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. 2023 Jan 13;10(4):359-368.
doi: 10.2478/jtim-2022-0053. eCollection 2022 Dec.

A Comprehensive Weighted Gene Co-expression Network Analysis Uncovers Potential Targets in Diabetic Kidney Disease

Shaokang Pan  1 , Zhengyong Li  1 , Yixue Wang  1 , Lulu Liang  1 , Fengxun Liu  1 , Yingjin Qiao  1 , Dongwei Liu  1 , Zhangsuo Liu  1
Affiliations

A Comprehensive Weighted Gene Co-expression Network Analysis Uncovers Potential Targets in Diabetic Kidney Disease

Shaokang Pan et al. J Transl Int Med. .

Abstract

Background and objectives: Diabetic kidney disease (DKD) is one of the most common microvascular complications of diabetes. It has always been difficult to explore novel biomarkers and therapeutic targets of DKD. We aimed to identify new biomarkers and further explore their functions in DKD.

Methods: The weighted gene co-expression network analysis (WGCNA) method was used to analyze the expression profile data of DKD, obtain key modules related to the clinical traits of DKD, and perform gene enrichment analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the mRNA expression of the hub genes in DKD. Spearman's correlation coefficients were used to determine the relationship between gene expression and clinical indicators.

Results: Fifteen gene modules were obtained via WGCNA analysis, among which the green module had the most significant correlation with DKD. Gene enrichment analysis revealed that the genes in this module were mainly involved in sugar and lipid metabolism, regulation of small guanosine triphosphatase (GTPase) mediated signal transduction, G protein-coupled receptor signaling pathway, peroxisome proliferator-activated receptor (PPAR) molecular signaling pathway, Rho protein signal transduction, and oxidoreductase activity. The qRT-PCR results showed that the relative expression of nuclear pore complex-interacting protein family member A2 (NPIPA2) and ankyrin repeat domain 36 (ANKRD36) was notably increased in DKD compared to the control. NPIPA2 was positively correlated with the urine albumin/creatinine ratio (ACR) and serum creatinine (Scr) but negatively correlated with albumin (ALB) and hemoglobin (Hb) levels. ANKRD36 was positively correlated with the triglyceride (TG) level and white blood cell (WBC) count.

Conclusion: NPIPA2 expression is closely related to the disease condition of DKD, whereas ANKRD36 may be involved in the progression of DKD through lipid metabolism and inflammation, providing an experimental basis to further explore the pathogenesis of DKD.

Keywords: ANKRD36; NPIPA2; WGCNA; bioinformatics analysis; diabetic kidney disease.

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Conflict of interest statement

Conflict of Interest Dongwei Liu is an Editorial Board Member of the journal. The article was subject to the journal's standard procedures, and peer review was handled independently of this editor and his research groups.

Figures

Figure 1
Figure 1
The differentially expressed genes are shown as a volcano map. Red dots represent upregulated genes, green dots represent downregulated genes, and black dots represent genes with no significant difference in expression.
Figure 2
Figure 2
Sample hierarchical clustering diagram.
Figure 3
Figure 3
Screening of soft thresholds. Analysis of the scale-free fit index (left) and the mean connectivity (right) for various soft-thresholding powers.
Figure 4
Figure 4
Gene cluster tree. The upper part of the figure is a diagram of the cluster tree, and the lower part is a cluster of gene modules with similar expression patterns. Different colors represent different modules.
Figure 5
Figure 5
Correlation analysis between gene modules and clinical information. (A) Module-phenotype correlation diagram. The horizontal axis was for DKD and sex, and the vertical axis contained the gene co-expression modules. (B) Adjacent heat map of the featured vector genes. The upper part of the figure is a cluster tree composed of the modules and clinical phenotypes of DKD. The lower part of the figure is the corresponding heat map of the cluster tree. (C) Scatter plot showing the correlation between the green module genes and DKD. The horizontal axis represents the degree of module membership, and the vertical axis represents gene significance. DKD: diabetic kidney disease.
Figure 6
Figure 6
Gene enrichment analysis of the green module. Gene ontology enrichment (A) and Kyoto Encyclopedia of Genes and Genome pathway enrichment analysis (B). PPAR: peroxisome proliferator-activated receptor.
Figure 7
Figure 7
Selection of hub genes. (A) Visualization of the gene co-expression network created using Cytoscape software. The node size represents the connectivity between the modules. (B) The intersection between differentially expressed genes (DEGs) and key genes is shown as a Venn diagram. DEGs: differentially expressed genes.
Figure 8
Figure 8
NPIPA2 and ANKRD36 were highly expressed in DKD. The relative expression levels of NPIPA2(A) and ANKRD36(B) in each group were detected via qRT-PCR. CON: healthy control group; DM: diabetes; DKD: diabetic kidney disease. **P < 0.01, as compared to CON.
Figure 9
Figure 9
Correlation analysis between the relative expression level of NPIPA2 and clinical indicators in patients with DKD. DKD: diabetic kidney disease; ACR: urine albumin/creatinine ratio; Scr: serum creatinine; ALB: albumin; Hb: hemoglobin.
Figure 10
Figure 10
Correlation analysis between the relative expression level of ANKRD36 and clinical indicators in patients with DKD. DKD: diabetic kidney disease; ACR: urine albumin/creatinine ratio; Scr: serum creatinine; TG: triglycerides; WBC: white blood cells.
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