Exploring the Differences in Molecular Mechanisms and Key Biomarkers Between Membranous Nephropathy and Lupus Nephritis Using Integrated Bioinformatics Analysis

Zhaocheng Dong; Haoran Dai; Wenbin Liu; Hanxue Jiang; Zhendong Feng; Fei Liu; Qihan Zhao; Hongliang Rui; Wei Jing Liu; Baoli Liu

doi:10.3389/fgene.2021.770902

Exploring the Differences in Molecular Mechanisms and Key Biomarkers Between Membranous Nephropathy and Lupus Nephritis Using Integrated Bioinformatics Analysis

Front Genet. 2022 Jan 3:12:770902. doi: 10.3389/fgene.2021.770902. eCollection 2021.

Authors

Zhaocheng Dong^{1

2}, Haoran Dai³, Wenbin Liu⁴, Hanxue Jiang¹, Zhendong Feng⁵, Fei Liu^{1

4}, Qihan Zhao^{1

6}, Hongliang Rui¹, Wei Jing Liu², Baoli Liu^{1

3}

Affiliations

¹ Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, China.
² Renal Research Institution of Beijing University of Chinese Medicine, and Key Laboratory of Chinese Internal Medicine of Ministry of Education and Beijing, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
³ Shunyi Branch, Beijing Traditional Chinese Medicine Hospital, Beijing, China.
⁴ Beijing University of Chinese Medicine, Beijing, China.
⁵ Beijing Chinese Medicine Hospital Pinggu Hospital, Beijing, China.
⁶ Capital Medical University, Beijing, China.

Abstract

Background: Both membranous nephropathy (MN) and lupus nephritis (LN) are autoimmune kidney disease. In recent years, with the deepening of research, some similarities have been found in the pathogenesis of these two diseases. However, the mechanism of their interrelationship is not clear. The purpose of this study was to investigate the differences in molecular mechanisms and key biomarkers between MN and LN. Method: The expression profiles of GSE99325, GSE99339, GSE104948 and GSE104954 were downloaded from GEO database, and the differentially expressed genes (DEGs) of MN and LN samples were obtained. We used Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for enrichment analysis of DEGs. A protein-protein interaction (PPI) network of DEGs was constructed using Metascape. We filtered DEGs with NetworkAnalyst. Finally, we used receiver operating characteristic (ROC) analysis to identify the most significant DEGs for MN and LN. Result: Compared with LN in the glomerulus, 14 DEGs were up-regulated and 77 DEGs were down-regulated in MN. Compared with LN in renal tubules, 21 DEGs were down-regulated, but no up-regulated genes were found in MN. According to the result of GO and KEGG enrichment, PPI network and Networkanalyst, we screened out six genes (IFI6, MX1, XAF1, HERC6, IFI44L, IFI44). Interestingly, among PLA2R, THSD7A and NELL1, which are the target antigens of podocyte in MN, the expression level of NELL1 in MN glomerulus is significantly higher than that of LN, while there is no significant difference in the expression level of PLA2R and THSD7A. Conclusion: Our study provides new insights into the pathogenesis of MN and LN by analyzing the differences in gene expression levels between MN and LN kidney samples, and is expected to be used to prepare an animal model of MN that is more similar to human.

Keywords: biomarker; differential gene analysis; integrated bioinformatics analysis; lupus nephritis; membranous nephropathy.