ArticleHeliyon2024
Integrated bioinformatics analysis for identifying fibroblast-associated biomarkers and molecular subtypes in human membranous nephropathy.
Article in Heliyon, 2024. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Unraveling the role of histone acetylation in sepsis biomarker discovery.Frontiers in molecular biosciences · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Membranous nephropathy (MN) is characterized by immune complex deposition in the glomerular basement membrane, leading to proteinuria and potentially progressive renal dysfunction. Fibroblasts have been implicated in the pathogenesis of MN through their involvement in tissue remodeling and immune modulation. Methods: We employed integrated bioinformatics analyses to identify fibroblast-associated biomarkers and molecular subtypes in MN. The xCell algorithm was used to assess fibroblast infiltration, and weighted gene co-expression network analysis (WGCNA) identified fibroblast-related gene modules. Differentially expressed fibroblast-associated genes (DEFAGs) were screened between MN and healthy controls (HC) using differential expression analysis and protein-protein interaction (PPI) network construction. Consensus clustering categorized MN patients into distinct subtypes based on DEFAG expression profiles. Results: Fibroblast scores were a significant elevation in MN compared to HC, indicating increased fibroblast involvement in MN pathogenesis. WGCNA identified 13 fibroblast-related gene modules, with the brown and turquoise modules showing strong correlation with fibroblast scores (correlation coefficient = 0.79 and 0.75, respectively, p < 0.01). DEFAG analysis revealed 308 genes overlapping between WGCNA and differentially expressed genes (DEGs) in MN. Consensus clustering identified two molecular subtypes (C1 and C2) based on DEFAG expression patterns, with differential gene expression enriching pathways related to immune response and extracellular matrix remodeling. Core biomarker analysis highlighted COL3A1 and TGFB1 as central genes associated with MN, with elevated expression validated across multiple datasets. Conclusion: Integrated bioinformatics analysis identified fibroblast-associated molecular subtypes in MN, revealing distinct immune profiles and biomarkers. COL3A1 emerged as a potential diagnostic and therapeutic target, implicating its role in immune regulation and disease progression in MN.
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