ORIGINAL RESEARCH article
Front. Med.
Sec. Nephrology
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1612200
This article is part of the Research TopicAI in Integrative BioinformaticsView all articles
Bioinformatic Analysis, Clinical Implications and Experimental Validation of Ferroptosis-Related Feature Gene in IgA Nephropathy: Focus on DUSP1
Provisionally accepted
- 1Department of Nephrology, The first hospital of Jilin University, Changchun, Hebei Province, China
- 2Department of Anaesthesiology, The First Hospital of Jilin University, changchun, China
- 3Department of Nephrology, The Affiliated Hospital to Changchun University of Chinese Medicine, changchun, China
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Background: Immunoglobulin A nephropathy (IgAN), recognized as the leading cause of primary glomerular disease worldwide, continues to present unresolved complexities in its underlying pathogenic mechanisms. Emerging evidence underscores ferroptosis, an iron-mediated regulated cell death pathway driven by the accumulation of lipid peroxides, as a potential contributor to various pathological conditions. Despite growing interest in this field, the exact molecular pathways governing ferroptosis activation in IgAN progression remain incompletely understood and require systematic investigation. The aim of this study was to identify ferroptosis-related feature gene (FFG) for the potential diagnosis of IgAN and to investigate its relationship with renal immune cell infiltration.Methods: Renal tissue microarray datasets (GSE93798, GSE104948, GSE99339) from IgAN patients and normal controls were retrieved from GEO database. The ferroptosis-related genes were obtained from the Ferrb database. Machine learning algorithms (LASSO, SVM-RFE, random forest) were employed to screen ferroptosis-related FFGs. The findings were validated in an IgAN mouse model using immunohistochemistry and western blotting. Gene set enrichment analysis (GSEA) was conducted to explore the underlying mechanism of FFG in IgAN. Immune cell infiltration characteristics were also analyzed vis CIBERSORT algorithm.Results: A total of 180 ferroptosis-related differentially expressed genes were identified in IgAN.Among them, dual specificity phosphatase 1 (DUSP1) was screened as FFG by three machine learning algorithms. DUSP1 exhibited significant downregulation in renal tissues of both IgAN patients and mice. Enhanced transcriptional abundance demonstrated significant positive associations with ferroptosis-associated biomarkers glutathione peroxidase-4 (GPX4) and cystine/glutamate antiporter (SLC7A11/xCT), while displaying an inverse relationship with acyl-CoA synthetase long-chain isoform 4 (ACSL4) expression. GSEA further identified DUSP1's functional enrichment in critical signaling networks, particularly mitogen-activated protein kinase (MAPK) cascades, ERBB receptor tyrosine kinase pathways, and Janus kinase-signal transducer (JAK-STAT) transduction mechanisms.Immunoinfiltration analysis demonstrated increased infiltration of T follicular helper cells, activated NK cells, and M1 macrophages in the renal tissues of IgAN patients, with DUSP1 expression showing negative correlations with these proinflammatory cell types.Our research successfully identified DUSP1 as a ferroptosis-related biomarker in IgAN patients, and explored its potential mechanism in the pathogenesis of IgAN and its potential
Keywords: IgA nephropathy, ferroptosis, machine learning, DUSP1, Immune Cell Infiltration
Received: 25 Apr 2025; Accepted: 28 Jul 2025.
Copyright: © 2025 Liu, Pan, Chang, Fu, Xu, Wu, Xu and Cheng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Zhonggao Xu, Department of Nephrology, The first hospital of Jilin University, Changchun, Hebei Province, China
Yanli Cheng, Department of Nephrology, The first hospital of Jilin University, Changchun, Hebei Province, China
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