Table_4_Identification of Potential miRNA-mRNA Regulatory Network Contributing to Hypertrophic Cardiomyopathy (HCM).DOCX

Background: Hypertrophic cardiomyopathy (HCM) is a myocardial disease with unidentified pathogenesis. Increasing evidence indicated the potential role of microRNA (miRNA)-mRNA regulatory network in disease development. This study aimed to explore the miRNA-mRNA axis in HCM.
Methods: The miRNA and mRNA expression profiles obtained from the Gene Expression Omnibus (GEO) database were used to identify differentially expressed miRNAs (DEMs) and genes (DEGs) between HCM and normal samples. Target genes of DEMs were determined by miRTarBase. Gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to identify biological functions of the DEGs and DEMs. miRNA-mRNA regulatory network was constructed to identify the hub genes and miRNAs. Logistic regression model for HCM prediction was established basing on the network.
Results: A total of 224 upregulated and 366 downregulated DEGs and 10 upregulated and 14 downregulated DEMs were determined. We identified 384 DEM-targeted genes, and 20 of them were overlapped with the DEGs. The enriched functions include extracellular structure organization, organ growth, and phagosome and melanoma pathways. The four miRNAs and three mRNAs, including hsa-miR-373, hsa-miR-371-3p, hsa-miR-34b, hsa-miR-452, ARHGDIA, SEC61A1, and MYC, were identified through miRNA-mRNA regulatory network to construct the logistic regression model. The area under curve (AUC) values over 0.9 suggested the good performance of the model.
Conclusion: The potential miRNA-mRNA regulatory network and established logistic regression model in our study may provide promising diagnostic methods for HCM.

背景：肥厚型心肌病（Hypertrophic cardiomyopathy, HCM）是一种发病机制尚未明确的心肌疾病。越来越多的研究证据表明，microRNA（miRNA）-mRNA调控网络在疾病发生发展中发挥潜在作用。本研究旨在探讨肥厚型心肌病中的miRNA-mRNA调控轴。
方法：从基因表达综合数据库（Gene Expression Omnibus, GEO）获取的miRNA及mRNA表达谱，用于筛选肥厚型心肌病样本与正常对照样本间的差异表达miRNA（differentially expressed miRNAs, DEMs）及差异表达基因（differentially expressed genes, DEGs）。通过miRTarBase数据库预测DEMs的靶基因。对DEGs及DEMs开展基因本体（Gene Ontology, GO）注释与京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）通路富集分析，以明确其生物学功能。构建miRNA-mRNA调控网络以筛选核心基因及核心miRNA，并基于该网络构建用于肥厚型心肌病预测的logistic回归模型。
结果：本研究共鉴定得到224个上调差异表达基因、366个下调差异表达基因，以及10个上调差异表达miRNA、14个下调差异表达miRNA。共预测得到384个DEMs靶基因，其中20个与DEGs存在交集。富集得到的生物学功能包括细胞外结构组织、器官生长，以及吞噬体通路与黑色素瘤通路。通过miRNA-mRNA调控网络筛选得到4个miRNA及3个mRNA，分别为hsa-miR-373、hsa-miR-371-3p、hsa-miR-34b、hsa-miR-452、ARHGDIA、SEC61A1及MYC，并以此构建logistic回归模型。该模型的曲线下面积（area under curve, AUC）值均大于0.9，表明模型具备良好的预测性能。
结论：本研究揭示的潜在miRNA-mRNA调控网络及构建的logistic回归模型，可为肥厚型心肌病的临床诊断提供具有应用前景的新思路与方法。