摘要
目的 利用加权基因共表达网络分析(WGCNA)联合机器学习方法筛选扩张型心肌病(DCM)生物标记,并进行免疫浸润分析.方法 对来自基因表达综合数据库(GEO)的DCM转录谱进行差异分析.对差异表达基因(DEGs)进行基因本体论(GO)富集分析和京都基因与基因组百科全书(KEGG)通路富集分析.整合WGCNA和套索回归(LASSO)分析,筛选出DCM的生物标记.绘制受试者工作特征曲线(ROC)评估其诊断效能,并进行免疫浸润分析.结果 DCM左心室组织中有268个DEGs,主要参与炎症反应的调节、上皮细胞增殖、含胶原蛋白的细胞外基质、细胞外基质结构组成成分等;同时,DEGs参与补体和凝血瀑布、吞噬体、酪氨酸激酶-信号传导子及转录激活子(JAK-STAT)信号通路、花生四烯酸代谢、白介素17(IL-17)信号通路等.鉴定出的生物标记ASPN、CRYM、CSDC2、ECM2、FCN3、HMOX2、LAD1、SERPINA3、TSPYL2和TUBA3D,具备良好的诊断效能.DCM心肌组织中辅助型T细胞2(Th2)、中央记忆型CD4+T细胞、中央记忆型CD8+T细胞显著上调;活化的树突状细胞、嗜酸性粒细胞、髓样抑制细胞、巨噬细胞、肥大细胞、自然杀伤细胞、中性粒细胞、调节性T细胞、滤泡辅助性T细胞、辅助型T细胞1(Th1)、辅助型T细胞17(Th17)和效应记忆型CD8+T细胞显著下调.结论 ASPN、CRYM、CSDC2、ECM2、FCN3、HMOX2、LAD1、SERPINA3、TSPYL2和TUBA3D可能作为DCM的生物标记.多种免疫细胞可能介导着DCM的发生发展.
Abstract
Objective To screen biomarkers of dilated cardiomyopathy(DCM)by applying weighted gene co-expression network analysis(WGCNA)combined with machine learning methods,and analyze immune infiltration.Methods DCM transcription profilesfrom gene expression omnibus(GEO)was given a differential analysis.The differentially expressed genes(DEG)were analyzed based on gene ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis.WGCNA and LASSO regression were integrated to screen DCM biomarkers.The diagnostic efficacy of ROC curve was reviewed and immuneinfiltration was analyzed.Results There were 268 DEG in left ventricular tissue of DCM,which were mainly involved in regulation of inflammatory response,epithelial cell proliferation,collagen-containing extracellular matrix(ECM),ECM structural constituent,and meanwhile,took part in complement,coagulation cascades,phagosome,signaling pathway of JAK-STAT,arachidonic acid metabolism and signaling pathway of interleukin 17(IL-17).The identified biomarkers had higher diagnostic efficacy,including ASPN,CRYM,CSDC2,ECM2,FCN3,HMOX2,LAD1,SERPINA3,TSPYL2 and TUBA3D.The expressions of T helper 2 cell(Th2),central memory CD4+T cells and central memory CD8+T cells were significantly up-regulated in DCM myocardium.The expressions of activated dendritic cells,eosinophils,myeloid-derived suppressor cells,macrophages,mast cells,natural killer cells,neutrophils,regulatory T cells,follicular T helper cells,T helper 1 cells(Th1),T helper 17 cells(Th17)and effector memory CD8+T cells were significantly down-regulated.Conclusion ASPN,CRYM,CSDC2,ECM2,FCN3,HMOX2,LAD1,SERPINA3,TSPYL2 and TUBA3D may be taken as biomarkers for DCM.A variety of immune cells may mediate the occurrence and development of DCM.
基金项目
陕西省人民医院科技人才支持计划项目(菁英人才)资助(2022JY-45)
陕西省人民医院科技发展孵化基金资助(2023YJY-63)
NETL
NSTL
万方数据