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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:Background:Acute 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 Methods:Microarray 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. Results:In 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). Conclusion:In 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.

Project description:Objective A growing body of emerging evidence indicates that metabolic processes play a pivotal role in the biological processes underlying acute myocardial infarction (AMI). The aim of the current study was to identify featured metabolism-related genes in patients with AMI using a support vector machine (SVM) and to further explore the value of these genes in the diagnosis of AMI. Methods Gene microarray expression data related to AMI were downloaded from the GSE66360 dataset in the Gene Expression Omnibus (GEO) database. This data set consisted of 50 AMI samples and 49 normal controls that were randomly classified into a discovery cohort (21 AMI samples and 22 normal controls) and a validation cohort (28 AMI and 28 normal controls). We applied a machine learning method that combined SVM with recursive feature elimination (RFE) to discriminate AMI patients from normal controls. Based on this, an SVM classifier was constructed. Receiver operating characteristic (ROC) analysis was used to investigate the predictive value for the early diagnosis of AMI in the two cohorts and was then further verified in an independent external cohort. Results Three metabolism-related genes were identified based on SVM-RFE (AKR1C3, GLUL, and PDE4B). The SVM classifier based on the three genes allowed for excellent discrimination between AMI and healthy samples in both the discovery cohort (AUC = 0.989) and the validation cohort (AUC = 0.964), and this was further confirmed in the GSE68060 dataset (AUC = 0.839). Additionally, the SVM classifier allowed for perfect discrimination between recurrent AMI events and nonrecurrent events in the GSE68060 cohort (AUC = 0.992). GO and KEGG pathway enrichment analysis of the identified featured genes revealed significant enrichment of specific metabolic pathways. Conclusion The identified metabolism-related genes may play important roles in the development of AMI and may represent diagnostic and therapeutic biomarkers of AMI.

Project description:BackgroundAcute myocardial infarction (AMI) is the most serious type of heart disease. Clinically, there is an urgent need to discover diagnostic biomarkers for the early diagnosis of AMI.MethodsSerum proteomic profiles in AMI patients, healthy controls, and stable angina pectoris (SAP) patients were explored and compared by iTRAQ-2DLC-MS/MS. The clinical data of AMI patients were also analyzed. Differentially expressed proteins were validated by enzyme linked immunosorbent assay (ELISA), and diagnostic models were constructed.ResultsA total of 39 differentially expressed proteins were identified in AMI patients. The results showed that the serum levels of apolipoprotein E (APOE) in AMI patients were notably higher than those in the healthy controls (P=0.0172). The serum levels of aspartate aminotransferase (AATC) in AMI patients were markedly higher than those in the healthy controls and SAP patients (P<0.0001 and P<0.0001, respectively). The serum levels of fibronectin (FINC) in SAP patients were significantly higher than those in the healthy controls and AMI patients (P=0.0043 and P=0.0044, respectively). Clinical data analysis showed a considerable difference in blood glucose levels, troponin I (TNI), and creatine kinase (CK) in AMI patients compared with SAP patients and healthy controls. A diagnostic model consisting of AATC and clinical indicators [lactate dehydrogenase (LDH) and CK] was established to distinguish between AMI patients and healthy controls, with an area under the curve (AUC) value of 0.993 sensitivity and specificity of 96.2% and 96.3%, respectively. A diagnostic model consisting of AATC and CK was established to distinguish between AMI patients and SAP patients, with an AUC value of 0.975 and a sensitivity and specificity of 85.2% and 79.30%, respectively.ConclusionsIn this study, differentially expressed proteins in AMI patients were combined with clinical indexes, LDH and CK, and two diagnostic models were constructed. This study may provide meaningful data for the early diagnosis of AMI.

Dataset Information

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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