THU271 Identification Of Candidate Biomarkers For Type 1 Diabetes Mellitus By Bioinformatics Analysis Of Pooled Microarray Gene Expression Datasets In Gene Expression Omnibus

Abstract

Abstract Disclosure: K. Feng: None. W. Chen: None. Background: Type 1 diabetes (T1DM) is a serious threat to childhood life and has a complicated pathogenesis. Currently, molecular mechanisms of T1DM remain largely unclear. The aim of this study was to identify the candidate genes in T1DM by integrated bioinformatics analysis. Methods: Transcriptomic datasets (GSE156035) in the GEO database were analyzed for differentially expressed genes (DEGs) using the R statistical language. The differentially expressed genes (DEGs) were identified, and the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. A protein - protein interaction (PPI) network was applied to screen out the candidate genes. Results: The results revealed that 273 DEGs of the three datasets were ascertained in our study, including 135 upregulated genes and 138 downregulated genes. The GO and KEGG enrichment analysis results showed that the functions of DEGs mainly involved in regulation of transcription from RNA polymerase II promoter, specific granule lumen, transcriptional activator activity, Osteoclast differentiation pathway, etc. Through the PPI analysis network, the core genes with the highest degree of 6 nodes were selected: FOS, RHOA, CXCL8, FOSB, EGR1, and DUSP1. Conclusion: The genes, identified in this study, may play a vital regulatory role in the occurrence and development of T1DM. Also, they are closely related to obesity and diabetes mellitus. Our results provide novel biomarkers that could be used as representa­tive reference indicators or potential therapeutic targets for T1DM clinical applications. Presentation: Thursday, June 15, 2023