Evidence map›Paper›PMID 40852352›Full record

ArticleFrontiers in medicine2025

Bioinformatic analysis, clinical implications and experimental validation of ferroptosis-related feature gene in IgA nephropathy: focus on DUSP1.

Tingting Liu, Tingting Pan, Mingxin Chang, Shaojie Fu, Hongzhao Xu, Hao Wu, Zhonggao Xu, Yanli Cheng

Abstract read
In one paragraph

Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. The Impact of Omega Fatty Acids on DKD: A Multimodal Study Integrating Mendelian Randomization, Proteomic Mediation Analysis, and Meta-Analysis.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026
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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Tingting LiuDepartment of Nephrology, The First Hospital of Jilin University, Changchun, China.
Tingting PanDepartment of Anaesthesiology, The First Hospital of Jilin University, Changchun, China.
Mingxin ChangNephrology Department, The Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, Jilin, China.
Shaojie FuDepartment of Nephrology, The First Hospital of Jilin University, Changchun, China.
Hongzhao XuDepartment of Nephrology, The First Hospital of Jilin University, Changchun, China.
Hao WuDepartment of Nephrology, The First Hospital of Jilin University, Changchun, China.
Zhonggao XuDepartment of Nephrology, The First Hospital of Jilin University, Changchun, China.
Yanli ChengDepartment of Nephrology, The First Hospital of Jilin University, Changchun, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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 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. Conclusion: 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 relationship with immune cell infiltration. These findings are of great significance for the diagnosis and prospective treatment strategies for IgAN patients.

Indexed as

DUSP1ferroptosisIgA nephropathyimmune cell infiltrationmachine learning

Identifiers

PMID40852352
PMCPMC12367746

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.