Objective: Present study investigated the mechanism of heart failure associated with coronavirus infection and predicted potential effective therapeutic drugs against heart failure associated with coronavirus infection. Methods: Coronavirus and heart failure were searched in the Gene Expression Omnibus (GEO) and omics data were selected to meet experimental requirements. Differentially expressed genes were analyzed using the Limma package in R language to screen for differentially expressed genes. The two sets of differential genes were introduced into the R language cluster Profiler package for gene ontology (GO) and Kyoto gene and genome encyclopedia (KEGG) pathway enrichment analysis. Two sets of intersections were taken. A protein interaction network was constructed for all differentially expressed genes using STRING database and core genes were screened. Finally, the apparently accurate treatment prediction platform (EpiMed) independently developed by the team was used to predict the therapeutic drug. Results: The GSE59185 coronavirus data set was searched and screened in the GEO database, and divided into wt group, ΔE group, Δ3 group, Δ5 group according to different subtypes, and compared with control group. After the difference analysis, 191 up-regulated genes and 18 down-regulated genes were defined. The GEO126062 heart failure data set was retrieved and screened from the GEO database. A total of 495 differentially expressed genes were screened, of which 165 were up-regulated and 330 were down-regulated. Correlation analysis of differentially expressed genes between coronavirus and heart failure was performed. After cross processing, there were 20 GO entries, which were mainly enriched in virus response, virus defense response, type Ⅰ interferon response, γ interferon regulation, innate immune response regulation, negative regulation of virus life cycle, replication regulation of viral genome, etc. There were 5 KEGG pathways, mainly interacting with tumor necrosis factor (TNF) signaling pathway, interleukin (IL)-17 signaling pathway, cytokine and receptor interaction, Toll-like receptor signaling pathway, human giant cells viral infection related. All differentially expressed genes were introduced into the STRING online analysis website for protein interaction network analysis, and core genes such as signal transducer and activator of transcription 3, IL-10, IL17, TNF, interferon regulatory factor 9, 2'-5'-oligoadenylate synthetase 1, mitogen-activated protein kinase 3, radical s-adenosyl methionine domain containing 2, c-x-c motif chemokine ligand 10, caspase 3 and other genes were screened. The drugs predicted by EpiMed's apparent precision treatment prediction platform for disease-drug association analysis were mainly TNF-α inhibitors, resveratrol, ritonavir, paeony, retinoic acid, forsythia, and houttuynia cordata. Conclusions: The abnormal activation of multiple inflammatory pathways may be the cause of heart failure in patients after coronavirus infection. Resveratrol, ritonavir, retinoic acid, amaranth, forsythia, houttuynia may have therapeutic effects. Future basic and clinical research is warranted to validate present results and hypothesis.
目的： 探讨冠状病毒感染所致心力衰竭（心衰）的机制，并预测对其可能有效的药物。 方法： 在基因表达数据库（GEO）检索冠状病毒和心衰，并筛选符合实验要求的组学数据。采用R语言Limma程序包进行差异表达基因分析，筛选差异表达基因。将两组差异基因导入R语言clusterProfiler包进行基因本体学（GO）和京都基因与基因组百科全书（KEGG）通路富集分析，取两组结果的交集。采用STRING数据库对所有差异表达基因构建蛋白质互作网络并筛选核心基因。最后采用团队自主研发的表观精准治疗预测平台EpiMed预测冠状病毒所致心衰的治疗药物。 结果： 在GEO数据库检索并筛选得到GSE59185冠状病毒数据集，根据不同的亚型分为wt组、∆E组、∆3组、∆5组、对照组5组样本，差异分析发现各亚组交集上调基因191个，下调基因18个。在GEO数据库检索并筛选得到GSE126062心衰数据集，共筛选差异表达基因495个，其中上调165个，下调330个。冠状病毒与心衰差异表达基因富集分析，取交集处理共有GO条目20条，主要富集在病毒反应、病毒防御反应、Ⅰ型干扰素反应、γ干扰素调节、先天免疫反应调节、病毒生命周期负调控、病毒基因组复制调控等；共有5条KEGG通路，主要与肿瘤坏死因子（TNF）信号通路、白细胞介素（IL）-17信号通路、细胞因子与受体相互作用、Toll样受体信号通路、人类巨细胞病毒感染有关。蛋白互作网络分析筛选出信号传导及转录激活蛋白3、IL-10、IL-17、TNF、干扰素调节因子9、2′, 5′-寡腺苷酸合成酶1、丝裂原活化蛋白激酶3、S-腺苷甲硫氨酸基区域蛋白2、CXC趋化因子配体10、半胱氨酸天冬氨酸蛋白酶3共10个核心基因。EpiMed平台预测可能对冠状病毒所致心衰有效的药物为TNF-α抑制剂、白藜醇、利托那韦、白芍、维甲酸、连翘、鱼腥草等。 结论： 多个炎症通路的异常活化可能是冠状病毒感染所致心衰的原因，白藜芦醇、利托那韦、维甲酸、白芍、连翘、鱼腥草可能对其具有治疗作用。.
Keywords: Bioinformatics; Coronavirus infections; Drug prediction; Heart failure.