Table4_Construction of an immune-related signature for predicting the ischemic events in patients undergoing carotid endarterectomy.XLSX

Background: Inflammatory responses have drawn more attention to atherosclerosis; however, the immune-related genes (IRGs) as a prognostic factor in atherosclerotic plaque remain to be fully elucidated. Here, the purpose of this study was to investigate whether the IRGs could be identified as a reliable biomarker for predicting ischemic events in patients undergoing carotid endarterectomy (CEA).
Methods: Two datasets GSE97210 and GSE21545 were downloaded from the Gene Expression Omnibus (GEO) database. The dataset GSE97210 was used to explore the significant pathways and differentially expressed IRGs (DEIRGs) between plaques and controls, which were further screened to identify the prognostic DEIRGs in the GSE21545 dataset. The identification of molecular subgroups with the prognostic gene expression patterns was achieved through nonnegative matrix factorization (NMF) clustering. Functional analyses including GO, KEGG, GSVA, and GSEA analyses, and immune analyses including xCell and ssGSEA algorithms were conducted to elucidate the underlying mechanisms. The prognostic risk model was constructed using the LASSO algorithm and multivariate Cox regression analysis.
Results: A total of 796 DEIRGs (including 588 upregulated and 208 downregulated) were identified. Nine prognostic DEIRGs were further screened with univariate Cox regression analysis. Two clusters with different prognosis were grouped based on the prognostic DEIRGs. Immune infiltration analysis shows that cluster 2 with a better prognosis presented with a higher immune response than cluster 1. A prognostic model based on seven IRGs (IL2RA, NR4A2, DES, ERAP2, SLPI, RASGRP1, and AGTR2) was developed and verified. Consistent with the immune analysis of the cluster, the immune infiltration in the low-risk group with a better prognosis was also more active than that in the high-risk group. Finally, a nomogram based on the seven genes was constructed, which might have future implications in clinical care.
Conclusion: The expression of immune-related genes is correlated with the immune microenvironment of atherosclerotic patients and could be applied to predict the ischemic events in patients undergoing CEA accurately.

背景：炎症反应在动脉粥样硬化研究中受到越来越多的关注，但免疫相关基因（immune-related genes, IRGs）作为动脉粥样硬化斑块的预后因子仍有待充分阐明。本研究旨在探讨免疫相关基因能否作为可靠的生物标志物，用于预测接受颈动脉内膜切除术（carotid endarterectomy, CEA）患者的缺血事件。
方法：从基因表达综合数据库（Gene Expression Omnibus, GEO）中下载GSE97210与GSE21545两个数据集。以GSE97210数据集探究斑块组与对照组间的显著通路及差异表达免疫相关基因（differentially expressed immune-related genes, DEIRGs），并通过GSE21545数据集进一步筛选预后相关差异表达免疫相关基因。通过非负矩阵分解（nonnegative matrix factorization, NMF）聚类识别具有预后基因表达模式的分子亚组。开展包括基因本体（Gene Ontology, GO）分析、京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）分析、基因集变异分析（gene set variation analysis, GSVA）及基因集富集分析（gene set enrichment analysis, GSEA）在内的功能富集分析，并采用xCell与单样本基因集富集分析（single-sample gene set enrichment analysis, ssGSEA）算法进行免疫浸润分析，以阐明潜在的作用机制。通过最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）回归与多因素Cox回归分析构建预后风险模型。
结果：共鉴定出796个差异表达免疫相关基因，其中上调基因588个，下调基因208个。经单因素Cox回归分析进一步筛选出9个预后相关差异表达免疫相关基因。基于上述预后基因将样本分为两个预后特征不同的聚类亚组。免疫浸润分析显示，预后较好的聚类2的免疫应答水平高于聚类1。构建并验证了基于7个免疫相关基因（IL2RA、NR4A2、DES、ERAP2、SLPI、RASGRP1及AGTR2）的预后模型。与聚类的免疫分析结果一致，预后较好的低风险组免疫浸润水平亦高于高风险组。最后构建了基于这7个基因的列线图，有望为临床诊疗提供参考依据。
结论：免疫相关基因的表达与动脉粥样硬化患者的免疫微环境密切相关，可用于精准预测接受颈动脉内膜切除术患者的缺血事件。