通过综合生物信息学分析和多种机器学习算法鉴定与系统性红斑狼疮和扩张型心肌病相关的免疫相关生物标志物。

Identification of immune-related biomarkers linked to systemic lupus erythematosus and dilated cardiomyopathy through integrated bioinformatics analysis and multiple machine learning algorithms.

作者信息

Li Gaijie, Lin Liwen, Wang Shushu, Lu Kachun, Szeto KaMan, Zhou Guiting, Tang Xianwen, Luo Chuanjin

机构信息

The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China.

Department of Cardiology, Shenzhen Hospital of Beijing University of Chinese Medicine (Longgang), Shenzhen, China.

出版信息

Front Immunol. 2025 Jul 30;16:1606920. doi: 10.3389/fimmu.2025.1606920. eCollection 2025.

DOI:10.3389/fimmu.2025.1606920

PMID:40808946

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12343684/

Abstract

BACKGROUND

Epidemiological evidence indicates that up to 50% of systemic lupus erythematosus (SLE) patients exhibit cardiac involvement, suggesting a potential strong association between SLE and dilated cardiomyopathy (DCM). This study aims to identify SLE-related genes that may contribute to DCM development and to discover potential biomarkers for early DCM diagnosis in SLE patients.

METHODS

We obtained expression profile datasets for dilated cardiomyopathy DCM and SLE from the Gene Expression Omnibus (GEO) database. Through differential expression analysis and weighted gene co-expression network analysis (WGCNA), we screened for candidate biomarkers shared between DCM and SLE and constructed a diagnostic nomogram. The diagnostic performance and effectiveness of the nomogram were evaluated using external datasets and qPCR. Additionally, we performed single-gene set enrichment analysis (GSEA) on key genes to elucidate their potential roles in SLE-related DCM. Finally, we applied the CIBERSORT algorithm to assess immune cell infiltration in both DCM and SLE patients.

RESULTS

Through DEG and WGCNA in the DCM and SLE datasets, we identified a total of 141 key module genes and 24 commonly expressed differentially expressed genes. Enrichment analysis revealed that these 24 genes were primarily involved in inflammation, cell apoptosis, and immune regulation. Through machine learning algorithms and dataset validation, we further identified the HERC6 and IFI44L genes as important diagnostic markers for SLE-related DCM. Experimental validation supports the key role of HERC6, IFI44L, and RSAD2 in SLE-related cardiac dysfunction. Additionally, we developed a nomogram for DCM based on these two genes, and the results showed that both genes exhibited AUC values greater than 0.84. Simultaneously, single-GSEA and immune infiltration analysis indicated immune dysfunction in both DCM and SLE, with both HERC6 and IFI44L significantly associated with immune cell infiltration. Furthermore, connectivity map (cMAP) analysis identified α-linolenic acid as a potential therapeutic agent for treating DCM.

CONCLUSION

Our study identifies HERC6 and IFI44L as diagnostic markers for DCM in SLE and suggests α-linolenic acid as a potential therapeutic agent.

摘要

背景

流行病学证据表明，高达50%的系统性红斑狼疮（SLE）患者存在心脏受累情况，这表明SLE与扩张型心肌病（DCM）之间可能存在密切关联。本研究旨在确定可能导致DCM发生的SLE相关基因，并发现SLE患者早期DCM诊断的潜在生物标志物。

方法

我们从基因表达综合数据库（GEO）中获取了扩张型心肌病DCM和SLE的表达谱数据集。通过差异表达分析和加权基因共表达网络分析（WGCNA），我们筛选出DCM和SLE共有的候选生物标志物，并构建了诊断列线图。使用外部数据集和qPCR评估列线图的诊断性能和有效性。此外，我们对关键基因进行单基因集富集分析（GSEA），以阐明它们在SLE相关DCM中的潜在作用。最后，我们应用CIBERSORT算法评估DCM和SLE患者的免疫细胞浸润情况。

结果

通过对DCM和SLE数据集进行差异表达基因（DEG）和WGCNA分析，我们共鉴定出141个关键模块基因和24个共同表达的差异表达基因。富集分析表明，这24个基因主要参与炎症、细胞凋亡和免疫调节。通过机器学习算法和数据集验证，我们进一步确定HERC6和IFI44L基因是SLE相关DCM的重要诊断标志物。实验验证支持HERC6、IFI44L和RSAD2在SLE相关心脏功能障碍中的关键作用。此外，我们基于这两个基因开发了DCM的列线图，结果表明这两个基因的AUC值均大于0.84。同时，单GSEA和免疫浸润分析表明DCM和SLE均存在免疫功能障碍，HERC6和IFI44L均与免疫细胞浸润显著相关。此外，连接图（cMAP）分析确定α-亚麻酸是治疗DCM的潜在治疗药物。