Diagnosis of coronary heart diseases using gene expression profiling; stable coronary artery disease, cardiac ischemia with and without myocardial necrosis
Cardiovascular disease including coronary artery disease and myocardial infarction is one of the leading causes of death in Europe, and is influenced by both environmental and genetic factors. With the advancements in genomic tools and technologies there is potential to predict and diagnose heart disease using molecular data from analysis of blood cells. We analyzed gene expression data from blood samples taken from normal people (n=21), non-significant coronary artery disease (n=93), patients with unstable angina (n=16), stable coronary artery disease (n=14) and myocardial infarction (MI; n=207). We used a feature selection approach to identify a set of gene expression variables which successfully differentiate different cardiovascular diseases. The initial features were discovered by fitting a linear model for each probe set across all arrays of normal individuals and patients with myocardial infarction. Three different feature optimisation algorithms were devised which identified two most discriminating sets of genes one using MI and normal controls (total genes=8) and another one using MI and unstable angina patients (total genes=17). The results proved the diagnostic robustness of the final feature sets in discriminating not only patients with myocardial infraction from healthy controls but also from patients with clinical symptoms of cardiac ischemia with myocardial necrosis and stable coronary artery disease despite the influence of batch effects and different microarray gene chips and platforms.
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