Guo Chunguang, Liu Zaoqu, Cao Can, Zheng Youyang, Lu Taoyuan, Yu Yin, Wang Libo, Liu Long, Liu Shirui, Hua Zhaohui, Han Xinwei, Li Zhen
Department of Endovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Front Cell Dev Biol. 2022 Mar 17;10:794608. doi: 10.3389/fcell.2022.794608. eCollection 2022.
Ischemic events after carotid endarterectomy (CEA) in carotid artery stenosis patients are unforeseeable and alarming. Therefore, we aimed to establish a novel model to prevent recurrent ischemic events after CEA. Ninety-eight peripheral blood mononuclear cell samples were collected from carotid artery stenosis patients. Based on weighted gene co-expression network analysis, we performed whole transcriptome correlation analysis and extracted the key module related to ischemic events. The biological functions of the 292 genes in the key module were annotated via GO and KEGG enrichment analysis, and the protein-protein interaction (PPI) network was constructed via the STRING database and Cytoscape software. The enrolled samples were divided into train ( = 66), validation ( = 28), and total sets ( = 94). In the train set, the random forest algorithm was used to identify critical genes for predicting ischemic events after CEA, and further dimension reduction was performed by LASSO logistic regression. A diagnosis model was established in the train set and verified in the validation and total sets. Furthermore, fifty peripheral venous blood samples from patients with carotid stenosis in our hospital were used as an independent cohort to validation the model by RT-qPCR. Meanwhile, GSEA, ssGSEA, CIBERSORT, and MCP-counter were used to enrichment analysis in high- and low-risk groups, which were divided by the median risk score. We established an eight-gene model consisting of , , , , , , , and . The ROC-AUCs and PR-AUCs of the train, validation, total, and independent cohort were 0.891 and 0.725, 0.826 and 0.364, 0.869 and 0.654, 0.792 and 0.372, respectively. GSEA, ssGSEA, CIBERSORT, and MCP-counter analyses further revealed that high-risk patients presented enhanced immune signatures, which indicated that immunotherapy may improve clinical outcomes in these patients. An eight-gene model with high accuracy for predicting ischemic events after CEA was constructed. This model might be a promising tool to facilitate the clinical management and postoperative surveillance of carotid artery stenosis patients.
颈动脉内膜切除术（CEA）后，颈动脉狭窄患者发生缺血性事件是不可预见且令人担忧的。因此，我们旨在建立一种新型模型，以预防CEA后复发性缺血性事件。从颈动脉狭窄患者中收集了98份外周血单核细胞样本。基于加权基因共表达网络分析，我们进行了全转录组相关性分析，并提取了与缺血性事件相关的关键模块。通过GO和KEGG富集分析对关键模块中292个基因的生物学功能进行注释，并通过STRING数据库和Cytoscape软件构建蛋白质-蛋白质相互作用（PPI）网络。将纳入的样本分为训练集（n = 66）、验证集（n = 28）和总集（n = 94）。在训练集中，使用随机森林算法识别预测CEA后缺血性事件的关键基因，并通过LASSO逻辑回归进行进一步降维。在训练集中建立诊断模型，并在验证集和总集中进行验证。此外，使用我院50例颈动脉狭窄患者的外周静脉血样本作为独立队列，通过RT-qPCR验证该模型。同时，使用GSEA、ssGSEA、CIBERSORT和MCP-counter对根据中位风险评分划分的高风险和低风险组进行富集分析。我们建立了一个由[此处原文缺失8个基因名称]组成的八基因模型。训练集、验证集、总集和独立队列的ROC-AUC和PR-AUC分别为0.891和0.725、0.826和0.364、0.869和0.654、0.792和0.372。GSEA、ssGSEA、CIBERSORT和MCP-counter分析进一步显示，高风险患者呈现增强的免疫特征，这表明免疫疗法可能改善这些患者的临床结局。构建了一个对预测CEA后缺血性事件具有高准确性的八基因模型。该模型可能是促进颈动脉狭窄患者临床管理和术后监测的有前景的工具。
