Identification of key genes in membranous nephropathy and non-alcoholic fatty liver disease by bioinformatics and machine learning - Fan, Jiachen; Li, Na; Lu, Yanfang; Cao, Huixia (Frontiers in Immunology, 2025)
BackgroundChronic kidney disease (CKD) and non-alcoholic fatty liver disease (NAFLD) are closely associated. However, membranous nephropathy (MN), one of the causes of CKD, may contribute to NAFLD through abnormalities in lipid metabolism.Methods93 patients diagnosed with MN by renal biopsy and admitted to Henan Provincial People’s Hospital between August 2021 and August 2022 were enrolled in this study. Patients were divided into two groups based on the presence or absence of NAFLD. Publicly available datasets related to NAFLD and MN were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, and weighted gene co-expression network analysis (WGCNA) was conducted to identify module genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. A protein-protein interaction (PPI) network was constructed, and key genes associated with both diseases were identified using Cytoscape software and machine learning algorithms. The correlation between immune cell infiltration and the two diseases was evaluated using the CIBERSORT algorithm. Finally, the key gene expression was validated using external datasets and immunohistochemistry (IHC).ResultsCompared with the non-NAFLD group, patients in the NAFLD group had significantly higher body weight, hemoglobin levels, triglycerides, and complement C3 and C4 levels. Conversely, IgG levels were significantly lower in the NAFLD group. A total of 211 shared DEGs were identified between MN and NAFLD, including 175 upregulated and 36 downregulated genes. Enrichment analysis indicated that these genes were primarily involved in immune and inflammatory responses. PPI network analysis identified seven hub genes: CSF1R, FCGR1G, FCGR3A, VAV1, SPI1, HCK, and CCR1. Among them, CSF1R was identified as the key gene using a machine learning approach.ConclusionThis study suggests that CSF1R is a shared molecular of MN and NAFLD, which may serve as a potential therapeutic target for patients affected by both diseases.