Whole blood gene expression analyses in patients with single versus recurrent venous thromboembolism

Abstract

Introduction: Venous thromboembolism may recur in up to 30% of patients with a spontaneous venous thromboembolism after a standard course of anticoagulation. Identification of patients at risk for recurrent venous thromboembolism would facilitate decisions concerning the duration of anticoagulant therapy.

Objectives: In this exploratory study, we investigated whether whole blood gene expression data could distinguish subjects with single venous thromboembolism from subjects with recurrent venous thromboembolism.

Methods: 40 adults with venous thromboembolism (23 with single event and 17 with recurrent events) on warfarin were recruited. Individuals with antiphospholipid syndrome or cancer were excluded. Plasma and serum samples were collected for biomarker testing, and PAXgene tubes were used to collect whole blood RNA samples.

Results: D-dimer levels were significantly higher in patients with recurrent venous thromboembolism, but P-selectin and thrombin-antithrombin complex levels were similar in the two groups. Comparison of gene expression data from the two groups provided us with a 50 gene probe model that distinguished these two groups with good receiver operating curve characteristics (AUC 0.75). This model includes genes involved in mRNA splicing and platelet aggregation. Pathway analysis between subjects with single and recurrent venous thromboembolism revealed that the Akt pathway was up-regulated in the recurrent venous thromboembolism group compared to the single venous thromboembolism group.

Conclusions: In this exploratory study, gene expression profiles of whole blood appear to be a useful strategy to distinguish subjects with single venous thromboembolism from those with recurrent venous thromboembolism. Prospective studies with additional patients are needed to validate these results.

Figure 1

Development of the gene model to distinguish subjects with a single VTE from those with recurrent VTE. (A) The heat map representing the expression of the 50 genes probes used to develop the metagenes model is shown with blue and red representing extremes of expression with visually apparent differences in gene expression. Samples from single VTE subjects are on the left (n=23), and samples from subjects with recurrent VTE are on the right (n=17). (B) Leave-one-out cross validation: a comparison of the individual and median (with interquartile range) estimated classification probability of recurrent VTE predicted by the 50 gene model is shown. (C) Leave-one-out cross validation: the ROC curve identifying the probability score of 0.63 as the optimal cut-point to be used to classify samples is shown (black solid line). The area under the curve (AUC) is 0.75. The ROC curves for the 10 random permutations are shown as dotted lines.

Figure 2

Akt pathway activities for the single and recurrent VTE subjects. The mean and standard error for each is shown.