Myocyte enhancer factor 2A (MEF2A) plays an important role in cell proliferation, differentiation and survival. Functional deletion or mutation in MEF2A predisposes individuals to cardiovascular disease mainly caused by vascular endothelial dysfunction. However, the effect of the inhibition of MEF2A expression on human coronary artery endothelial cells (HCAECs) is unclear. In this study, expression of MEF2A was inhibited by specific small interference RNA (siRNA), and changes in mRNA profiles in response to MEF2A knockdown were analyzed using an Agilent human mRNA array. Silencing of MEF2A in HCAECs accelerated cell senescence and suppressed cell proliferation. Microarray analysis identified 962 differentially expressed genes (DEGs) between the MEF2A knockdown group and the negative control group. Annotation clustering analysis showed that the DEGs were preferentially enriched in gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to proliferation, development, survival, and inflammation. Furthermore, 61 of the 578 downregulated DEGs have at least one potential MEF2A binding site in the proximal promoter and were mostly enriched in the GO terms “reproduction” and “cardiovascular.” The protein–protein interaction network analyzed for the downregulated DEGs and the DEGs in the GO terms “cardiovascular” and “aging” revealed that PIK3CG, IL1B, IL8, and PRKCB were included in hot nodes, and the regulation of the longevity-associated gene PIK3CG by MEF2A has been verified at the protein level, suggesting that PIK3CG might play a key role in MEF2A knockdown induced HCAEC senescence. MEF2A knockdown accelerates HCAEC senescence, and the underlying molecular mechanism may be involved in down-regulation of the genes related with cell proliferation, development, inflammation and survival, in which PIK3CG may play a key role.

Background Atherosclerosis (AS) is a chronic inflammatory disease characterized by lipid necrosis and vessel wall hardening manifested by intimal lipid deposition and smooth muscle cell proliferation. The interactions and mechanisms involved in the development of atherosclerotic plaques are important to understand. Growing evidence shows that long non-coding RNAs (lncRNAs) can act as competing endogenous RNAs (ceRNAs) by competitively splicing microRNAs (miRNAs) to regulate key cellular and molecular processes during various stages of AS. The aim of this study was to elucidate the potential role of lncRNAs, miRNAs and mRNA ceRNA networks in advanced atherosclerotic plaque tissue compared with normal intimal tissue. Methods The limma package was used to identify differentially expressed lncRNAs and mRNAs (DElncRNAs and DEmRNAs, respectively) based on expression profiles from the Gene Expression Omnibus (GEO) datasets GSE97210 and GSE137578. Differentially expressed genes (DEGs) were further analysed by Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis and Metascape online tools. The intersection of the two DEG datasets was screened for hub genes. In addition, a lncRNA–miRNA–mRNA–Hub gene regulatory network was constructed to investigate key genes that may influence AS pathogenesis. Results A total of 393 probes associated with DElncRNA, including 122 up-regulated and 271 down-regulated probes were identified from the GSE97210 dataset, and 675 up-regulated and 689 down-regulated DEGs were also identified in atherosclerotic samples. In the validation dataset (GSE137578), 400 up-regulated and 388 down-regulated DEGs in atherosclerotic samples were identified. CeRNA networks were constructed based on the expression profiles of lncRNAs, miRNAs and mRNAs in patients with AS, and a total of 80 lncRNA nodes, 30 miRNA nodes, 80 mRNA nodes and 625 edges were identified as differential expression profiles. Four core genes, including BAMBI, TBX18, DLGAP5 and RRM2, were screened after taking intersections with the validation dataset. Then, a ceRNA regulatory relationship network consisting of these four mRNAs, three miRNAs and 13 lncRNAs was established. Conclusion Our study found that BAMBI, TBX18, DLGAP5 and RRM2 are potentially involved in the pathological process of AS.