冠状动脉粥样硬化性心脏病患者心外膜脂肪组织的生物信息学研究

doi:10.12114/j.issn.1007-9572.2022.0487

摘要/Abstract

摘要： 背景心血管疾病（CVD）是常见病和多发病，患病率和死亡率呈快速上升趋势。动脉粥样硬化（AS）是缺血性CVD的病理基础，研究表明心外膜脂肪组织（EAT）通过分泌外泌体（EXO）和生物活性物质促进AS进展，但其作用机制仍需进一步研究。目的通过生物信息学方法挖掘冠状动脉粥样硬化性心脏病（CAD）患者EAT中的关键基因，探讨免疫细胞浸润情况，联合CAD患者EXO间差异表达基因（DEGs）推测EAT来源EXO间DEGs并进行验证，从细胞及分子水平上探讨EAT在CAD疾病过程中的作用机制。方法从基因表达综合数据库（GEO）中下载关于EAT的数据集GSE64554、GSE120774，根据临床信息将EAT的测序数据分为CAD组和健康对照组，使用R语言及相关软件包进行生物信息学分析。首先使用R语言筛选CAD组与健康对照组EAT间DEGs，并进行GO富集分析和KEGG通路富集分析，构建蛋白质-蛋白质相互作用（PPI）网络，评估所选基因的生物学功能及可能参与其调控的转录因子。构建GSE64554数据集中EAT的加权基因共表达网络（WGCNA），获取同CAD表型相关的基因模块，将所获EAT间DEGs与模块内hub基因取交集获得关键基因，采用Cibersort反卷积算法对EAT组织的免疫细胞浸润情况进行分析。通过exoRbase数据库获取CAD组与健康对照组血液EXO间DEGs，CAD组和健康对照组EAT间DEGs与EXO间DEGs取交集作为CAD的诊断、治疗标志物，收集临床样本通过实时荧光定量PCR（qRT-PCR）对其进行验证。对所选基因进行GO/KEGG富集分析和Metascape富集分析。结果共筛选出CAD组与健康对照组EAT间DEGs 1 511个，其中表达上调的基因956个，表达下调的基因555个。通过对CAD表型相关的模块内枢纽基因与EAT间DEGs取交集获得EAT在CAD发生、发展中的关键基因DDX47、FEM1C、NOL11、SRP54、ABI1、PATL1、BNIP2、C1orf159、CHCHD4。免疫细胞浸润分析显示，CAD组EAT中幼稚CD4+ T细胞表达丰度升高而静息树突细胞表达丰度减低（P<0.05）。筛选获得CAD EXO间DEGs 1 658个，其中表达上调的基因278个，表达下调的基因1 380个，EAT间DEGs与EXO间DEGs取交集，共获得129个基因，选取表达丰度较高的BPI、BIRC5、CXCL12、RNASE1、F2R作为CAD患者潜在诊断、治疗标志物，经qRT-PCR验证结果显示，CAD组与健康对照组比较BPI、BIRC5、CXCL12和RNASE1的mRNA水平升高（P<0.05），F2RmRNA水平下降（P<0.05）。GO/KEGG富集分析显示EAT间DEGs主要富集于细胞质基质、MHC蛋白复合物、RNA降解、抗原加工和呈递等，构建PPI网络，通过Cytoscape软件CytoHubba插件MCC算法获得连接度最高的基因RPS27A。Metascape富集分析显示DEGs主要富集于细胞对DNA损伤刺激反应、RNA代谢、调节细胞对压力的反应、适应性免疫系统等，TRRUST数据库预测CIITA转录因子可能参与了EAT间DEGs的调控。结论（1）EAT可能通过促炎和免疫途径参与CAD的发生和发展，DDX47、FEM1C、NOL11、SRP54、ABI1、PATL1、BNIP2、C1orf159、CHCHD4、RPS27A可能作为关键基因并发挥重要作用。（2）CAD患者EAT中幼稚CD4+ T细胞表达丰度升高而静息树突细胞表达丰度减低。（3）BPI、BIRC5、CXCL12、RNASE1、F2R可能由EAT分泌并可作为CAD的诊断、治疗标志物。

关键词: 冠心病, 心外膜脂肪组织, 外泌体, 生物信息学分析, 免疫浸润, 关键基因, Hub基因

Abstract:

Background

Cardiovascular disease (CVD) is a common and frequently occurring disease, and the prevalence and mortality of which are increasing rapidly. Atherosclerosis (AS) is the pathological basis of ischemic CVD. Studies have shown that epicardial adipose tissue (EAT) promotes the progression of AS by secreting exosomes and bioactive substances, but the mechanism of action still needs to be further studied.

Objective

To perform a bioinformatics analysis of role of EAT in coronary artery disease (CAD) at cellular and molecular levels by identifying the differentially expressed genes (DEGs) in EAT to explore the status of immune cell infiltration, and to assess and verify whether EAT is derived from DEGs in exosomes in CAD patients.

Methods

We downloaded GSE64554 and GSE120774 datasets about EAT from the GEO database and performed a bioinformatics analysis using R language and related packages. We first used R language to screen the DEGs in EAT and CAD patients, then used GO/KEGG enrichment analysis to establish a protein interaction network to explore biological functions of the screened genes and transcription factors potentially involved in their regulation process. After that, we conducted a weighted gene co-expression network analysis (WGCNA) of EAT in GSE64554 dataset to obtain a gene module related to CAD phenotype, then crossed the hub genes in this module and DEGs in EAT to obtain the key common genes. We used Cibersort to characterize the immune cell infiltration in EAT. Then we obtained DEGs from blood exosomes of CAD patients and healthy controls included in the exoRbase database, crossed DEGs in EAT and blood exosomes to identify the common genes to be used as diagnostic and therapeutic markers for CAD, and their values were tested by qRT-PCR measurement of clinical samples. The selected genes were analyzed by GO/KEGG and Metascape enrichment analyses.

Results

A total of 1 511 DEGs in EAT of CAD patients were identified, including 956 with up-regulated expression and 555 with down-regulated expression. By crossing the DEGs in EAT and hub genes in modules associated with CAD closely, we identified DDX47, FEM1C, NOL11, SRP54, ABI1, PATL1, BNIP2, C1orf159, and CHCHD4 as key genes in the development of CAD. Immune cell infiltration analysis showed that the abundance of immature CD₄⁺ T cells increased while expression abundance of resting dendritic cells decreased in EAT of CAD patients (P<0.05). A total of 1 658 DEGs in exosomes of CAD patients, including 278 with up-regulated expression and 1 380 with down-regulated expression. One hundred and twenty-nine common DEGs were obtained by cross-tabbing DEGs in EAT and exosomes of CAD patients, among which BPI, BIRC5, CXCL12, RNASE1 and F2R with higher expression abundance were selected as potential diagnostic and therapeutic markers for CAD. By qRT-PCR detection, CAD patients were found with increased mRNA expression levels of BPI, BIRC5, CXCL12, RNASE1 (P>0.05), and decreased F2RmRNA expression level (P<0.05) than controls. GO/KEGG enrichment analysis showed that DEGs in EAT were mainly involved in the cytosol, MHC protein complex, RNA degradation, antigen processing and presentation. A PPI network was built, in which RPS27A gene was identified as a gene with the highest degree of connectivity by use of Cytoscape plugin CytoHubba with MCC algorithm. Metascape enrich analysis indicated that DEGs enriched mainly in cellular response to DNA damage, RNA metabolism, regulation of cell stress responses, and adaptive immune system. By an analysis of TRRUST datasets, we predicted that transcription factor CIITA may play a role in the regulation of DEGs in EAT influencing CAD.

Conclusion

EAT may be involved in the development of CAD through proinflammatory and immune pathways, in which DDX47, FEM1C, NOL11, SRP54, ABI1, PATL1, BNIP2, C1orf159, CHCHD4 and RPS27A may play a vital role as the key genes. The abundance of naive CD₄⁺ T cells significantly increased while that of resting dendritic cells decreased obviously in EAT from CAD patients. BPI, BIRC5, CXCL12, RNASE1 and F2R may be excreted by EAT and have the potential as markers in CAD diagnosis and treatment.

Key words: Coronary artery disease, Epicardial adipose tissue, Exosome, Bioinformatics, Immune cell infiltration, Key genes, Hub genes

柴晏,赵玉青,郭旭男,等. 冠状动脉粥样硬化性心脏病患者心外膜脂肪组织的生物信息学研究[J]. 中国全科医学, 2023, 26(08): 939-950. DOI: 10.12114/j.issn.1007-9572.2022.0487.
CHAI Yan,ZHAO Yuqing,GUO Xunan, et al. Bioinformatics Analysis of the Role of Epicardial Adipose Tissue in Coronary Artery Disease[J]. Chinese General Practice, 2023, 26(08): 939-950. DOI: 10.12114/j.issn.1007-9572.2022.0487.

图/表 18

表1 PCR引物

Table 1 Primer sequences for qRT-PCR

引物名称	引物序列	产物长度（bp）	退火温度（℃）
H-BPI-S	GAATCAACTATGGTCTGGTGGCA	106	60.62
H-BPI-A	AAGGGAGGTGGATTGTGGTG
H-BIRC5-S	CGCATCTCTACATTCAAGAACTGG	182	59.73
H-BIRC5-A	GGTTTCCTTTGCATGGGGT
H-CXCL12-S	TGAGCTACAGATGCCCATGC	137	60.18
H-CXCL12-A	GCACACTTGTCTGTTGTTGTTCTTC
H-RNASE1-S	GCTCCTTCTGCTTGTCCTGAT	149	60.07
H-RNASE1-A	TCATCATTTGGTTACAGTAGGTGGA
H-F2R-S	CAGTGATTGGCAGTTTGGGTCT	270	61.08
H-F2R-A	TGACAGGTAGTGATGTTGAGCCC
H-ACTIN-S	CACCCAGCACAATGAAGATCAAGT	317	61.60
H-ACTIN-A	CCAGTTTTTAAATCCTGAGTCAAGC

图1 CAD组与健康对照组EAT间DEGs火山图

Figure 1 Volcano plot of differentially expressed genes in epicardial adipose tissue between CAD group and healthy control group

图2 CAD组与健康对照组EAT间DEGs热图注：CAD=冠状动脉粥样硬化性心脏病

Figure 2 Heatmap of differentially expressed genes in epicardial adipose tissue between CAD group and healthy control group

图3 EAT间DEGs的GO富集分析注：BP=生物过程，CC=细胞组分，MF=分子功能

Figure 3 GO enrichment analysis of differentially expressed genes in epicardial adipose tissue

图4 EAT间DEGs的KEGG富集分析

Figure 4 KEGG enrichment analysis of differentially expressed genes in epicardial adipose tissue

图5 EAT间DEGs的PPI网络与关键基因注：A为EAT间DEGs的PPI网络图，B为EAT间DEGs的关键基因图

Figure 5 Differentially expressed genes in epicardial adipose tissue and the key genes identified in the PPI network

图6 EAT间DEGs的Metascape富集分析

Figure 6 Metascape enrichment analysis of differentially expressed genes in epicardial adipose tissue

图7 EAT间DEGs的TRRUST数据库预测转录因子

Figure 7 TRRUST database-based analysis of differentially expressed genes in epicardial adipose tissue to predict transcription factors

图8 软阈值参数网络拓扑结构的分析注：A为不同β下计算的无尺度符合指数，B为不同β下计算的平均连接度

Figure 8 Network topology for soft thresholding powers

图9 基因聚类树状图

Figure 9 Dendrogram of gene clustering

图10 基因模块与临床信息关联性

Figure 10 Correlation between gene modules and clinical information

图11 差异性表达基因与模块内hub基因Venn图注：A为上调基因，B为下调基因

Figure 11 Venn diagram of differentially expressed genes and hub genes in the module

图12 GSE64554数据集EAT组织免疫细胞浸润柱状图

Figure 12 Bar graph of immune cell infiltration in epicardial adipose tissue between CAD patients and controls included in dataset GSE64554

图13 GSE64554数据集EAT组织免疫细胞浸润小提琴图注：红色为CAD组，蓝色为健康对照组

Figure 13 Violin plot of immune cell infiltration in epicardial adipose tissue between CAD patients and controls included in dataset GSE64554

图14 CAD组与健康对照组EXO间火山图

Figure 14 Volcano plot of differentially expressed genes in exosomes between CAD patients and heathy controls

图15 CAD组与健康对照组EXO间DEGs热图

Figure 15 Heatmap of differentially expressed genes in exosomes between CAD patients and heathy controls

图16 CAD患者EAT组织、EXO间DEGs Venn图

Figure 16 Venn diagram of differentially expressed genes in epicardial adipose tissue and exosomes from CAD patients

表2 CAD组与健康对照组EXO差异性基因表达（±s）

Table 2 Differentially expressed genes in exosomes between CAD patients and healthy controls

组别	样本数	BPI	BIRC5	CXCL12	RNASE1	F2R
CAD组	10	2.14±0.32	1.98±0.23	2.91±0.89	4.47±1.56	0.45±0.06
健康对照组	10	0.99±0.17	1.02±0.11	1.14±0.44	1.28±0.56	1.00±0.12
t值		-9.945	-12.070	-5.630	-6.001	12.637
P值		0.011	0.010	<0.001	0.009	<0.001

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