4.6 Article

Comprehensive analysis of epigenomics and transcriptome data to identify potential target genes associated with obesity

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FRONTIERS IN GENETICS
卷 13, 期 -, 页码 -

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FRONTIERS MEDIA SA
DOI: 10.3389/fgene.2022.1024300

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obesity; methylation; immune infiltration; metabolism; comprehensive analysis

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This study identified key genes associated with obesity through DNA methylation and transcriptome analysis and explored the potential molecular mechanisms involved in obesity pathogenesis. The study also revealed changes in the immune microenvironment of adipose tissue in the obese group and the relationship between key genes and the immune microenvironment. Additionally, the study calculated the metabolic pathway activity and constructed a key gene-metabolic network, as well as screened for potentially effective drugs for obesity treatment based on differential genes.
DNA methylation is closely related to the occurrence and development of many diseases, but its role in obesity is still unclear. This study aimed to find the potential differentially methylated genes associated with obesity occurrence and development. By combining methylation and transcriptome analysis, we identified the key genes in adipose tissue affecting the occurrence and development of obesity and revealed the possible molecular mechanisms involved in obesity pathogenesis. We first screened 14 methylation-related differential genes and verified their expression in adipose tissue by quantitative polymerase chain reaction (qPCR). Seven genes with the same expression pattern were identified as key genes, namely, CCRL2, GPT, LGALS12, PC, SLC27A2, SLC4A4, and TTC36. Then, the immune microenvironment of adipose tissue was quantified by CIBERSORT, and we found that the content of M0 macrophages and T follicular helper cells in adipose tissue was significantly increased and decreased, respectively, in the obese group. Furthermore, the relationship between key genes and the immune microenvironment was analyzed. Additionally, the metabolic pathway activity of each sample was calculated based on the ssGSEA algorithm, and the key gene-metabolic network was constructed. Moreover, we performed a CMAP analysis based on the differential genes in adipose tissue to screen out drugs potentially effective in obesity treatment. In conclusion, we identified seven methylation-related key genes closely related to obesity pathogenesis and explored the potential mechanism of their role in obesity. This study provided novel insights into the molecular mechanisms and management of obesity.

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