Analysis of differentially expressed genes in visceral adipose tissue of patients with type 2 diabetes mellitus based on bioinformatics
10.3760/cma.j.cn115624-20240829-00693
- VernacularTitle:基于生物信息学的2型糖尿病患者内脏脂肪差异表达基因分析
- Author:
Ying LI
1
;
Jiaxiang LU
;
Lu HE
;
Xiaojie XIE
;
Ren LIN
;
Shiqi TANG
;
Lijuan XU
Author Information
1. 武汉大学人民医院健康管理中心，武汉 430060
- Keywords:
Diabetes mellitus, type 2;
Visceral adipose tissue;
Gene expression;
Bioinformatics
- From:
Chinese Journal of Health Management
2024;18(12):910-915
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To analyze the differentially expressed genes (DEGs) in visceral adipose tissue of patients with type 2 diabetes mellitus (T2DM) based on bioinformatics.Methods:The microarray dataset GSE78721 was downloaded from the Gene Expression Omnibus (GEO) database, including visceral fat samples data from 19 T2DM patients and 16 non-diabetic subjects. The analysis of transcriptomic profiling results from tissue samples was conducted, and a comparison between different groups of samples based on gender was performed. The online Xintao Academic Database was utilized for the analysis, employing the "limma" package in R language to filter DEGs. Subsequently, the DEGs were visualized, and Gene Ontology (GO) functional annotation along with Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were carried out and visualized. Based on the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, a protein-protein interaction network (PPI) of DEGs was constructed and key differentially expressed genes were identified and visualized using Cytoscape software.Results:Analysis of visceral adipose tissue gene expression profiles revealed 168 DEGs (|log 2FC|≥1, P<0.05). In females, 42 mRNAs were up-regulated, 3 were down-regulated; in males, 105 were up-regulated, 37 were down-regulated, 19 genes were shared by the two groups. GO analysis linked DEGs to insulin-like growth factor receptor signaling and regulation, nutrient response, and leukocyte migration. KEGG analysis implicated extracellular matrix receptor interactions and leukocyte transendothelial migration. The PPI network unveiled 10 key genes, including COL1A1, COL1A2, TGFB3, PCOLCE, TIMP1, COL6A2, COMP, COL14A1, VCAM1 and THY1. Conclusion:Bioinformatics technology can effectively analyze and screen DEGs in visceral adipose tissue of T2DM patients, providing useful clues for further exploring its molecular mechanism and finding therapeutic targets.
