A Whole Blood Molecular Signature for Acute Myocardial Infarction

Abstract

Chest pain is a leading reason patients seek medical evaluation. While assays to detect myocyte death are used to diagnose a heart attack (acute myocardial infarction, AMI), there is no biomarker to indicate an impending cardiac event. Transcriptional patterns present in circulating endothelial cells (CEC) may provide a window into the plaque rupture process and identify a proximal biomarker for AMI. Thus, we aimed to identify a transcriptomic signature of AMI present in whole blood, but derived from CECs. Candidate genes indicative of AMI were nominated from microarray of enriched CEC samples, and then verified for detectability and predictive potential via qPCR in whole blood. This signature was validated in an independent cohort. Our findings suggest that a whole blood CEC-derived molecular signature identifies patients with AMI and sets the framework to potentially identify the earlier stages of an impending cardiac event when used in concert with clinical history and other diagnostics where conventional biomarkers indicative of myonecrosis remain undetected.

Figure 1

Enumeration of CECs in Patients with AMI. Circulating endothelial cells (CEC) are elevated in the setting of acute myocardial infarction (AMI). CD146 + CECs immuno-magnetically separated from whole blood are increased in patients during AMI (n = 28) as compared to healthy controls (n = 28). *p < 0.0002, non-parametric Mann-Whitney two-tailed t-test.

Figure 2

Microarray Analysis of Enriched CECs. An 11-gene signature for AMI was determined from microarray gene expression analysis of enriched CECs from healthy control and AMI patients. (A,B) Heat maps for the 11 genes in the microarray of the (A) discovery cohort of healthy control (n = 22) and AMI patients (n = 21) and (B) replication cohort of healthy control (n = 25) and AMI patients (n = 23) found in the elastic net to discriminate AMI from control. Samples are ordered according to their predicted probability of being an AMI. Expression levels are represented from high (blue) to low (red). (C,D) ROC-curves for the 11-gene signature in the (C) discovery cohort with AUC of 1.0 (p = 1.90 × 10⁻¹² and (D) validation cohort with AUC of 0.99 (p = 7.78 × 10⁻¹³).

Figure 3

qPCR Analysis of Whole Blood. Candidate genes from enriched CEC microarray were assessed by qPCR in the whole blood of healthy control, stable diseased control, and two separate AMI patient groups. (A,B) Individual plots for each gene assessed by qPCR in (A) healthy controls (n = 29) vs AMI (n = 44) (cohort 1) and (B) diseased controls (n = 36) vs AMI (n = 45) (cohort 2). Specific gene counts normalized by GAPDH for each sample. (C) ROC-curve analysis for each model: solid black line, trained in cohort 1 and tested in cohort 1; dashed red line, trained in cohort 1 and tested in cohort 2. *p < 0.005, **p < 0.05, unpaired, two-tailed t-test. Models are evaluated using leave-one-out cross validation when using the same cohort for training and testing.