A bioinformatics analysis of the contribution of m6A methylation to the occurrence of diabetes mellitus

Abstract

N6-methyladenosine (m6A) methylation has been reported to play a role in type 2 diabetes (T2D). However, the key component of m6A methylation has not been well explored in T2D. This study investigates the biological role and the underlying mechanism of m6A methylation genes in T2D. The Gene Expression Omnibus (GEO) database combined with the m6A methylation and transcriptome data of T2D patients were used to identify m6A methylation differentially expressed genes (mMDEGs). Ingenuity pathway analysis (IPA) was used to predict T2D-related differentially expressed genes (DEGs). Gene ontology (GO) term enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to determine the biological functions of mMDEGs. Gene set enrichment analysis (GSEA) was performed to further confirm the functional enrichment of mMDEGs and determine candidate hub genes. The least absolute shrinkage and selection operator (LASSO) regression analysis was carried out to screen for the best predictors of T2D, and RT-PCR and Western blot were used to verify the expression of the predictors. A total of 194 overlapping mMDEGs were detected. GO, KEGG, and GSEA analysis showed that mMDEGs were enriched in T2D and insulin signaling pathways, where the insulin gene (INS), the type 2 membranal glycoprotein gene (MAFA), and hexokinase 2 (HK2) gene were found. The LASSO regression analysis of candidate hub genes showed that the INS gene could be invoked as a predictive hub gene for T2D. INS, MAFA,and HK2 genes participate in the T2D disease process, but INS can better predict the occurrence of T2D.