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BAS/BSCR poster abstract
BAS/BSCR22 Time series analysis of acute coronary syndrome from peripheral whole blood using Affymetrix GeneChip arrays
  1. S Craig1,2,
  2. A C Morton1,
  3. J Arnold1,
  4. D C Crossman1,2,
  5. M Milo1,2
  1. 1NIHR Cardiovascular Biomedical Research Unit, Northern General Hospital, Sheffield, UK
  2. 2School of Medicine and Biomedical Sciences, University of Sheffield, Sheffield, UK

Abstract

Introduction Acute coronary syndrome (ACS) is the cause of over 114 000 UK hospital admissions1 and a cost to the NHS of over £3.9 billion every year.2 Advances in microarray technology allow a detailed understanding of genome-wide expression profiles of pathological processes. We hypothesised that analysis of ACS, at the time of an acute event and throughout recovery, would provide insight into pathology, as well as identify genes as potential drug targets and both diagnostic and prognostic markers.

Methods 50 patients presenting with chest pain consistent with ACS were recruited within 48 h of admission. 3 ml of peripheral whole blood was collected using Tempus RNA tubes at days 1, 3, 7, 30 and 90. Total RNA was extracted, cleared of globin mRNA and arrayed using Affymetrix HG_U133 plusv.2 GeneChips. Data were analysed using open source software PUMA.

Results We used principal component analysis (PCA) to visualise the data. With clinical information incorporated, it was found that the data discriminated between patients, putting them into troponin-positive and troponin-negative groups across all time points. Hierarchical clustering, comparing the expression profiles between groups, identified different clusters of genes that increased in expression over time in the troponin-positive group. Pathway analysis of the clusters showed overexpression of Rho GTPase cytoskeletal, endothelin signalling, integrin signalling, G-protein signalling and inflammation-mediated pathways.

Conclusions Microarray analysis identified expression differences between troponin-positive and troponin-negative patients over time. Specific biological pathways possibly showing the late effects of acute events can be used to discover biomarkers of coronary heart disease.

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