ArticleArchives of endocrinology and metabolism2023
Integrated bioinformatics approach reveals methylation-regulated differentially expressed genes in obesity.
Article in Archives of endocrinology and metabolism, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 2 citations in OpenAlex.
- Integrative network analysis of differentially methylated regions to study the impact of gestational weight gain on maternal metabolism and fetal-neonatal growth.Genetics and molecular biology · 2024Article
- Bioinformatics analysis of the association between obesity and gastric cancer.Frontiers in genetics · 2024Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To identify DNA methylation and gene expression profiles involved in obesity by implementing an integrated bioinformatics approach. Materials and methods: Gene expression (GSE94752, GSE55200, and GSE48964) and DNA methylation (GSE67024 and GSE111632) datasets were obtained from the GEO database. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) in subcutaneous adipose tissue of patients with obesity were identified using GEO2R. Methylation-regulated DEGs (MeDEGs) were identified by overlapping DEGs and DMGs. The protein-protein interaction (PPI) network was constructed with the STRING database and analyzed using Cytoscape. Functional modules and hub-bottleneck genes were identified by using MCODE and CytoHubba plugins. Functional enrichment analyses were performed based on Gene Ontology terms and KEGG pathways. To prioritize and identify candidate genes for obesity, MeDEGs were compared with obesity-related genes available at the DisGeNET database. Results: A total of 54 MeDEGs were identified after overlapping the lists of significant 274 DEGs and 11,556 DMGs. Of these, 25 were hypermethylated-low expression genes and 29 were hypomethylated-high expression genes. The PPI network showed three hub-bottleneck genes ( Conclusion: This study identifies new MeDEGs involved in obesity and assessed their related pathways and functions. These results data may provide a deeper understanding of methylation-mediated regulatory mechanisms of obesity.
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