Search Results

You are looking at 1 - 1 of 1 items for

  • Author: Yi-Hua Bai x
Clear All Modify Search
Lei Lei Department of Nephrology, The Second Hospital Affiliated to Kunming Medical University, Kunming, Yunnan, China

Search for other papers by Lei Lei in
Google Scholar
PubMed
Close
,
Yi-Hua Bai Department of Nephrology, The Second Hospital Affiliated to Kunming Medical University, Kunming, Yunnan, China

Search for other papers by Yi-Hua Bai in
Google Scholar
PubMed
Close
,
Hong-Ying Jiang Department of Nephrology, The Second Hospital Affiliated to Kunming Medical University, Kunming, Yunnan, China

Search for other papers by Hong-Ying Jiang in
Google Scholar
PubMed
Close
,
Ting He Department of Nephrology, The Second Hospital Affiliated to Kunming Medical University, Kunming, Yunnan, China

Search for other papers by Ting He in
Google Scholar
PubMed
Close
,
Meng Li Department of Nephrology, The Second Hospital Affiliated to Kunming Medical University, Kunming, Yunnan, China

Search for other papers by Meng Li in
Google Scholar
PubMed
Close
, and
Jia-Ping Wang Department of Radiology, The Second Hospital Affiliated to Kunming Medical University, Kunming, Yunnan, China

Search for other papers by Jia-Ping Wang in
Google Scholar
PubMed
Close

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.

Open access