Six-Gene Signature Associated with Immune Cells in the Progression of Atherosclerosis Discovered by Comprehensive Bioinformatics Analyses
Six-Gene Signature Associated with Immune Cells in the Progression of Atherosclerosis Discovered by Comprehensive Bioinformatics Analyses
复制标题
综合生物信息学分析发现与动脉粥样硬化进展中免疫细胞相关的六基因特征
DOI：
10.1155/2020/1230513
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发表时间：
2020-08-01
影响因子：
3.1
通讯作者：
Zhao, Yilin
中科院分区：
文献类型：
--
作者：
Zhao, Bin;Wang, Dan;Zhao, Yilin
动脉粥样硬化是一种多面性疾病，其特征通常是斑块在动脉内壁的形成和积聚，可导致一些心血管疾病和血管栓塞。许多研究报道了动脉粥样硬化的发病机制。然而，很少有研究同时关注基因和免疫细胞，并且通过综合生物信息学分析来评估基因和免疫细胞的相关性。方法分析基因表达Omnibus （GEO）数据库中16例人类晚期动脉粥样硬化斑块（AA）和13例人类早期动脉粥样硬化斑块（EA）的29例动脉粥样硬化相关基因表达谱，获得差异表达基因（DEGs）并构建蛋白和蛋白相互作用（PPI）网络。此外，我们使用“通过估计RNA转录物的相对子集进行细胞类型鉴定（CIBERSORT）”的反卷积算法检测了22种免疫细胞类型在动脉粥样硬化中的相对比例。最终，基于显著改变的免疫细胞类型，我们进行了DEGs与免疫细胞之间的相关性分析，以发现与免疫细胞相关的潜在基因和途径。结果共鉴定出17个模块基因和6种显著改变的免疫细胞。相关性分析显示，EA中T细胞CD8的相对百分比与C1QB的表达呈负相关（R = - 0.63, p = 0.02），巨噬细胞M2的相对百分比与CD86的表达呈正相关（R = 0.57, p = 0.041），同时，4个基因表达（CD53、C1QC、NCF2、ITGAM）与AA样品中T细胞CD8和巨噬细胞（M0、M2）的百分比呈高度相关。本研究提示动脉粥样硬化的进展可能与CD86、C1QB、CD53、C1QC、NCF2、ITGAM有关，并对T细胞CD8、巨噬细胞M0、M2等免疫能力细胞起调节作用。这些结果将有助于研究与动脉粥样硬化进展中免疫细胞相关的潜在基因，并为发现新的治疗方法和药物提供见解。
Background As a multifaceted disease, atherosclerosis is often characterized by the formation and accumulation of plaque anchored to the inner wall of the arteries and causes some cardiovascular diseases and vascular embolism. Numerous studies have reported on the pathogenesis of atherosclerosis. However, fewer studies focused on both genes and immune cells, and the correlation of genes and immune cells was evaluated via comprehensive bioinformatics analyses. Methods 29 samples of atherosclerosis-related gene expression profiling, including 16 human advanced atherosclerosis plaque (AA) and 13 human early atherosclerosis plaque (EA) samples from the Gene Expression Omnibus (GEO) database, were analyzed to get differentially expressed genes (DEGs) and the construction of protein and protein interaction (PPI) networks. Besides, we detected the relative fraction of 22 immune cell types in atherosclerosis by using the deconvolution algorithm of “cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT).” Ultimately, based on the significantly changed types of immune cells, we executed the correlation analysis between DEGs and immune cells to discover the potential genes and pathways associated with immune cells. Results We identified 17 module genes and 6 types of significantly changed immune cells. Correlation analysis showed that the relative percentage of T cell CD8 has negative correlation with the C1QB expression (R = −0.63, p = 0.02), and the relative percentage of macrophage M2 has positive correlation with the CD86 expression (R = 0.57, p = 0.041) in EA. Meanwhile, four gene expressions (CD53, C1QC, NCF2, and ITGAM) have a high correlation with the percentages of T cell CD8 and macrophages (M0 and M2) in AA samples. Conclusions In this study, we suggested that the progression of atherosclerosis might be related to CD86, C1QB, CD53, C1QC, NCF2, and ITGAM and that it plays a role in regulating immune-competent cells such as T cell CD8 and macrophages M0 and M2. These results will enable studies of the potential genes associated with immune cells in the progression of atherosclerosis, as well as provide insight for discovering new treatments and drugs.