Diabetic kidney disease‐predisposing proinflammatory and profibrotic genes identified by weighted gene co‐expression network analysis (WGCNA)

作者：Jing Chen, Shifu Luo, Xin Yuan, Mi Wang, Haijie Yu, Zheng Zhang, Yong‐Yu Yang · 发表于：Journal of Cellular Biochemistry · 年份：2021 · DOI：10.1002/jcb.30195 · 被引用次数：37 · 研究领域：Chronic Kidney Disease and Diabetes、GDF15 and Related Biomarkers、Ferroptosis and cancer prognosis

Diabetic kidney disease (DKD) is one of the most serious microvascular complications of diabetes. Despite enormous efforts, the underlying underpinnings of DKD remain incompletely appreciated. We sought to perform novel and informative bioinformatic analysis to explore the molecular mechanism of DKD. The gene expression profiles of GSE142025, GSE30528, and GSE30529 datasets were downloaded from the Gene Expression Omnibus database. After the GSE142025 data set was preprocessed, a gene co-expression network was constructed by weighted gene co-expression network analysis (WGCNA), and hub genes were selected in the key modules. Meanwhile, differentially expressed genes (DEGs) upregulated commonly were identified between the GSE30528 and GSE30529 datasets. Then, pathway and process enrichment analysis were performed for hub genes and commonly upregulated DEGs. Next, candidate targets were identified by comparing hub genes to commonly upregulated DEGs. Finally, reverse-transcription quantitative polymerase chain reaction (RT-qPCR) was carried out to validate the expression of candidate targets, and protein-protein interaction (PPI) network was constructed. A total of 17 modules were clustered by WGCNA, and the most significant turquoise module was selected. Based upon MM > 0.7 and GM > 0.7, 313 hub genes were screened out in turquoise module. Functional analysis of these 313 genes demonstrated their enrichment in pathways involved in leukocyte differentiation, cell morphogenesis, ...