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基于生物信息学分析和实验验证的狼疮性肾炎铁死亡标志物综合分析

Comprehensive Analysis of Ferroptosis Markers in Lupus Nephritis Based on Bioinformatics Analysis and Experimental Validation.

作者信息

Zhang Su, Hu Weitao, Huang Chunyan, Lin Xinxin, Chen Xiaoqing

机构信息

The Second Clinical College of Fujian Medical University, Quanzhou, People's Republic of China.

Department of Rheumatology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, People's Republic of China.

出版信息

J Inflamm Res. 2025 Aug 12;18:10855-10871. doi: 10.2147/JIR.S527545. eCollection 2025.

DOI:10.2147/JIR.S527545
PMID:40823359
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12357583/
Abstract

INTRODUCTION

Lupus nephritis (LN) is a common and severe complication of systemic lupus erythematosus (SLE). Ferroptosis is a form of iron-dependent cell death induced by lipid peroxidation. However, its specific role in LN remains unclear.

METHODS

We utilized the GSE32591 obtained from the GEO database to identify differentially expressed genes (DEGs) and conduct enrichment analysis. Differentially expressed ferroptosis-related genes of LN (LNDE-FRGs) were derived by taking the intersection of DEGs and ferroptosis-related genes (FRGs). Three machine learning algorithms were applied to screen candidate key LNDE-FRGs. The expression of the key LNDE-FRGs was validated using external validation cohorts and clinical samples. The diagnostic value of the key LNDE-FRGs was then assessed by receiver operating characteristic curve (ROC) analysis. In addition, we investigated the correlation between the key genes and glomerular filtration rate (GFR), urinary protein and serum creatinine (Scr) in LN patients via the Nephroseq V5 database. Subsequently, we performed immune infiltrating cell analysis of LN kidney tissue using Cibersort. Finally, we validated the expression of the key gene CYBB by clinical samples and in vivo experiments.

RESULTS

A total of 377 DEGs and 20 LNDE-FRGs were identified. Machine learning algorithms selected four candidate key LNDE-FRGs (, , , and ). However, only exhibited consistent expression trends in both the training and validation cohorts ( < 0.05). Immune infiltration analysis revealed that the expression levels of monocytes and M0 macrophages were significantly higher in the LN group than in the normal control group. In addition, there was a correlation between key genes and GFR, urinary protein and Scr. Finally, the expression level of was verified in lupus mice.

CONCLUSION

may be a ferroptosis-related biomarker in LN. This may provide new ideas for the clinical treatment and pathogenesis of LN.

摘要

引言

狼疮性肾炎(LN)是系统性红斑狼疮(SLE)常见且严重的并发症。铁死亡是一种由脂质过氧化诱导的铁依赖性细胞死亡形式。然而,其在LN中的具体作用仍不清楚。

方法

我们利用从基因表达综合数据库(GEO)获得的GSE32591来鉴定差异表达基因(DEGs)并进行富集分析。通过取DEGs与铁死亡相关基因(FRGs)的交集,得出LN的差异表达铁死亡相关基因(LNDE - FRGs)。应用三种机器学习算法筛选候选关键LNDE - FRGs。使用外部验证队列和临床样本验证关键LNDE - FRGs的表达。然后通过受试者工作特征曲线(ROC)分析评估关键LNDE - FRGs的诊断价值。此外,我们通过Nephroseq V5数据库研究LN患者中关键基因与肾小球滤过率(GFR)、尿蛋白和血清肌酐(Scr)之间的相关性。随后,我们使用Cibersort对LN肾组织进行免疫浸润细胞分析。最后,我们通过临床样本和体内实验验证关键基因CYBB的表达。

结果

共鉴定出377个DEGs和20个LNDE - FRGs。机器学习算法选择了四个候选关键LNDE - FRGs(, , ,和 )。然而,只有 在训练和验证队列中均表现出一致的表达趋势( < 0.05)。免疫浸润分析显示,LN组中单核细胞和M0巨噬细胞的表达水平显著高于正常对照组。此外,关键基因与GFR、尿蛋白和Scr之间存在相关性。最后,在狼疮小鼠中验证了 的表达水平。

结论

可能是LN中与铁死亡相关的生物标志物。这可能为LN的临床治疗和发病机制提供新的思路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2ad/12357583/5ba04871a508/JIR-18-10855-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2ad/12357583/76cf71d0f5cb/JIR-18-10855-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2ad/12357583/760b8483901e/JIR-18-10855-g0002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2ad/12357583/df34772f3b9c/JIR-18-10855-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2ad/12357583/fa3581a8aa86/JIR-18-10855-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2ad/12357583/5ba04871a508/JIR-18-10855-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2ad/12357583/76cf71d0f5cb/JIR-18-10855-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2ad/12357583/760b8483901e/JIR-18-10855-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2ad/12357583/75b2ad35bb82/JIR-18-10855-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2ad/12357583/cde03af09a1b/JIR-18-10855-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2ad/12357583/df34772f3b9c/JIR-18-10855-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2ad/12357583/fa3581a8aa86/JIR-18-10855-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2ad/12357583/5ba04871a508/JIR-18-10855-g0007.jpg

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