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Abstract：
Arrhythmogenic right ventricular cardiomyopathy (ARVC) can lead to sudden cardiac death and life-threatening heart failure. Due to its high fatality rate and limited therapies, the pathogenesis and diagnosis biomarker of ARVC needs to be explored urgently. This study aimed to explore the lncRNA-miRNA-mRNA competitive endogenous RNA (ceRNA) network in ARVC. The mRNA and lncRNA expression datasets obtained from the Gene Expression Omnibus (GEO) database were used to analyze differentially expressed mRNA (DEM) and lncRNA (DElnc) between ARVC and non-failing controls. Differentially expressed miRNAs (DEmiRs) were obtained from the previous profiling work. Using starBase to predict targets of DEmiRs and intersecting with DEM and DElnc, a ceRNA network of lncRNA-miRNA-mRNA was constructed. The DEM and DElnc were validated by real-time quantitative PCR in human heart tissue. Protein-protein interaction network and weighted gene co-expression network analyses were used to identify hub genes. A logistic regression model for ARVC diagnostic prediction was established with the hub genes and their ceRNA pairs in the network. A total of 448 DEMs (282 upregulated and 166 downregulated) were identified, mainly enriched in extracellular matrix and fibrosis-related GO terms and KEGG pathways, such as extracellular matrix organization and collagen fibril organization. Four mRNAs and two lncRNAs, including COL1A1, COL5A1, FBN1, BGN, XIST, and LINC00173 identified through the ceRNA network, were validated by real-time quantitative PCR in human heart tissue and used to construct a logistic regression model. Good ARVC diagnostic prediction performance for the model was shown in both the training set and the validation set. The potential lncRNA-miRNA-mRNA regulatory network and logistic regression model established in our study may provide promising diagnostic methods for ARVC.
摘要：
致心律失常性右心室心肌病（ARVC）可导致心脏猝死和危及生命的心力衰竭。由于其高致死率和有限的治疗方法，ARVC的发病机制和诊断生物标志物亟待探索。本研究旨在探索ARVC中lncRNA-miRNA-mRNA竞争性内源性RNA(ceRNA)网络。从基因表达综合(GEO)数据库获得的mRNA和lncRNA表达数据集用于分析ARVC和非失败对照之间的差异表达的mRNA(DEM)和lncRNA(DElnc)。差异表达的miRNA(DEmiR)从先前的谱分析工作中获得。使用starBase预测DEmiR的目标，并与DEM和DElnc相交，构建了lncRNA-miRNA-mRNA的ceRNA网络。通过实时定量PCR在人心脏组织中验证DEM和DElnc。使用蛋白质-蛋白质相互作用网络和加权基因共表达网络分析来识别集线器基因。利用网络中的hub基因及其ceRNA对建立了ARVC诊断预测的逻辑回归模型。总共确定了448个DEM（282个上调和166个下调），主要富集在细胞外基质和纤维化相关的GO术语和KEGG通路中，如细胞外基质组织和胶原原纤维组织。四个mRNAs和两个lncRNAs，包括COL1A1,COL5A1,FBN1,BGN,XIST,和LINC00173通过ceRNA网络鉴定，通过实时定量PCR在人体心脏组织中进行验证，并用于构建逻辑回归模型。训练集和验证集均显示了模型的良好ARVC诊断预测性能。我们研究中建立的潜在lncRNA-miRNA-mRNA调控网络和逻辑回归模型可能为ARVC提供有希望的诊断方法。
