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Bioinformatic screening for key miRNAs and genes associated with myocardial infarction.

ABSTRACT: Despite significant advances in understanding of the causes of and treatment of myocardial infarction (MI) in recent years, morbidity and mortality is still high. The aim of this study was to identify miRNA and genes potentially associated with MI. mRNA and miRNA expression datasets were downloaded from the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/). Interactions between miRNA and the expression and function of target genes were analyzed, and a protein-protein interaction network was constructed. The diagnostic value of identified miRNA and genes was assessed. Quantitative RT-PCR was applied to validate the results of the bioinformatics analysis. MiR-27a, miR-31*, miR-1291, miR-139-5p, miR-204, miR-375, and target genes including CX3CR1,HSPA6, and TPM3 had potential diagnostic value. The genes TFEB,IRS2,GRB2,FASLG,LIMS1,CX3CR1,HSPA6,TPM3,LAT2,CEBPD,AQP9, and MAPKAPK2 were associated with recovery from MI. In conclusion, the identified miRNA and genes might be associated with the pathology of MI.

SUBMITTER: Wu K

PROVIDER: S-EPMC5985982 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Bioinformatic screening for key miRNAs and genes associated with myocardial infarction.

Wu Ke K Zhao Qiang Q Li Zhengmei Z Li Nannan N Xiao Qiang Q Li Xiuchang X Zhao Quanming Q

FEBS open bio 20180419 6

Despite significant advances in understanding of the causes of and treatment of myocardial infarction (MI) in recent years, morbidity and mortality is still high. The aim of this study was to identify miRNA and genes potentially associated with MI. mRNA and miRNA expression datasets were downloaded from the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/). Interactions between miRNA and the expression and function of target genes were analyzed, and a protein-protein interactio ...[more]

PMID: 29928570

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Project description:Background Inflammation and immune cell infiltration in infarcted myocardial tissue are critical to myocardial infarction (MI) prognosis, and alterations in sphingolipid metabolism (SM) have been shown to potentially influence the inflammatory response and induce cardioprotection, but the underlying mechanisms are unclear. We therefore performed bioinformatics analysis to screen for key genes of SM in MI immune cells. Methods Three matrix files including GSE61145, GSE23294, and GSE71906 were downloaded from the Gene Expression Omnibus (GEO) database. GSE61145 was a human peripheral blood database, and GSE23294 and GSE71906 were 2 mouse myocardial tissue databases. R and annotation packages were used to screen for differentially-expressed genes (DEGs). Datasets of human and mouse cardiac tissues were downloaded from the GEO database for subsequent validation. The downloaded platform and matrix files were processed using R language and annotation packages. Key targets and enrichment pathways were identified using Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The Wilcoxon test was performed on the genes involved in SM pathways in neutrophils. Results A total of 261 DEGs were obtained from human peripheral blood datasets, among which 101 were immune-related. GO analysis revealed that neutrophil activation, T cell activation, and T cell differentiation were significantly enriched in the immune-related DEGs. Three types of immune cells were identified in infarcted myocardial tissues. In addition, 194 DEGs were obtained from mouse myocardial tissue data, among which 6 SM-related genes (Asah1, Degs1, Neu1, Sptlc2, Sphk1, and Gba2) were significantly associated with MI. Evaluation of the relationships between these DEGs and neutrophils showed that the expression of the Sptlc2 gene was significantly upregulated in neutrophils of the MI group, while the expression levels of the Asah1 and Degs1 genes were downregulated. Conclusions We identified 3 SM-related genes that were highly associated with neutrophils in MI, which may advance our understanding of SM in immune cells after MI.

Project description:BackgroundAcute myocardial infarction (AMI) is regarded as an urgent clinical entity, and identification of differentially expressed genes, lncRNAs, and altered pathways shall provide new insight into the molecular mechanisms behind AMI.Materials and methodsMicroarray data was collected to identify key genes and lncRNAs involved in AMI pathogenesis. The differential expression analysis and gene set enrichment analysis (GSEA) were employed to identify the upregulated and downregulated genes and pathways in AMI. The protein-protein interaction network and protein-RNA interaction analysis were utilized to reveal key long noncoding RNAs.ResultsIn the present study, we utilized gene expression profiles of circulating endothelial cells (CEC) from 49 patients of AMI and 50 controls and identified a total of 552 differentially expressed genes (DEGs). Based on these DEGs, we also observed that inflammatory response-related genes and pathways were highly upregulated in AMI. Mapping the DEGs to the protein-protein interaction (PPI) network and identifying the subnetworks, we found that OMD and WDFY3 were the hub nodes of two subnetworks with the highest connectivity, which were found to be involved in circadian rhythm and organ- or tissue-specific immune response. Furthermore, 23 lncRNAs were differentially expressed between AMI and control groups. Specifically, we identified some functional lncRNAs, including XIST and its antisense RNA, TSIX, and three lncRNAs (LINC00528, LINC00936, and LINC01001), which were predicted to be interacting with TLR2 and participate in Toll-like receptor signaling pathway. In addition, we also employed the MMPC algorithm to identify six gene signatures for AMI diagnosis. Particularly, the multivariable SVM model based on the six genes has achieved a satisfying performance (AUC = 0.97).ConclusionIn conclusion, we have identified key regulatory lncRNAs implicated in AMI, which not only deepens our understanding of the lncRNA-related molecular mechanism of AMI but also provides computationally predicted regulatory lncRNAs for AMI researchers.

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Bioinformatic screening for key miRNAs and genes associated with myocardial infarction.

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Bioinformatic screening for key miRNAs and genes associated with myocardial infarction.

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