Evidence map›Paper›PMID 40539070›Full record

ArticleFrontiers in immunology2025

Identification of key genes in membranous nephropathy and non-alcoholic fatty liver disease by bioinformatics and machine learning.

Jiachen Fan, Na Li, Yanfang Lu, Huixia Cao

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Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Jiachen FanPeople's Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Na LiHenan Provincial Key Laboratory of Kidney Disease and Immunology, Henan Provincial Clinical Research Center for Kidney Disease, Henan Provincial People's Hospital, Zhengzhou, Henan, China.
Yanfang LuHenan Provincial Key Laboratory of Kidney Disease and Immunology, Henan Provincial Clinical Research Center for Kidney Disease, Henan Provincial People's Hospital, Zhengzhou, Henan, China.
Huixia CaoHenan Provincial Key Laboratory of Kidney Disease and Immunology, Henan Provincial Clinical Research Center for Kidney Disease, Henan Provincial People's Hospital, Zhengzhou, Henan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Chronic 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. Methods: 93 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). Results: Compared 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: Conclusion: This 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.

Indexed as

Glomerulonephritis, MembranousMachine LearningNon-alcoholic Fatty Liver DiseaseReceptor, Macrophage Colony-Stimulating FactorAdultAnimalsComputational BiologyDatasets as TopicHumansLipid MetabolismMiceMonocytesRenal Insufficiency, ChronicSingle-Cell Gene Expression AnalysisTranscriptomeCSF1R protein, humanReceptor, Macrophage Colony-Stimulating Factorbioinformaticsimmunemachine learningmembranous nephropathynon-alcoholic fatty liver diseasesingle-cell RNA-seq

Identifiers

PMID40539070
PMCPMC12176592

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