BMC Medical Genomics (Mar 2011)

Development of a blood-based gene expression algorithm for assessment of obstructive coronary artery disease in non-diabetic patients

  • Ellis Stephen G,
  • Schwartz Robert S,
  • Ginsburg Geoffrey S,
  • Kraus William E,
  • Voros Szilard,
  • Rosenberg Steven,
  • Tingley Whittemore G,
  • Daniels Susan E,
  • Beineke Philip,
  • Wingrove James A,
  • Elashoff Michael R,
  • Tahirkheli Naheem,
  • Waksman Ron,
  • McPherson John,
  • Lansky Alexandra J,
  • Topol Eric J

DOI
https://doi.org/10.1186/1755-8794-4-26
Journal volume & issue
Vol. 4, no. 1
p. 26

Abstract

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Abstract Background Alterations in gene expression in peripheral blood cells have been shown to be sensitive to the presence and extent of coronary artery disease (CAD). A non-invasive blood test that could reliably assess obstructive CAD likelihood would have diagnostic utility. Results Microarray analysis of RNA samples from a 195 patient Duke CATHGEN registry case:control cohort yielded 2,438 genes with significant CAD association (p RT-PCR analysis of these 113 genes in a PREDICT cohort of 640 non-diabetic subject samples was used for algorithm development. Gene expression correlations identified clusters of CAD classifier genes which were reduced to meta-genes using LASSO. The final classifier for assessment of obstructive CAD was derived by Ridge Regression and contained sex-specific age functions and 6 meta-gene terms, comprising 23 genes. This algorithm showed a cross-validated estimated AUC = 0.77 (95% CI 0.73-0.81) in ROC analysis. Conclusions We have developed a whole blood classifier based on gene expression, age and sex for the assessment of obstructive CAD in non-diabetic patients from a combination of microarray and RT-PCR data derived from studies of patients clinically indicated for invasive angiography. Clinical trial registration information PREDICT, Personalized Risk Evaluation and Diagnosis in the Coronary Tree, http://www.clinicaltrials.gov, NCT00500617

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