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 be en well explored in T2D. This study investigates the biological role and the underl ying mechanism of m6A methylation genes in T2D. The Gene Expression Omnibus (GEO) dat abase 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 enric hment analysis (GSEA) was performed to further confirm the functional enrichment of mM DEGs and determine candidate hub genes. The least absolute shrinkage and selection operator (LASSO) regression analysis was carried out to screen for the best pred ictors of T2D, and RT-PCR and Western blot were used to verify the expression of the pred ictors. A total of 194 overlapping mMDEGs were detected. GO, KEGG, and GSEA analysis s howed 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 tha t 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.

Keywords

• n6-methyladenosine methylation
• type 2 diabetes
• ins
• mafa
• hk2
• bioinformatics