Deciphering crucial genes in coeliac disease by bioinformatics analysis

Abstract Coeliac disease (CD) is a chronic autoimmune disease that is characterized by malabsorption in sensitive individuals. CD is triggered by the ingestion of grains containing gluten. CD is concomitant with several other disorders, including dermatitis herpetiformis, selective IgA deficiency, thyroid disorders, diabetes mellitus, various connective tissue disorders, inflammatory bowel disease, and rheumatoid arthritis. The advent of high throughput technologies has provided a massive wealth of data which are processed in various omics scale fields. These approaches have revolutionized the medical research and monitoring of the biological systems. In this regard, omics scaled analyses of CD by Comparative Toxicogenomics Database (CTD), DISEASES, and GeneCards databases have retrieved 2656 CD associated genes. Amongst, 54 genes were assigned by Venn Diagram of the intersection to be shared by these 3 databases for CD. These common genes were subjected to further analysis and screening. The Enrich database, GeneMANIA, Cytoscape, and WebGestalt (WEB-based GEne SeT AnaLysis Toolkit) were employed for functional analysis. These analyses indicated that the obtained genes are mainly involved in the immune system and signalling pathways related to autoimmune diseases. The STAT1, ALB, IL10, IL2, IL4, IL17A, TGFB1, IL1B, IL6, TNF, IFNG hub genes were particularly indicated to have significant roles in CD. Functional analyses of these hub genes by GeneMANIA indicated that they are involved in immune systems regulation. Moreover, 25 out of 54 genes were identified to be seed genes by the WebGestalt database. Gene set analysis with GEO2R tool from Gene Expression Omnibus (GEO) showed that there were 15 significant genes in GSE76168, 29 significant genes in GSE87460, 12 significant genes in GSE87458, 9 significant genes in GSE87457, 3753 significant genes in GSE112102 and 1043 significant genes in GSE102991 with differential expression in coeliac patients compared to controls. The IRF1and STAT1 genes were common between the significant genes from GEO and the 54 CD related genes from three public databases. In the light these results, nine key genes, including IRF1, STAT1, IL17A, TGFB1, ALB, IL10, IL2, IL4, and IL1B, were identified to be associated with CD. These findings could be used to find novel diagnostic biomarkers, understand the pathology of disease, and devise more efficient treatments.

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