Analysis of differential gene enrichment and weighted gene co-expression network in coronary artery disease
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摘要:目的 采用基因集富集分析和加权基因共表达网络分析探讨冠状动脉疾病的差异表达基因，挖掘与冠状动脉疾病发病相关的关键基因。方法 从美国国家生物技术信息中心(NCBI)基因表达综合数据库下载GSE71226数据集，筛选冠状动脉疾病差异基因并进行富集分析; 对差异基因进行基因集富集分析，筛选冠状动脉疾病差异表达基因相关的微小RNA(miRNAs)并构建调控网络; 根据基因的相关性，构建基因共表达模块，并计算模块基因与临床信息的相关性; 选取与临床表型显著相关的模块，构建蛋白质互作网络并筛选核心基因。结果 经筛选，共获取冠状动脉疾病差异表达基因130个，富集于Hippo、流体剪切力与动脉粥样硬化、一磷酸腺苷依赖的蛋白激酶(AMPK)、Wnt、核苷酸结合寡聚化结构域(NOD)样受体和白细胞介素17等信号通路上。富集分析显示, miR-503-3p、miR-3674、miR-5088-5p、miR-4486等miRNAs与冠状动脉疾病关系密切。根据基因表达的相关性，获取10个共表达模块，其中洋红色模块与冠状动脉疾病发病显著相关。经拓扑分析, BMP4和C3在网络中处于核心地位。结论 冠状动脉疾病发病过程中基因表达存在显著差异，其病理过程涉及多靶点、多通路，可能受到多个miRNAs调控。BMP4和C3可能是冠状动脉疾病发病的关键基因。Abstract:Objective To explore the differential expression genes of coronary artery disease by gene set enrichment analysis and weighted gene co-expression network analysis and excavate key genes associated with the development of coronary artery disease.Methods The GSE71226 dataset was downloaded from the Gene Expression Omnibus database of the National Center for Biotechnology Information (NCBI), and the differential genes of coronary artery disease were screened and the enrichment analysis was performed. The gene set enrichment analysis was performed on the differential genes to screen for microRNAs (miRNAs) associated with differential expression genes in coronary artery disease, and the regulation network was constructed. The gene co-expression modules were constructed based on the correlation of genes, and the correlation between gene modules and clinical information was calculated. The genes of modules significantly correlating with clinical phenotypes were selected to construct protein-protein interaction network for screening of the core genes.Results After screening, a total of 130 differential expression genes in coronary artery disease were obtained, enriched in signaling pathways such as Hippo, fluid shear stress and atherosclerosis, adenosine monophosphate-dependent protein kinase (AMPK), Wnt, nucleotide-binding oligomerization domain (NOD) like receptors and interleukin 17. Enrichment analysis showed that the miRNAs such as miR-503-3p, miR-3674, miR -5088-5p and miR-4486 were closely related to coronary artery disease. Based on the correlation of gene expression, 10 co-expression modules were obtained, and the magenta module was significantly associated with the development of coronary artery disease. Topological analysis showed that BMP4 and C3 were the core genes of the protein-protein interaction network.Conclusion There are significant differences in gene expressions during the pathogenesis of coronary artery disease, the pathological process involves multiple targets and pathways, and pathogenesis involving in multiple targets and pathways may be regulated by multiple miRNAs. BMP4 and C3 may be the core genes in the pathogenesis of coronary artery disease.
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图 1 经处理后的差异基因表达图
A: 数据标准化后箱线图; B: 未进行批次去除前的PCA结果图, 2个数据集各自分开，无任何交集; C: 去除批次后的PCA结果图, 2个数据集交集在一起，可作为一批数据进行后续分析; D: 使用Fold change和校正后P值绘制火山图，图中红点表示显著差异上调的基因，蓝点表示显著差异下调的基因; E: 差异基因表达热图，不同颜色代表在不同样本中的表达趋势，红色代表上调，蓝色代表下调。
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图 8 洋红色模块相关基因PPI网络及核心靶点图
A: 洋红色模块基因PPI网络图; B: 经cytoHubba拓扑分析，以网络节点度值为参考，筛选出前10位的靶点，颜色越深代表处于网络中的核心地位，其中BMP4和C3为网络中的核心靶点; C: 验证数据集中C3和BMP4的表达情况，与健康对照者(CON)相比, *P <0.05, **P <0.01。
表 1 CAD的DEGs
表达水平 基因 上调 CCNE1、LEFTY1、WNT10A、NCR2、SELENBP1、CFC1、IL1R2、SEMA4A、HBG1、INSRR、PNPLA2、TNMD、RNF182、DCAF12、METTL7B、LGI4、GNG13、S100A11、GYPC、PDZK1IP1、CACNG2、ACTG1、RAX2、SLC9A3、SCG5、R3HDM4、KATNB1、SIRPB1、FCGR3B、MUC2、DMTN、CLDN7、UBC、ACADS、RPL39、ZNF428、DRP2、CIB3、EPN1、CKB、RS1、TAF6、ADIPOR1、STRADB、GFRA3、ACTB、TYROBP、IFITM2、EPB42、KRT23、COG7、RPS12、UBA52、MKRN1、VNN2、SLC4A1、RILP、OR1E1、LATS2、S100A13、C5AR1、IGF1R 下调 ANAPC2、ENTPD4、PLCB3、TCTN3、HTR3A、CCDC183、ZNF646、NUDC、PCGF6、LIN28A、PARP10、RPL37A、INF2、EEF1D、NDUFB7、RPL41、RPLP2、NHSL1、MTHFS、PWWP2B、CAMK2A、ADM2、GLUD2、YIF1B、ATXN7L2、PLB1、CCDC43、THPO、GET4、EFNA4、DROSHA、LCN1、PLEC、SEC14L5、XAB2、FAM78A、PFDN2、TXN2、ETS2、DYRK2、STAB 1、GSPT2、PAM、CES2、BAZ1B、VANGL1、IRF7、RASGRP1、DCXR、S100A8、EML3、AVP、HIST1H2AC、TTC22、DYRK1B、TMSB4Y、TFPT、RBM4B、SLC1A5、RNF19A、QDPR、ISYNA1、BTBD3、MAPK9、LIM2、ARPC1B、DLGAP1、RPLP1 
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