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Diabetic kidney disease-predisposing proinflammatory and profibrotic genes identified by weighted gene co-expression network analysis (WGCNA)
Chen, Jing1; Luo, Shi Fu1; Yuan, Xin1; Wang, Mi2; Yu, Hai Jie3; Zhang, Zheng1,4; Yang, Yong Yu5
2022-02-01
Source PublicationJournal of Cellular Biochemistry
ISSN0730-2312
Volume123Issue:2Pages:481-492
Abstract

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, lymphocyte activation, vascular development, collagen synthesis, chemotaxis, and chemokine signaling. A total of 115 commonly upregulated DEGs were identified between the GSE30528 and GSE30529 datasets. Intriguingly, a total of six proinflammatory and profibrotic candidate targets were selected and validated in DKD mice in vivo, including CCR2, MOXD1, COL6A3, COL1A2, PYCARD, and C7. Based on WGCNA and DEG analysis of DKD datasets, six DKD-predisposing candidate targets were uncovered. The data suggest that inflammation and fibrosis are key mechanisms of DKD, and future studies may determine the causal link between the six proinflammatory and profibrotic genes and DKD.

KeywordDiabetic Kidney Disease Differentially Expressed Gene Weighted Gene Co-expression Network Analysis
DOI10.1002/jcb.30195
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaBiochemistry & Molecular Biology ; Cell Biology
WOS SubjectBiochemistry & Molecular Biology ; Cell Biology
WOS IDWOS:000730013300001
PublisherWILEY111 RIVER ST, HOBOKEN 07030-5774, NJ
Scopus ID2-s2.0-85121305968
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorZhang, Zheng; Yang, Yong Yu
Affiliation1.Department of Pharmacology, Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan, China
2.Department of Cardiology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
3.Dr Neher's Biophysics Laboratory for Innovative Drug Discovery/State Key laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macau, China
4.Hunan Provincial Key Laboratory of Cardiovascular Research, Central South University, Changsha, Hunan, China
5.Department of Pharmacy, the Second Xiangya Hospital of Central South University, Changsha, Hunan, China
Recommended Citation
GB/T 7714
Chen, Jing,Luo, Shi Fu,Yuan, Xin,et al. Diabetic kidney disease-predisposing proinflammatory and profibrotic genes identified by weighted gene co-expression network analysis (WGCNA)[J]. Journal of Cellular Biochemistry, 2022, 123(2), 481-492.
APA Chen, Jing., Luo, Shi Fu., Yuan, Xin., Wang, Mi., Yu, Hai Jie., Zhang, Zheng., & Yang, Yong Yu (2022). Diabetic kidney disease-predisposing proinflammatory and profibrotic genes identified by weighted gene co-expression network analysis (WGCNA). Journal of Cellular Biochemistry, 123(2), 481-492.
MLA Chen, Jing,et al."Diabetic kidney disease-predisposing proinflammatory and profibrotic genes identified by weighted gene co-expression network analysis (WGCNA)".Journal of Cellular Biochemistry 123.2(2022):481-492.
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