Application of Proteomics Profiling for Biomarker Discovery in Hypertrophic Cardiomyopathy

Abstract

High-throughput proteomics profiling has never been applied to discover biomarkers in patients with hypertrophic cardiomyopathy (HCM). The objective was to identify plasma protein biomarkers that can distinguish HCM from controls. We performed a case-control study of patients with HCM (n = 15) and controls (n = 22). We carried out plasma proteomics profiling of 1129 proteins using the SOMAscan assay. We used the sparse partial least squares discriminant analysis to identify 50 most discriminant proteins. We also determined the area under the curve (AUC) of the receiver operating characteristic curve using the Monte Carlo cross validation with balanced subsampling. The average AUC was 0.94 (95% confidence interval, 0.82-1.00) and the discriminative accuracy was 89%. In HCM, 13 out of the 50 proteins correlated with troponin I and 12 with New York Heart Association class. Proteomics profiling can be used to elucidate protein biomarkers that distinguish HCM from controls.

Keywords: Biomarker discovery; Hypertrophic cardiomyopathy; Proteomics.

Fig. 1

Two-dimensional score plot using the sparse partial least squares discriminant analysis model. Green circles represent the proteomic profile of HCM cases, and red circles are that of controls. HCM hypertrophic cardiomyopathy, sPLS-DA sparse partial least squares discriminant analysis

Fig. 2

The 50 most discriminant proteins to distinguish hypertrophic cardiomyopathy cases from controls, identified with the sparse partial least squares discriminant analysis. Red box on the right indicates that the protein concentration was increased in HCM, and green box means that was decreased in HCM. P values were computed with the Mann Whitney-Wilcoxon test. Fold change was calculated by dividing the median in case by the median in control. The blue bars indicate importance of each protein to discriminate HCM cases from controls, which was determined by the contribution to the discriminative model. HCM hypertrophic cardiomyopathy, MAPK mitogen-activated protein kinase, NEGF neurite growth-promoting factor

Fig. 3

Area under the receiver operating characteristic curve using 5-protein discrimination model. AUC area under the curve, CI confidence interval

Fig. 4

Discriminative accuracy using different number of proteins in the discrimination model. Numbers on the x-axis represents the number of proteins used in each model