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

Shaokang Pan; Zhengyong Li; Yixue Wang; Lulu Liang; Fengxun Liu; Yingjin Qiao; Dongwei Liu; Zhangsuo Liu

doi:10.2478/jtim-2022-0053

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

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

Authors

Shaokang Pan¹, Zhengyong Li¹, Yixue Wang¹, Lulu Liang¹, Fengxun Liu¹, Yingjin Qiao¹, Dongwei Liu¹, Zhangsuo Liu¹

Affiliation

¹ Department of TCM-Integrated Department of Nephrology, the First Affiliated Hospital of Zhengzhou University; Research Institute of Nephrology, Zhengzhou University; Research Center for Kidney Disease, Henan Province; Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province; Core Unit of National Clinical Medical Research Center of Kidney Disease, Zhengzhou 450052, Henan Province, China.

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.