>
<td valign="top" align="left">GO:0035456: Response to interferon-beta</td>
<td valign="top" align="center">9</td>
<td valign="top" align="center">8.91E-14</td></tr></tbody></table></table-wrap>
<table-wrap id="tII-mmr-14-04-3785" position="float">
<label>Table II</label>
<caption>
<p>Enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of differentially expressed genes (P&lt;0.05).</p></caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th valign="middle" align="left">Term</th>
<th valign="middle" align="center">Description</th>
<th valign="middle" align="center">Count</th>
<th valign="middle" align="center">P-value</th></tr></thead>
<tbody>
<tr>
<td valign="top" align="left">KEGG:05168</td>
<td valign="top" align="left">Herpes simplex infection</td>
<td valign="top" align="center">21</td>
<td valign="top" align="center">2.31E-13</td></tr>
<tr>
<td valign="top" align="left">KEGG:05164</td>
<td valign="top" align="left">Influenza A</td>
<td valign="top" align="center">18</td>
<td valign="top" align="center">7.66E-11</td></tr>
<tr>
<td valign="top" align="left">KEGG:04612</td>
<td valign="top" align="left">Antigen processing and presentation</td>
<td valign="top" align="center">13</td>
<td valign="top" align="center">1.30E-10</td></tr>
<tr>
<td valign="top" align="left">KEGG:05162</td>
<td valign="top" align="left">Measles</td>
<td valign="top" align="center">13</td>
<td valign="top" align="center">8.29E-08</td></tr>
<tr>
<td valign="top" align="left">KEGG:05160</td>
<td valign="top" align="left">Hepatitis C</td>
<td valign="top" align="center">11</td>
<td valign="top" align="center">4.99E-06</td></tr>
<tr>
<td valign="top" align="left">KEGG:04610</td>
<td valign="top" align="left">Complement and coagulation cascades</td>
<td valign="top" align="center">8</td>
<td valign="top" align="center">1.14E-05</td></tr>
<tr>
<td valign="top" align="left">KEGG:05133</td>
<td valign="top" align="left">Pertussis</td>
<td valign="top" align="center">8</td>
<td valign="top" align="center">1.85E-05</td></tr>
<tr>
<td valign="top" align="left">KEGG:05150</td>
<td valign="top" align="left"><italic>Staphylococcus aureus</italic> infection</td>
<td valign="top" align="center">6</td>
<td valign="top" align="center">2.96E-04</td></tr>
<tr>
<td valign="top" align="left">KEGG:05330</td>
<td valign="top" align="left">Allograft rejection</td>
<td valign="top" align="center">5</td>
<td valign="top" align="center">4.14E-04</td></tr>
<tr>
<td valign="top" align="left">KEGG:04145</td>
<td valign="top" align="left">Phagosome</td>
<td valign="top" align="center">9</td>
<td valign="top" align="center">4.41E-04</td></tr>
<tr>
<td valign="top" align="left">KEGG:05332</td>
<td valign="top" align="left">Graft-versus-host disease</td>
<td valign="top" align="center">5</td>
<td valign="top" align="center">5.44E-04</td></tr>
<tr>
<td valign="top" align="left">KEGG:04940</td>
<td valign="top" align="left">Type I diabetes mellitus</td>
<td valign="top" align="center">5</td>
<td valign="top" align="center">6.20E-04</td></tr>
<tr>
<td valign="top" align="left">KEGG:04622</td>
<td valign="top" align="left">RIG-I-like receptor signaling pathway</td>
<td valign="top" align="center">6</td>
<td valign="top" align="center">5.76E-04</td></tr>
<tr>
<td valign="top" align="left">KEGG:05142</td>
<td valign="top" align="left">Chagas disease (American trypanosomiasis)</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">7.30E-04</td></tr>
<tr>
<td valign="top" align="left">KEGG:05320</td>
<td valign="top" align="left">Autoimmune thyroid disease</td>
<td valign="top" align="center">5</td>
<td valign="top" align="center">1.17E-03</td></tr>
<tr>
<td valign="top" align="left">KEGG:05416</td>
<td valign="top" align="left">Viral myocarditis</td>
<td valign="top" align="center">5</td>
<td valign="top" align="center">1.79E-03</td></tr>
<tr>
<td valign="top" align="left">KEGG:04620</td>
<td valign="top" align="left">Toll-like receptor signaling pathway</td>
<td valign="top" align="center">6</td>
<td valign="top" align="center">3.97E-03</td></tr>
<tr>
<td valign="top" align="left">KEGG:04623</td>
<td valign="top" align="left">Cytosolic DNA-sensing pathway</td>
<td valign="top" align="center">4</td>
<td valign="top" align="center">1.47E-02</td></tr>
<tr>
<td valign="top" align="left">KEGG:05143</td>
<td valign="top" align="left">African trypanosomiasis</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">1.43E-02</td></tr>
<tr>
<td valign="top" align="left">KEGG:04514</td>
<td valign="top" align="left">Cell adhesion molecules (CAMs)</td>
<td valign="top" align="center">6</td>
<td valign="top" align="center">1.55E-02</td></tr>
<tr>
<td valign="top" align="left">KEGG:05020</td>
<td valign="top" align="left">Prion diseases</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">1.52E-02</td></tr>
<tr>
<td valign="top" align="left">KEGG:05161</td>
<td valign="top" align="left">Hepatitis B</td>
<td valign="top" align="center">6</td>
<td valign="top" align="center">1.46E-02</td></tr>
<tr>
<td valign="top" align="left">KEGG:04919</td>
<td valign="top" align="left">Thyroid hormone signaling pathway</td>
<td valign="top" align="center">5</td>
<td valign="top" align="center">2.32E-02</td></tr>
<tr>
<td valign="top" align="left">KEGG:03050</td>
<td valign="top" align="left">Proteasome</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">2.29E-02</td></tr>
<tr>
<td valign="top" align="left">KEGG:05144</td>
<td valign="top" align="left">Malaria</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">2.92E-02</td></tr>
<tr>
<td valign="top" align="left">KEGG:04064</td>
<td valign="top" align="left">NF-kappa B signaling pathway</td>
<td valign="top" align="center">4</td>
<td valign="top" align="center">3.31E-02</td></tr>
<tr>
<td valign="top" align="left">KEGG:00330</td>
<td valign="top" align="left">Arginine and proline metabolism</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">4.74E-02</td></tr></tbody></table></table-wrap>
<table-wrap id="tIII-mmr-14-04-3785" position="float">
<label>Table III</label>
<caption>
<p>Degree of each hub node in the protein-protein interaction networks.</p></caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th valign="middle" align="left">Gene symbol</th>
<th valign="middle" align="center">Degree</th></tr></thead>
<tbody>
<tr>
<td valign="top" align="left">ISG15</td>
<td valign="top" align="center">30</td></tr>
<tr>
<td valign="top" align="left">IRF1</td>
<td valign="top" align="center">29</td></tr>
<tr>
<td valign="top" align="left">IFIT1</td>
<td valign="top" align="center">28</td></tr>
<tr>
<td valign="top" align="left">HLA-A, HLA-B, HLA-C, GBP2, IRF9</td>
<td valign="top" align="center">27</td></tr>
<tr>
<td valign="top" align="left">OASL, OAS1, OAS2, OAS3</td>
<td valign="top" align="center">26</td></tr>
<tr>
<td valign="top" align="left">STAT2</td>
<td valign="top" align="center">24</td></tr>
<tr>
<td valign="top" align="left">IFIT2, IFIT3, MX1, IFITM1, IFITM2, IFITM3, IFI6, IFI27, IFI35, XAF1, ADAR</td>
<td valign="top" align="center">23</td></tr></tbody></table>
<table-wrap-foot><fn id="tfn1-mmr-14-04-3785">
<p>ISG15, ISG15 ubiquitin-like modifier; IRF, interferon regulatory factor; IFIT, interferon-induced protein with tetratricopeptide repeats; HLA, major histocompatibility complex, class I; GBP2, guanylate binding protein 2; OASL, 2&#x02032;-5&#x02032;-oligoadenylate synthetase-like; OAS, 2&#x02032;-5&#x02032;-oligoadenylate synthetase; STAT2, signal transducer and activator of transcription 2; MX1, MX dynamin-like GTPase 1; IFITM, interferon induced transmembrane protein 1; IFI, interferon, alpha-inducible protein; XAF1, XIAP associated factor 1; ADAR, adenosine deaminase, RNA-specific.</p></fn></table-wrap-foot></table-wrap></floats-group></article>
