Integrated bioinformatics approach reveals methylation-regulated differentially expressed genes in obesity
Duarte GCK., Pellenz F., Crispim D., Assmann TS.
Prospective Study, published in Arch Endocrinol Metab (2023) — summary generated from the PubMed abstract.
Early human evidence such as case series or small samples is exploring possible benefits.
- Level A · Stronger Clinical Evidence
- Level B · Emerging clinical evidence with positive signals
- Level C · Early human research exploring benefits
- Level D · Scientific groundwork from lab and animal studies
- Emerging · Emerging topic under active research
This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.
- Study type
- Prospective Study
- Journal
- Arch Endocrinol Metab (2023)
- Reported sample size
- —
- Source database
- Europe PMC
- PMID
- 37252693
- PMCID
- PMC10665070
- DOI
- 10.20945/2359-3997000000604
- Citations
- 2
Abstract (original English)
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 ( PTGS2 , TNFAIP3 , and FBXL20 ) and one functional module. The 54 MeDEGs were mainly involved in the regulation of fibroblast g
What this study does not prove
- • This study does not prove SVF is an approved treatment or a replacement for standard care.
Evidence level
Early human evidence such as case series or small samples is exploring possible benefits.
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