AB0020 Differentially expressed genes in sjgren’s syndrome microarray
Authors: L. Wei, Z. Wenjia, W. Yuanhao, J. Yue, Z. Lei, L. Bin, X. Bin, Z. Bo, D. Ran, C. Yue, W. Xiaoshuang
Journal: Annals of the Rheumatic Diseases (2018)

<h3>Background</h3> Sjogren's syndrome (SS) is a chronic autoimmune disease characterised by a decrease in the secretion of tears and saliva, and is considered to be a common rheumatic immuno disease second only to rheumatoid arthritis. Patients with SS may have different prognosis or different reactions to the same treatment plan. Therefore, a more accurate and more practical predictor is needed to provide a basis for the individualised treatment of SS. Bioinformatics is a new approach. It applies bioinformatics methodology, tools, software and database to molecular mechanisms, new biomarkers and individualised treatment measures. <h3>Objectives</h3> In this study, gene expression data as the basis, combined with bioinformatics tools and literature mining methods, firstly analyses the expression differences between normal people and patients with parotid gland parotid Sjogren syndrome gene, a key pathway and further study the effect of SS involved in the occurrence and development of the application of protein interaction network screening key genes, pathogenesis help to clarify SS, and for the future of this disease and provide a new direction. <h3>Methods</h3> The expression profiles of mRNA in parotid gland of patients with Sjogren syndrome and normal parotid gland were obtained from the GEO database. A total of 24 patients with Sjogren's syndrome, 6 patients with dry mouth and dry eye symptoms and 25 patients without dry mouth and dry eye were enrolled in the study. 65 samples were collected. The GEO2R tool screened the parotid differentially expressed genes in the patients with SS compared with the healthy people, and the DAVID tool enriched the function and pathway of the gene. The STRING database constructs a network that differentially expresses the interaction of gene protein products and screening core genes. <h3>Results</h3> 24 upregulated genes and 147 down regulated genes were screened. KEGG enriched 5 pathways including cell adhesion, intestinal immune network IgA secretion, viral myocarditis pathway, rheumatoid arthritis pathway and leukocyte transendothelial migration. String protein interaction database was used to subcellular localization of differentially expressed gene protein products in parotid gland, and differentially expressed proteins were identified. The interaction network was constructed by Cytoscape software. The protein interaction network was constructed based on the 171 differentially expressed genes of SS, and the isolated and non interacting protein nodes were screened out. 108 upregulated gene encoded proteins were found to interact with each other, forming a complex network containing 390 interactions. The Degree&gt;20 of the node is selected as the standard selection centre node, and 5 Hub genes are obtained. It is found that PTPRC, CD86, STAT1, FYN and LCP2 are key genes and may play an important role in the pathogenesis of Sjogren syndrome. <h3>Conclusions</h3> Above all, we used bioinformatics to find genes and critical pathways related to SS, which is expected to provide new molecular markers for the diagnosis and treatment of SS. It provides a new way of thinking for the treatment and prognosis of Sjogren syndrome. <h3>Acknowledgements</h3> This study was supported by grants from the National Natural Science Foundation of China (No. 81473635), Science and Technology Program of Tianjin (No. 15ZXLCSY00020). <h3>Disclosure of Interest</h3> None declared