Table_1_Identification of Crucial Genes and Pathways in Human Arrhythmogenic Right Ventricular Cardiomyopathy by Coexpression Analysis.XLSX

As one common disease causing young people to die suddenly due to cardiac arrest, arrhythmogenic right ventricular cardiomyopathy (ARVC) is a disorder of heart muscle whose progression covers one complicated gene interaction network that influence the diagnosis and prognosis of it. In our research, differentially expressed genes (DEGs) were screened, and we established a weighted gene coexpression network analysis (WGCNA) and gene set net correlations analysis (GSNCA) for identifying crucial genes as well as pathways related to ARVC pathogenic mechanism (n = 12). In the research, the results demonstrated that there were 619 DEGs in total between non-failing donor myocardial samples and ARVC tissues (FDR < 0.05). WGCNA analysis identified the two gene modules (brown and turquoise) as being most significantly associated with ARVC state. Then the ARVC-related four key biological pathways (cytokine–cytokine receptor interaction, chemokine signaling pathway, neuroactive ligand receptor interaction, and JAK-STAT signaling pathway) and four hub genes (CXCL2, TNFRSF11B, LIFR, and C5AR1) in ARVC samples were further identified by GSNCA method. Finally, we used t-test and receiver operating characteristic (ROC) curves for validating hub genes, results showed significant differences in t-test and their AUC areas all greater than 0.8. Together, these results revealed that the new four hub genes as well as key pathways that might be involved into ARVC diagnosis. Even though further experimental validation is required for the implication by association, our findings demonstrate that the computational methods based on systems biology might complement the traditional gene-wide approaches, as such, might offer a new insight in therapeutic intervention within rare diseases of people like ARVC.

作为一种可导致年轻人心搏性猝死的常见疾病，致心律失常性右室心肌病（arrhythmogenic right ventricular cardiomyopathy, ARVC）是一种心肌疾病，其病程进展涉及一套复杂的基因互作网络，该网络会影响该病的诊断与预后。本研究筛选了差异表达基因（differentially expressed genes, DEGs），并构建了加权基因共表达网络分析（weighted gene coexpression network analysis, WGCNA）与基因集网络相关性分析（gene set net correlations analysis, GSNCA），以识别与ARVC致病机制相关的关键基因及通路（样本量n=12）。本研究结果显示，在未衰竭供体心肌样本与ARVC组织样本间共存在619个差异表达基因，错误发现率（false discovery rate, FDR）<0.05。通过WGCNA分析，我们鉴定出棕色与绿松石色两个基因模块与ARVC状态显著相关。随后通过GSNCA方法，我们进一步明确了ARVC样本中4条关键生物学通路：细胞因子-细胞因子受体互作、趋化因子信号通路、神经活性配体-受体互作以及JAK-STAT信号通路，以及4个枢纽基因（hub genes）：CXCL2、TNFRSF11B、LIFR与C5AR1。最后，我们采用t检验与受试者工作特征（receiver operating characteristic, ROC）曲线对枢纽基因进行验证，结果显示t检验存在显著差异，且所有基因的曲线下面积（AUC）均大于0.8。综上，本研究结果揭示了这4个新的枢纽基因与关键通路可能参与ARVC的发病过程。尽管该关联结论尚需进一步实验验证以明确其应用价值，但本研究表明，基于系统生物学的计算方法可作为传统全基因组研究的补充，有望为ARVC这类罕见病的治疗干预提供新的思路。