Diagnostic potential of plasmatic MicroRNA signatures in stable and unstable angina

Abstract

Purpose: We examined circulating miRNA expression profiles in plasma of patients with coronary artery disease (CAD) vs. matched controls, with the aim of identifying novel discriminating biomarkers of Stable (SA) and Unstable (UA) angina.

Methods: An exploratory analysis of plasmatic expression profile of 367 miRNAs was conducted in a group of SA and UA patients and control donors, using TaqMan microRNA Arrays. Screening confirmation and expression analysis were performed by qRT-PCR: all miRNAs found dysregulated were examined in the plasma of troponin-negative UA (n=19) and SA (n=34) patients and control subjects (n=20), matched for sex, age, and cardiovascular risk factors. In addition, the expression of 14 known CAD-associated miRNAs was also investigated.

Results: Out of 178 miRNAs consistently detected in plasma samples, 3 showed positive modulation by CAD when compared to controls: miR-337-5p, miR-433, and miR-485-3p. Further, miR-1, -122, -126, -133a, -133b, and miR-199a were positively modulated in both UA and SA patients, while miR-337-5p and miR-145 showed a positive modulation only in SA or UA patients, respectively. ROC curve analyses showed a good diagnostic potential (AUC ≥ 0.85) for miR-1, -126, and -483-5p in SA and for miR-1, -126, and -133a in UA patients vs. controls, respectively. No discriminating AUC values were observed comparing SA vs. UA patients. Hierarchical cluster analysis showed that the combination of miR-1, -133a, and -126 in UA and of miR-1, -126, and -485-3p in SA correctly classified patients vs. controls with an efficiency ≥ 87%. No combination of miRNAs was able to reliably discriminate patients with UA from patients with SA.

Conclusions: This work showed that specific plasmatic miRNA signatures have the potential to accurately discriminate patients with angiographically documented CAD from matched controls. We failed to identify a plasmatic miRNA expression pattern capable to differentiate SA from UA patients.

Figure 1. Plasmatic miRNAs regulation by CAD in Stable and Unstable Angina patients.

Panels A to L. All investigated miRNAs showed increased expression in CAD subjects, both in SA (yellow bars, 5.7–42.4 fold increase with the exception of miR-145, not regulated) and UA (blue bars, 2.9–29.7 fold increase with the exception of miR-337-5p, not regulated) patients. Values indicate fold changes (expressed as mean ± SEM) of each miRNA vs. its level in control healthy subjects (CTRLS, red bars), arbitrarily set to 1. (*) indicates p≤0.05 vs. control; NS=not significant. Differences among groups were compared using either One-way ANOVA or non-parametric Kruskal-Wallis with Dunn’s post-hoc test, when appropriate.

Figure 2. ROC curve analysis of CAD-miRNAs in Stable Angina patients and control subjects.

The figure depicts calculated ROC curve and respective AUC values for miR-1, miR-126, and miR-485-3p, which exhibited good accuracy (AUC>0.85) in differentiating Stable Angina (SA) patients from matched controls (C).

Figure 3. ROC curve analysis of CAD-miRNAs in Unstable Angina patients and control subjects.

The figure depicts calculated ROC curve and respective AUC values for miR-1, miR-126, and miR-133a, which exhibited good accuracy (AUC>0.85) in differentiating Unstable Angina (UA) patients from matched controls (C).

Figure 4. ROC curve analysis of CAD-miRNAs in Stable and Unstable Angina patients.

None of the investigated miRNAs exhibited adequate accuracy in differentiating Stable (SA) from Unstable Angina (UA) patients: AUC values ranged between 0.404 (miR-145) and 0.678 (miR-337-5p).

Figure 5. miRNA clusters efficiently differentiate SA and UA patients from Controls.

Hierarchical clustering demonstrated that different miRNA “signatures” efficiently classify between matched controls (CTRLS) and CAD patients. The cluster composed by miR-1 and miR-126 and miR-485-3p can be used to correctly classify SA patients from controls with 90.2% (yellow bar) efficiency. Similarly the miR-1, miR-126 and miR-133a cluster can be used to correctly classify UA patients from controls with 87.2% (red bar) efficiency. No signatures of miRNAs could be found to efficiently discriminate SA from UA patients with an accuracy > 66% (miR-126 and miR-337-5p cluster, blue bar).