Background:Dermatomyositis (DM) is a chronic systemic autoimmune disease characterized by inflammatory infiltrates in the skin and muscle1. The genes and pathways in the inflamed myopathies in patients with DM are poorly understood2.Objectives:To identify the key genes and pathways associated with DM and further discover its pathogenesis.Methods:Muscle tissue gene expression profile (GSE143323) were acquired from the GEO database, which included 39 DM samples and 20 normal samples. The differentially expressed genes (DEGs) in DM muscle tissue were screened by adopting the R software. Gene ontology (GO) and Kyoto Encyclopedia of Genome (KEGG) pathway enrichment analysis was performed by Metascape online analysis tool. A protein-protein interaction (PPI) network was then constructed by STRING software using the genes in significantly different pathways. Network of DEGs was analyzed by Cytoscape software. And degree of nodes was used to screen key genes.Results:Totally, 126 DEGs were obtained, which contained 122 up-regulated and 4 down-regulated. GO analysis revealed that most of the DEGs were significantly enriched in type I interferon signaling pathway, response to interferon-gamma, collagen-containing extracellular matrix, response to interferon-alpha and bacterium, positive regulation of cell death, leukocyte chemotaxis. KEGG pathway analysis showed that upregulated DEGs enhanced pathways associated with the hepatitis C, complement and coagulation cascades, p53 signaling pathway, RIG-I-like receptor signaling, Osteoclast differentiation, and AGE-RAGE signaling pathway. Ten hub genes were identified in DM, they were ISG15, IRF7, STAT1, MX1, OASL, OAS2, OAS1, OAS3, GBP1, and IRF9 according to the Cytoscape software and cytoHubba plugin.Conclusion:The findings from this bioinformatics network analysis study identified the key hub genes that might provide new molecular markers for its diagnosis and treatment.References:[1]Olazagasti JM, Niewold TB, Reed AM. Immunological biomarkers in dermatomyositis. Curr Rheumatol Rep 2015;17(11):68. doi: 10.1007/s11926-015-0543-y [published Online First: 2015/09/26].[2]Chen LY, Cui ZL, Hua FC, et al. Bioinformatics analysis of gene expression profiles of dermatomyositis. Mol Med Rep 2016;14(4):3785-90. doi: 10.3892/mmr.2016.5703 [published Online First: 2016/09/08].[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

Background:Psoriasis is an immune-mediated, genetic disease manifesting in the skin or joints or both, and also has a strong genetic predisposition and autoimmune pathogenic traits1. The hallmark of psoriasis is sustained inflammation that leads to uncontrolled keratinocyte proliferation and dysfunctional differentiation. And it’s also a chronic relapsing disease, which often necessitates a long-term therapy2.Objectives:To investigate the molecular mechanisms of psoriasis and find the potential gene targets for diagnosis and treating psoriasis.Methods:Total 334 gene expression data of patients with psoriasis research (GSE13355 GSE14905 and GSE30999) were obtained from the Gene Expression Omnibus database. After data preprocessing and screening of differentially expressed genes (DEGs) by R software. Online toll Metascape3 was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. Interactions of proteins encoded by DEGs were discovered by Protein-protein interaction network (PPI) using STRING online software. Cytoscape software was utilized to visualize PPI and the degree of each DEGs was obtained by analyzing the topological structure of the PPI network.Results:A total of 611 DEGs were found to be differentially expressed in psoriasis. GO analysis revealed that up-regulated DEGs were mostly associated with defense and response to external stimulus while down-regulated DEGs were mostly associated with metabolism and synthesis of lipids. KEGG enrichment analysis suggested they were mainly enriched in IL-17 signaling, Toll-like receptor signaling and PPAR signaling pathways, Cytokine-cytokine receptor interaction and lipid metabolism. In addition, top 9 key genes (CXCL10, OASL, IFIT1, IFIT3, RSAD2, MX1, OAS1, IFI44 and OAS2) were identified through Cytoscape.Conclusion:DEGs of psoriasis may play an essential role in disease development and may be potential pathogeneses of psoriasis.References:[1]Boehncke WH, Schon MP. Psoriasis. Lancet 2015;386(9997):983-94. doi: 10.1016/S0140-6736(14)61909-7 [published Online First: 2015/05/31].[2]Zhang YJ, Sun YZ, Gao XH, et al. Integrated bioinformatic analysis of differentially expressed genes and signaling pathways in plaque psoriasis. Mol Med Rep 2019;20(1):225-35. doi: 10.3892/mmr.2019.10241 [published Online First: 2019/05/23].[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

BackgroundPsoriasis is a common, chronic, immune-mediated skin disorder, which is characterized by clearly demarcated areas of erythematous plaques with overlying silvery scales appearing on the skin [1]. Previous studies have shown that plenty of psoriasis patients are engaged in less physical activity (PA), however, the genetic causal association between PA and psoriasis is of scarce evidence [2].ObjectivesIn this study, we aimed to evaluate the causal association between PA and psoriasis.MethodsWe used genome-wide association study (GWAS) data to conduct a Two-Sample Mendelian randomization (MR). MR utilizes single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to examine the causality of an observed association between exposure and outcome [3]. SNPs that were independent (r2 < 0.001) and without linkage disequilibrium and strongly related to PA (p < 1e-08, F>10), were selected as IVs. PA was divided into two parts including sedentary and physically active activities. Physically active behaviors include accelerometer-based physical activity, vigorous physical activity, moderate to vigorous physical activity, moderate physical activity 10+ minutes, and vigorous physical activity 10+ minutes. GWAS summary data of four types of sedentary behavior and five types of physically active behaviors were acquired from the UK biobank. The GWAS data of sedentary behavior includes watching TV (N=437,88), using computer(N=360,895), mobile phone use (N=310,555), and driving(N=456,972). The FinnGen collaboration provides summary statistics for psoriasis, which included 216,752 European individuals (4,510 cases and 212,242 non-cases). Inverse-variance weighted (IVW), MR-Egger, and the weighted median were used to assess causality. IVW was a key method to estimate. Cochran’s Q test and egger-intercept analysis were used as auxiliary analyses to assess pleiotropy and heterogeneity.ResultsThere were strong genetic causal relationships between psoriasis and TV watching. Watching TV was identified as the risk factor of psoriasis by IVW (OR =2.11; 95% Cl, 1.35-3.30, P=1.02E-03), identical to MR-Egger and the weighted median analysis. Pleiotropy and heterogeneity were not found by Cochran’s Q (P =0.140)and egger-intercept (P=0.43), suggesting these findings were reliable. In contrast, there was no causal relationship between the other activities and psoriasis (P > 0.05).Figure 1.Mendelian randomization results for gene-level causality between physical activity (PA) and psoriasis were evaluated by the odds ratio (OR) values of IVW, MR Egger, and Weighted median. Scatter plots (A), funnel plots (B), leave-one-out analysis (C), and forest plot (D) present TV watching as a risk factor for psoriasis. IVW, inverse-variance weighted; CI, confidence interval; SNP, single nucleotide polymorphisms.ConclusionOur findings reveal that TV watching is an apparent risk factor for psoriasis. It provides psoriasis patients with a piece of advice that they should lessen extended sedentary time and boost physical activity.References[1]Gr Greb, J. E., Goldminz, A. M., Elder, J. T., Lebwohl, M. G., Gladman, D. D., Wu, J. J., Mehta, N. N., Finlay, A. Y., & Gottlieb, A. B. (2016). Psoriasis. Nature reviews. Disease primers, 2, 16082.https://doi.org/10.1038/nrdp.2016.82[2]Wilson, P. B., Bohjanen, K. A., Ingraham, S. J., & Leon, A. S. (2012). Psoriasis and physical activity: a review. Journal of the European Academy of Dermatology and Venereology: JEADV, 26(11), 1345–1353.https://doi.org/10.1111/j.1468-3083.2012.04494.x[3]Davey Smith, G., & Hemani, G. (2014). Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Human molecular genetics, 23(R1), R89–R98.https://doi.org/10.1093/hmg/ddu328Acknowledgements:NIL.Disclosure of InterestsNone Declared.