Comprehensive analysis of differential immunocyte infiltration and the potential ceRNA networks during epicardial adipose tissue development in congenital heart disease

• Published in: Journal of translational medicine Vol. 18; no. 1; p. 111
• Main Authors: Ma, Li, Shi, Wanting, Ma, Xun, Zou, Minghui, Chen, Weidan, Li, Wenlei, Zou, Rongjun, Chen, Xinxin
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 02-03-2020; BioMed Central; BMC
• Subjects: Adipose Tissue; Algorithms; Atherosclerosis; Biochemistry; Bioinformatics analysis; Body fat; Cardiomyocytes; Cardiovascular diseases; Cell growth; Child; Competing endogenous RNA network; Computational biology; Congenital heart defects; Congenital heart disease; Coronary artery disease; Developmental biology; Diseases; Fatty acids; Gene expression; Gene Regulatory Networks; Gene set enrichment analysis; Genes; Genetic aspects; Genetic disorders; Genomes; Genomics; Health aspects; Heart Defects, Congenital - genetics; Heart diseases; Humans; Immunocyte infiltration; Immunocytochemistry; Infant, Newborn; Infants; Messenger RNA; Metabolism; Metabolites; MicroRNA; MicroRNAs - genetics; Newborn infants; Oxidative stress; Pediatric cardiology; RNA; RNA, Long Noncoding; Software; Statistical analysis; Time series; Veins & arteries; China; Bioinformatics analysis; Congenital heart disease; Competing endogenous RNA network; Immunocyte infiltration
• ISSN: 1479-5876; 1479-5876
• DOI: 10.1186/s12967-020-02279-y

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.
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.
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.
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.