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. 2021 Sep 28;15(3):534–544. doi: 10.1093/ckj/sfab186

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© The Author(s) 2021. Published by Oxford University Press on behalf of the ERA.

This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

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FIGURE 1: — Identification of gene modules negatively associated with eGFR in dataset GSE30528 and GO and KEGG pathway enrichment analysis. (A) Heatmap of the correlation between MEs and clinical characters in dataset GSE30528. The MEsteelblue was the highest negatively correlated with eGFR. (B) The top 10 terms of GO analysis of all genes in the steelblue module identified by WGCNA. (C) The top 10 terms of KEGG pathway analysis of all genes in the steelblue module identified by WGCNA. GSE30528, dataset of microarray data of microdissected glomeruli samples from 9 patients with DN and 13 HCs ME, module eigengene. Source: Woroniecka KI, Park AS, Mohtat D et al. Transcriptome analysis of human diabetic kidney disease. Diabetes 2011;60(9);2354–69; PMID: 21 752 957.