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通过综合分析筛选系统性红斑狼疮相关心肌梗死的潜在循环诊断生物标志物及分子机制

Screening of Potential Circulating Diagnostic Biomarkers and Molecular Mechanisms of Systemic Lupus Erythematosus-Related Myocardial Infarction by Integrative Analysis.

作者信息

Ding Haoran, Zhu Guoqi, Lin Hao, Chu Jiapeng, Yuan Deqiang, Yao Yi'an, Gao Yanhua, Chen Fei, Liu Xuebo

机构信息

Department of Cardiology, Tongji Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China.

出版信息

J Inflamm Res. 2023 Jul 24;16:3119-3134. doi: 10.2147/JIR.S404066. eCollection 2023.

Abstract

BACKGROUND

The risk of acute myocardial infarction (AMI) is elevated in patients with systemic lupus erythematosus (SLE), and it is of great clinical value to identify potential molecular mechanisms and diagnostic markers of AMI associated with SLE by analyzing public database data and transcriptome sequencing data.

METHODS

AMI and SLE-related sequencing datasets GSE62646, GSE60993, GSE50772 and GSE81622 were downloaded from the Gene Expression Omnibus (GEO) database and divided into prediction and validation cohorts. To identify the key genes associated with AMI related to SLE, WGCNA and DEGs analysis were performed for the prediction and validation cohorts, respectively. The related signaling pathways were identified by GO/KEGG enrichment analysis. Peripheral blood mononuclear cells (PBMCs) from patients with AMI were collected for transcriptome sequencing to validate the expression of key genes in patients with AMI. Least absolute shrinkage and selection operator (LASSO) regression analysis was applied to screen diagnostic biomarkers. The diagnostic efficacy of biomarkers was validated by ROC analysis, and the CIBERSORTx platform was used to analyze the composition of immune cells in AMI and SLE.

RESULTS

A total of 108 genes closely related to AMI and SLE were identified in the prediction cohort, and GO/KEGG analysis showed significantly enriched signaling pathways. The results of differential analysis in validation cohort were consistent with them. By transcriptional sequencing of PBMCs from peripheral blood of AMI patients, combined with the results of prediction and validation cohort analysis, seven genes were finally screened out. LASSO analysis finally identifies DYSF, LRG1 and CSF3R as diagnostic biomarkers of SLE-related-AMI. CIBERSORTx analysis revealed that the biomarkers were highly correlated with neutrophils.

CONCLUSION

Neutrophil degranulation and NETs formation play important roles in SLE-related AMI, and DYSF, LRG1 and CSF3R were identified as important diagnostic markers for the development and progression of SLE-related AMI.

摘要

背景

系统性红斑狼疮(SLE)患者急性心肌梗死(AMI)风险升高,通过分析公共数据库数据和转录组测序数据来识别与SLE相关的AMI潜在分子机制和诊断标志物具有重要临床价值。

方法

从基因表达综合数据库(GEO)下载AMI和SLE相关测序数据集GSE62646、GSE60993、GSE50772和GSE81622,并分为预测和验证队列。为识别与SLE相关的AMI关键基因,分别对预测和验证队列进行加权基因共表达网络分析(WGCNA)和差异表达基因(DEGs)分析。通过基因本体论(GO)/京都基因与基因组百科全书(KEGG)富集分析确定相关信号通路。收集AMI患者外周血单个核细胞(PBMCs)进行转录组测序,以验证AMI患者关键基因的表达。应用最小绝对收缩和选择算子(LASSO)回归分析筛选诊断生物标志物。通过受试者工作特征(ROC)分析验证生物标志物的诊断效能,并使用CIBERSORTx平台分析AMI和SLE中免疫细胞的组成。

结果

在预测队列中总共鉴定出108个与AMI和SLE密切相关的基因,GO/KEGG分析显示信号通路显著富集。验证队列中的差异分析结果与之相符。通过对AMI患者外周血PBMCs进行转录测序,结合预测和验证队列分析结果,最终筛选出7个基因。LASSO分析最终确定肌营养不良蛋白聚糖(DYSF)、富含亮氨酸α-2糖蛋白1(LRG1)和集落刺激因子3受体(CSF3R)为SLE相关AMI的诊断生物标志物。CIBERSORTx分析显示这些生物标志物与中性粒细胞高度相关。

结论

中性粒细胞脱颗粒和中性粒细胞胞外陷阱(NETs)形成在SLE相关AMI中起重要作用,DYSF、LRG1和CSF3R被确定为SLE相关AMI发生和发展的重要诊断标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c553/10378693/155fe23e6dee/JIR-16-3119-g0001.jpg

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