机构：[1]Department of Endocrinology, The First Affiliated Hospital of Kunming Medical University, Kunming, China,[2]Department of Geriatric Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, China
Background: 5-methylcytosine (m5C) RNA methylation plays a significant role
in several human diseases. However, the functional role of m5C in type
2 diabetes (T2D) remains unclear.
Methods: The merged gene expression profiles from two Gene Expression
Omnibus (GEO) datasets were used to identify m5C-related genes and T2Drelated
differentially expressed genes (DEGs). Least-absolute shrinkage and
selection operator (LASSO) regression analysis was performed to identify
optimal predictors of T2D. After LASSO regression, we constructed a
diagnostic model and validated its accuracy. Gene ontology (GO) and Kyoto
Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to
confirm the biological functions of DEGs. Gene Set Enrichment Analysis (GSEA)
was used to determine the functional enrichment of molecular subtypes.
Weighted gene co-expression network analysis (WGCNA) was used to select
the module that correlated with the most pyroptosis-related genes. Proteinprotein
interaction (PPI) network was established using the STRING database,
and hub genes were identified using Cytoscape software. The competitive
endogenous RNA (ceRNA) interaction network of the hub genes was obtained.
The CIBERSORT algorithm was applied to analyze the interactions between hub
gene expression and immune infiltration.
Results: m5C-related genes were significantly differentially expressed in T2D
and correlated with most T2D-related DEGs. LASSO regression showed that
ZBTB4 could be a predictive gene for T2D. GO, KEGG, and GSEA indicated that
the enriched modules and pathways were closely related to metabolismrelated
biological processes and cell death. The top five genes were
identified as hub genes in the PPI network. In addition, a ceRNA interaction
network of hub genes was obtained. Moreover, the expression levels of the hub
genes were significantly correlated with the abundance of various immune
cells.
Conclusion: Our findings may provide insights into the molecular mechanisms
underlying T2D based on its pathophysiology and suggest potential biomarkers
and therapeutic targets for T2D.
基金：
Joint Program of Applied Basic
Research of Yunnan Provincial Department of Science and
Technology—Kunming Medical University [2019FE001
(-057)], sci-tech innovation team construction project of
Kunming Medical University [CXTD202106], and Yunnan Province Clinical Research Center for Metabolic Disease
[202102AA100056].
第一作者机构：[1]Department of Endocrinology, The First Affiliated Hospital of Kunming Medical University, Kunming, China,
共同第一作者：
通讯作者：
通讯机构：[1]Department of Endocrinology, The First Affiliated Hospital of Kunming Medical University, Kunming, China,
推荐引用方式(GB/T 7714)：
Yaxian Song,Yan Jiang,Li Shi,et al.Comprehensive analysis of key m5C modification-related genes in type 2 diabetes[J].Frontier in Genetics.2022,13:1015879.doi:10.3389/fgene.2022.1015879.
APA：
Yaxian Song,Yan Jiang,Li Shi,Chen He,Wenhua Zhang...&Yushan Xu.(2022).Comprehensive analysis of key m5C modification-related genes in type 2 diabetes.Frontier in Genetics,13,
MLA：
Yaxian Song,et al."Comprehensive analysis of key m5C modification-related genes in type 2 diabetes".Frontier in Genetics 13.(2022):1015879
