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Identification of Potential Molecular Mechanisms and Candidate Genes Involved in The Acute Phase of Myocardial Infarction.

ABSTRACT: Objective:This study used bioinformatics to determine genetic factors involved in progression of acute myocardial infarction (MI). Materials and Methods:In this prospective study, gene expression profile GSE59867 was downloaded from the Gene Expression Omnibus database, which contained 46 normal samples obtained from stable coronary artery disease patients (n=46) who were without history of MI (control) and 390 samples from patients (n=111) who had evolving ST-segment elevation myocardial infarction (STEMI) as the MI group. These samples were divided into 4 groups based on time points. After identification of differentially expressed genes (DEGs), we conducted hierarchical clustering and functional enrichment analysis. Protein interaction and transcriptional regulation among DEGs were analysed. Results:We observed 8 clusters of DEGs that had a peak or a minimum at the t=1 time point according to gene expression levels. Upregulated DEGs showed significant enrichment in the biological process, single-organism cellular process, response to stimulus and stress, and osteoclast differentiation and lysosome. Downregulated DEGs enriched in the T-cell receptor signalling pathway and natural killer cell mediated cytotoxicity. We identified multiple genes, including signal transducer and activator of transcription 3 (STAT3); LCK proto-oncogene, Src family tyrosine kinase (LCK); and FYN proto-oncogene, Src family tyrosine kinase (FYN) from the protein-protein interaction (PPI) network and/or the transcriptional regulatory network. Conclusion:Cytokine-mediated inflammation, lysosome and osteoclast differentiation, and metabolism processes, as well as STAT3 may be involved in the acute phase of MI.

SUBMITTER: Yang Y

PROVIDER: S-EPMC6005004 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Identification of Potential Molecular Mechanisms and Candidate Genes Involved in The Acute Phase of Myocardial Infarction.

Yang Yushuang Y Yang Jie J Sui Fenghua F Huo Pengfei P Yang Hailing H

Cell journal 20180515 3

<h4>Objective</h4>This study used bioinformatics to determine genetic factors involved in progression of acute myocardial infarction (MI).<h4>Materials and methods</h4>In this prospective study, gene expression profile GSE59867 was downloaded from the Gene Expression Omnibus database, which contained 46 normal samples obtained from stable coronary artery disease patients (n=46) who were without history of MI (control) and 390 samples from patients (n=111) who had evolving ST-segment elevation my ...[more]

PMID: 29845799

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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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Identification of Potential Molecular Mechanisms and Candidate Genes Involved in The Acute Phase of Myocardial Infarction.

Publications

Identification of Potential Molecular Mechanisms and Candidate Genes Involved in The Acute Phase of Myocardial Infarction.

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