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通过加权基因共表达网络分析构建与人类动脉粥样硬化发生发展及预后相关的 ceRNA-免疫调节网络。

Constructing a ceRNA-immunoregulatory network associated with the development and prognosis of human atherosclerosis through weighted gene co-expression network analysis.

机构信息

Department of Cardiovascular Medicine, Second Xiangya Hospital, Central South University, Changsha, Hunan Province, China.

出版信息

Aging (Albany NY). 2021 Jan 17;13(2):3080-3100. doi: 10.18632/aging.202486.

Abstract

There is now overwhelming experimental and clinical evidence that atherosclerosis (AS) is a chronic inflammatory disease. The recent discovery of a new group of mediators known as competing endogenous RNA (ceRNA) offers a unique opportunity for investigating immunoregulation in AS. In this study, we used gene expression profiles from GEO database to construct a lncRNA-miRNA-mRNA ceRNA network during AS plaque development through weighted gene co-expression network analysis (WGCNA). GO annotation and pathway enrichment analysis suggested that the ceRNA network was mainly involved in the immune response. CIBERSORT and GSVA were used to calculate the immune cell infiltration score and identified macrophage as hub immunocyte in plaque development. A macrophage related ceRNA subnetwork was constructed through correlation analysis. Samples from Biobank of Karolinska Endarterectomy (BiKE) were used to identify prognostic factors from the subnetwork and yielded 7 hub factors that can predict ischemic events including macrophage GSVA score and expression value of AL138756.1, CTSB, MAFB, LYN, GRK3, and BID. A nomogram based on the key factors was established. GSEA identified that the PD1 signaling pathway was negatively associated with these prognostic factors which may explain the cardiovascular side effect of immune checkpoint therapy in anti-tumor treatment.

摘要

现在有压倒性的实验和临床证据表明,动脉粥样硬化(AS)是一种慢性炎症性疾病。最近发现的一组被称为竞争性内源 RNA(ceRNA)的新介质为研究 AS 中的免疫调节提供了一个独特的机会。在这项研究中,我们使用 GEO 数据库中的基因表达谱,通过加权基因共表达网络分析(WGCNA)构建 AS 斑块发展过程中的 lncRNA-miRNA-mRNA ceRNA 网络。GO 注释和通路富集分析表明,ceRNA 网络主要参与免疫反应。CIBERSORT 和 GSVA 用于计算免疫细胞浸润评分,并确定斑块发展中的巨噬细胞为枢纽免疫细胞。通过相关分析构建了一个巨噬细胞相关的 ceRNA 子网络。使用来自 Karolinska 内膜切除术生物库(BiKE)的样本,从子网络中鉴定出 7 个预后因素,这些因素可以预测包括巨噬细胞 GSVA 评分和 AL138756.1、CTSB、MAFB、LYN、GRK3 和 BID 的表达值在内的缺血事件。基于关键因素建立了一个列线图。GSEA 确定 PD1 信号通路与这些预后因素呈负相关,这可能解释了免疫检查点治疗在抗肿瘤治疗中的心血管副作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe87/7880393/0c57cbfbad19/aging-13-202486-g001.jpg

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