Lei Lei, Bai Yi-Hua, Jiang Hong-Ying, He Ting, Li Meng, Wang Jia-Ping
Department of Nephrology, The Second Hospital Affiliated to Kunming Medical University, Kunming, Yunnan, China.
Department of Radiology, The Second Hospital Affiliated to Kunming Medical University, Kunming, Yunnan, China.
Endocr Connect. 2021 Oct 4;10(10):1253-1265. doi: 10.1530/EC-21-0328.
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.
据报道，N6-甲基腺嘌呤（m6A）甲基化在2型糖尿病（T2D）中发挥作用。然而，m6A甲基化的关键成分在T2D中尚未得到充分研究。本研究调查了m6A甲基化基因在T2D中的生物学作用及潜在机制。利用基因表达综合数据库（GEO）结合T2D患者的m6A甲基化和转录组数据来鉴定m6A甲基化差异表达基因（mMDEGs）。采用 Ingenuity 通路分析（IPA）预测与T2D相关的差异表达基因（DEGs）。利用基因本体（GO）术语富集分析和京都基因与基因组百科全书（KEGG）来确定mMDEGs的生物学功能。进行基因集富集分析（GSEA）以进一步确认mMDEGs的功能富集并确定候选枢纽基因。进行最小绝对收缩和选择算子（LASSO）回归分析以筛选T2D的最佳预测因子，并使用逆转录聚合酶链反应（RT-PCR）和蛋白质免疫印迹法（Western blot）验证预测因子的表达。共检测到194个重叠的mMDEGs。GO、KEGG和GSEA分析表明，mMDEGs在T2D和胰岛素信号通路中富集，其中发现了胰岛素基因（INS）、2型膜糖蛋白基因（MAFA）和己糖激酶2（HK2）基因。对候选枢纽基因的LASSO回归分析表明，INS基因可作为T2D的预测枢纽基因。INS、MAFA和HK2基因参与T2D疾病过程，但INS能更好地预测T2D的发生。
