BACKGROUND To detect the development, function and therapeutic potential of epicardial adipose tissue (EAT); analyze a related gene expression dataset, including data from neonates, infants, and children with congenital heart disease (CHD); compare the data to identify the codifferentially expressed (DE) mRNAs and lncRNAs and the corresponding miRNAs; generate a potential competitive endogenous RNA (ceRNA) network; and assess the involvement of immunocyte infiltration in the development of the EAT. METHODS Multiple algorithms for linear models for microarray data algorithms (LIMMA), CIBERSORT, gene-set enrichment analysis (GSEA), and gene set variation analysis (GSVA) were used. The miRcode, miRDB, miRTarBase, and TargetScan database were used to construct the ceRNA network. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DE mRNAs were performed. RESULTS Thirteen co-DE mRNAs and 47 co-DE lncRNAs were subsequently identified. The related categories included negative regulation of myoblast differentiation, regulation of ion transmembrane transport, and heart development, which were primarily identified for further pathway enrichment analysis. Additionally, the hub ceRNA network in EAT development involving MIR210HG, hsa-miR-449c-5p, and CACNA2D4 was generated and shown to target monocyte infiltration. CONCLUSION These findings suggest that the pathways of myoblast differentiation and ion transmembrane transport may be potential hub pathways involved in EAT development in CHD patients. In addition, the network includes monocytes, MIR210HG, and CACNA2D4, which were shown to target the RIG-I-like receptor signaling pathway and PPAR signaling pathway, indicating that these factors may be novel regulators and therapeutic targets in EAT development.

中文翻译：

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先天性心脏病心外膜脂肪组织发育过程中差异免疫细胞浸润和潜在的ceRNA网络的综合分析。

背景技术检测心外膜脂肪组织（EAT）的发育，功能和治疗潜力；分析相关的基因表达数据集，包括新生儿，婴儿和患有先天性心脏病（CHD）的数据；比较数据以鉴定共差异表达（DE）的mRNA和lncRNA以及相应的miRNA；产生潜在的竞争性内源性RNA（ceRNA）网络；并评估免疫细胞浸润与EAT发生的关系。方法使用了多种线性模型算法，包括微阵列数据算法（LIMMA），CIBERSORT，基因集富集分析（GSEA）和基因集变异分析（GSVA）。使用miRcode，miRDB，miRTarBase和TargetScan数据库构建ceRNA网络。对DE mRNA进行了基因本体论（GO）和《京都基因与基因组百科全书》（KEGG）途径富集分析。结果随后鉴定出13种co-DE mRNA和47种co-DE lncRNA。相关类别包括成肌细胞分化的负调节，离子跨膜转运的调节和心脏发育，这些主要被确定用于进一步的途径富集分析。此外，生成了涉及MIR210HG，hsa-miR-449c-5p和CACNA2D4的EAT开发中的中枢ceRNA网络，并显示其靶向单核细胞浸润。结论这些发现提示成肌细胞分化和离子跨膜转运的途径可能是冠心病患者饮食发展中潜在的枢纽途径。此外，该网络还包括单核细胞MIR210HG和CACNA2D4，