Identification of key pathways and genes in different types of chronic kidney disease based on WGCNA

Guo,Yuhe; Ma,Junjie; Xiao,Lanyan; Fang,Jiali; Li,Guanghui; Zhang,Lei; Xu,Lu; Lai,Xingqiang; Pan,Guanghui; Chen,Zheng

doi:10.3892/mmr.2019.10443

Identification of key pathways and genes in different types of chronic kidney disease based on WGCNA

Authors:
- Yuhe Guo
- Junjie Ma
- Lanyan Xiao
- Jiali Fang
- Guanghui Li
- Lei Zhang
- Lu Xu
- Xingqiang Lai
- Guanghui Pan
- Zheng Chen
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Affiliations: Department of Organ Transplantation, Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510260, P.R. China, Department of Center Laboratory, The Third Affiliated Hospital, Sun Yat‑sen University, Guangzhou, Guangdong 510700, P.R. China
Published online on: June 28, 2019 https://doi.org/10.3892/mmr.2019.10443
Pages: 2245-2257
Copyright: © Guo et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Chronic kidney disease (CKD) is a highly heterogeneous nephrosis that occurs when the structure and function of the kidney is damaged. Gene expression studies have been widely used to elucidate various biological processes; however, the gene expression profile of CKD is currently unclear. The present study aimed to identify diagnostic biomarkers and therapeutic targets using renal biopsy sample data from patients with CKD. Gene expression data from 30 patients with CKD and 21 living donors were analyzed by weighted gene co‑expression network analysis (WGCNA), in order to identify gene networks and profiles for CKD, as well as its specific characteristics, and to potentially uncover diagnostic biomarkers and therapeutic targets for patients with CKD. In addition, functional enrichment analysis was performed on co‑expressed genes to determine modules of interest. Four co‑expression modules were constructed from the WGCNA. The number of genes in the constructed modules ranged from 269 genes in the Turquoise module to 60 genes in the Yellow module. All four co‑expression modules were correlated with CKD clinical traits (P<0.05). For example, the Turquoise module, which mostly contained genes that were upregulated in CKD, was positively correlated with CKD clinical traits, whereas the Blue, Brown and Yellow modules were negatively correlated with clinical traits. Functional enrichment analysis revealed that the Turquoise module was mainly enriched in genes associated with the ‘defense response’, ‘mitotic cell cycle’ and ‘collagen catabolic process’ Gene Ontology (GO) terms, implying that genes involved in cell cycle arrest and fibrogenesis were upregulated in CKD. Conversely, the Yellow module was mainly enriched in genes associated with ‘glomerulus development’ and ‘kidney development’ GO terms, indicating that genes associated with renal development and damage repair were downregulated in CKD. The hub genes in the modules were acetyl‑CoA carboxylase α, cyclin‑dependent kinase 1, Wilm's tumour 1, NPHS2 stomatin family member, podocin, JunB proto‑oncogene, AP‑1 transcription factor subunit, activating transcription factor 3, forkhead box O1 and v‑abl Abelson murine leukemia viral oncogene homolog 1, which were confirmed to be significantly differentially expressed in CKD biopsies. Combining the eight hub genes enabled a high capacity for discrimination between patients with CKD and healthy subjects, with an area under the receiver operating characteristic curve of 1.00. In conclusion, this study provided a framework for co‑expression modules of renal biopsy samples from patients with CKD and living donors, and identified several potential diagnostic biomarkers and therapeutic targets for CKD.

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