Chen Peipei, Long Bo, Xu Yi, Wu Wei, Zhang Shuyang
Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Central Research Laboratory, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Front Physiol. 2018 Dec 6;9:1778. doi: 10.3389/fphys.2018.01778. eCollection 2018.
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 ( = 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 (, and ) in ARVC samples were further identified by GSNCA method. Finally, we used -test and receiver operating characteristic (ROC) curves for validating hub genes, results showed significant differences in -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.
作为导致年轻人因心脏骤停而猝死的常见疾病之一，致心律失常性右室心肌病（ARVC）是一种心肌疾病，其进展涉及一个复杂的基因相互作用网络，该网络影响其诊断和预后。在我们的研究中，筛选了差异表达基因（DEG），并建立了加权基因共表达网络分析（WGCNA）和基因集网络相关性分析（GSNCA），以识别与ARVC致病机制相关的关键基因和通路（n = 12）。在该研究中，结果表明，在非衰竭供体心肌样本和ARVC组织之间总共存在619个DEG（FDR < 0.05）。WGCNA分析确定了两个基因模块（棕色和蓝绿色）与ARVC状态最显著相关。然后，通过GSNCA方法进一步确定了ARVC样本中与ARVC相关的四个关键生物学通路（细胞因子-细胞因子受体相互作用、趋化因子信号通路、神经活性配体受体相互作用和JAK-STAT信号通路）和四个枢纽基因（、和）。最后，我们使用t检验和受试者工作特征（ROC）曲线来验证枢纽基因，结果显示t检验存在显著差异，其AUC面积均大于0.8。总之，这些结果揭示了这四个新的枢纽基因以及可能参与ARVC诊断的关键通路。尽管关联的影响需要进一步的实验验证，但我们的研究结果表明，基于系统生物学的计算方法可能补充传统的全基因方法，因此，可能为ARVC等罕见病的治疗干预提供新的见解。
