ArticleJournal of immunology research2022
Identification of Important Modules and Hub Gene in Chronic Kidney Disease Based on WGCNA.
Article in Journal of immunology research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 11 citations in OpenAlex.
- Dissecting lncRNA-mRNA regulatory network in type 2 diabetes as the risk factor of pancreatic cancer.Scientific reports · 2025Article
- Currently Used Methods to Evaluate the Efficacy of Therapeutic Drugs and Kidney Safety.Biomolecules · 2023Review
- Identification of AGXT2, SHMT1, and ACO2 as important biomarkers of acute kidney injury by WGCNA.PloS one · 2023Article
- Identification of immune-related molecular clusters and diagnostic markers in chronic kidney disease based on cluster analysis.Frontiers in genetics · 2023Article
- An integrated co-expression network analysis reveals novel genetic biomarkers for immune cell infiltration in chronic kidney disease.Frontiers in immunology · 2023Article
- Identification of diagnostic markers related to oxidative stress and inflammatory response in diabetic kidney disease by machine learning algorithms: Evidence from human transcriptomic data and mouse experiments.Frontiers in endocrinology · 2023Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
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Abstract
Chronic kidney disease (CKD) is an ongoing deterioration of renal function that often progresses to end-stage renal disease. In this study, we aimed to screen and identify potential key genes for CKD using the weighted gene coexpression network (WGCNA) analysis tool. Gene expression data related to CKD were screened from GEO database, and expression datasets of GSE66494 and GSE62792 were obtained. After discrete analysis of samples, WGCNA analysis was performed to construct gene coexpression module, and the correlation between the module and disease was calculated. The modules with a significant correlation with the disease were selected for Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Then, the interaction network of related molecules was constructed, and the high score subnetwork was selected, and the candidate key molecules were identified. A total of 882 DEGs were identified in the screening datasets. A subnetwork containing 6 nodes was found with a high score of 12.08, including CEBPZ, IFI16, LYAR, BRIX1, BMS1, and DDX18. DEGs could significantly differentiate CKD and healthy individuals in principal component analysis. In addition, the MEturquiose, MEred, and MEblue in group were significantly correlated with disease in WGCNA. These 6 hub genes were found to significantly discriminate between CKD and healthy controls in the validation dataset, suggesting that they could use these molecules as candidate markers to distinguish CKD from healthy people. Overall, our study indicated that 6 hub genes may play key roles in the occurrence and development of CKD.
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