Transciptome analysis of acute myocardial infarction
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ABSTRACT:
PROVIDER: PRJNA508899 | ENA |
REPOSITORIES: ENA
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Project description:Analysis of peripheral blood specimens from patients with acute myocardial infarction (AMI). Results provide insight into molecular mechanisms associated with AMI.
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Project description:Despite a substantial progress in diagnosis and therapy, acute myocardial infarction (MI) is a major cause of mortality in the general population. A novel insight into the pathophysiology of myocardial infarction obtained by studying gene expression should help to discover novel biomarkers of MI and to suggest novel strategies of therapy. The aim of our study was to establish gene expression patterns in leukocytes from acute myocardial infarction patients. ST-segment elevation myocardial infarction alters expression of several groups of genes. On admission, several genes and pathways that could be directly or indirectly linked with lipid/glucose metabolism, platelet function and atherosclerotic plaque stability were affected (signaling of PPAR, IL-10, IL-6). Analysis at discharge highlighted specific immune response (upregulation of immunoglobulins). Highly significant and substantial upregulation of SOCS3 and FAM20 genes expression in the first 4-6 days of myocardial infarction in all patients is the most robust observation of our work Twenty-eight patients with ST-segment elevation myocardial infarction (STEMI) were included. The blood was collected on the 1st day of myocardial infarction, after 4-6 days, and after 6 months. Control group comprised 14 patients with stable coronary artery disease (CAD), without history of myocardial infarction. Gene expression analysis was performed with Affymetrix GeneChipM-BM-. Human Gene 1.0 ST microarrays and GCS3000 TG system.
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2018-10-26 | GSE121779 | GEO
Project description:Despite a substantial progress in diagnosis and therapy, acute myocardial infarction (MI) is a major cause of mortality in the general population. A novel insight into the pathophysiology of myocardial infarction obtained by studying gene expression should help to discover novel biomarkers of MI and to suggest novel strategies of therapy. The aim of our study was to establish gene expression patterns in leukocytes from acute myocardial infarction patients. ST-segment elevation myocardial infarction alters expression of several groups of genes. On admission, several genes and pathways that could be directly or indirectly linked with lipid/glucose metabolism, platelet function and atherosclerotic plaque stability were affected (signaling of PPAR, IL-10, IL-6). Analysis at discharge highlighted specific immune response (upregulation of immunoglobulins). Highly significant and substantial upregulation of SOCS3 and FAM20 genes expression in the first 4-6 days of myocardial infarction in all patients is the most robust observation of our work
2014-10-23 | GSE62646 | GEO
Project description:The genes had different expression between healthy people and acute myocardial infarction.We aimed to identify the differentially expressed genes involved in acute myocardial infarction in Northeast Chinese Han people. We used microarrays to detail the global programme of gene expression to identify the differentially gene between the patients with acute myocardial infarction and healthy people in Northeast Chinese Han people
2017-04-04 | GSE97320 | GEO
Project description:Acute myocardial infarction (AMI) is primarily due to coronary atherosclerotic plaque rupture and subsequent thrombus formation. Platelets play a key role in the genesis and progression of both atherosclerosis and thrombosis. Since platelets are anuclear cells that inherit their mRNA from megakaryocyte precursors and maintain it unchanged during their life span, gene expression (GE) profiling at the time of an AMI provides information concerning the platelet GE preceding the coronary event. In ST-segment elevation myocardial infarction (STEMI), a gene-by-gene analysis of the platelet GE identified five differentially expressed genes (DEGs): FKBP5, S100P, SAMSN1, CLEC4E and S100A12. The logistic regression model used to combine the GE in a STEMI vs healthy donors score showed an AUC of 0.95. The same five DEGs were externally validated using platelet GE data from patients with coronary atherosclerosis but without thrombosis. Early signals of an imminent AMI are likely to be found by platelet GE profiling before the infarction occurs.
2019-12-31 | GSE109048 | GEO
Project description:Despite the significant reduction in the overall burden of cardiovascular disease (CVD) over the past decade, CVD still accounts for a third of all deaths in the United States and worldwide each year. While efforts to identify and reduce risk factors for atherosclerotic heart disease (i.e. hypertension, dyslipidemia, diabetes mellitus, cigarette smoking, inactivity) remain the focus of primary prevention, the inability to accurately and temporally predict acute myocardial infarction (AMI) impairs our ability to further improve patient outcomes. Our diagnostic evaluation for the presence of coronary artery disease relies on functional testing, which detects flow-limiting coronary stenosis, but we have known for decades that most lesions underlying AMI are only of mild to moderate luminal narrowings, not obstructing coronary blood flow. Accordingly, there is a dire need of improved diagnostics for underlying arterial plaque dynamics, fissure and rupture. Here we describe the designation of a specific gene expression pattern acting as a molecular signature for acute myocardial infarction present in whole blood of patients that was determined using microarray analysis of enriched circulating endothelial cells (CEC). We isolated circulating endothelial cells from patients experience acute myocardial infartion and healthy cohorts, and measured gene expression using the HG-133U_PLUS_2 microarray Circulating endothelial cells were isolated from patients experiencing acute myocardial infarction (n=49) and from healthy cohorts (n=50). The patients were separated into a discovery cohort (n=43) for biomarker discovery and model training; and into a validation cohort (n=56) for biomarker validation and model testing.
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