Screening Differential Hub Genes Related with the Hypoglycemic Effect of Quercetin Through Data Mining

Title:Screening Differential Hub Genes Related with the Hypoglycemic Effect of Quercetin Through Data Mining

Volume: 16 Issue: 9

Author(s): Ji-Ping Wei, Tao Luo*, Yuchen Wang and Wenyu Lu*

Affiliation:

• College of Intelligence and Computing, Tianjin University, Tianjin, 300350,China

• School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300350,China

Keywords: Quercetin, Differential Hub Gene (DHGs), hypoglycemic effect, screening, Weighted Gene Co-Expression NetworkAnalysis (WGCNA), systematic mining.

Abstract:

Background: The effect of quercetin on blood glucose levels has been widely studied. However, the mechanism of hypoglycemic effect of quercetin remains unclear.

Objective: To elucidate hypoglycemic effect of quercetin, microarray data of GSE38067 dataset have been used to screen Differential Hub Genes (DHGs) by differential expression analysis, weighted gene co-expression network analysis and protein-protein interaction analysis.

Methods: Through systematic data mining in this study, the hypoglycemic effect of quercetin was exerted via affecting the gene expression of seven candidate DHGs, especially Cdkn1a and Cd36 genes, to relieve insulin resistance, prevent oxidative damage and protect pancreatic β-cells in streptozotocin (STZ) induced diabetic mice.

Result: As a result, this work provides a possible way to fight against diabetes by using quercetin as functional food ingredients or medicine.

Conclusion: Microarray data of GSE38067 dataset were analyzed by differential expression analysis, WGCNA, and PPI analysis to identify DHGs of hypoglycemic effect of quercetin.

Cite this article as:

Wei Ji-Ping , Luo Tao *, Wang Yuchen and Lu Wenyu *, Screening Differential Hub Genes Related with the Hypoglycemic Effect of Quercetin Through Data Mining, Current Bioinformatics 2021; 16 (9) . https://dx.doi.org/10.2174/1574893616666210617110314