Transient ischemic attacks characterized by RNA profiles in blood

Abstract

Objective: Transient ischemic attacks (TIA) are common. Though systemic inflammation and thrombosis are associated with TIA, further study may provide insight into TIA pathophysiology and possibly lead to the development of treatments specifically targeted to TIA. We sought to determine whether gene expression profiles in blood could better characterize the proinflammatory and procoagulant states in TIA patients.

Methods: RNA expression in blood of TIA patients (n = 26) was compared to vascular risk factor control subjects without symptomatic cardiovascular disease (n = 26) using Affymetrix U133 Plus 2.0 microarrays. Differentially expressed genes in TIA were identified by analysis of covariance and evaluated with cross-validation and functional analyses.

Results: Patients with TIA had different patterns of gene expression compared to controls. There were 480 probe sets, corresponding to 449 genes, differentially expressed between TIA and controls (false discovery rate correction for multiple comparisons, p ≤ 0.05, absolute fold change ≥1.2). These genes were associated with systemic inflammation, platelet activation, and prothrombin activation. Hierarchical cluster analysis of the identified genes suggested the presence of 2 patterns of RNA expression in patients with TIA. Prediction analysis identified a set of 34 genes that discriminated TIA from controls with 100% sensitivity and 100% specificity.

Conclusion: Patients with recent TIA have differences of gene expression in blood compared to controls. The 2 gene expression profiles associated with TIA suggests heterogeneous responses between subjects with TIA that may provide insight into cause, risk of stroke, and other TIA pathophysiology.

Figures

Figure 1. Hierarchical cluster analysis of identified genes in transient ischemic attack (TIA) vs matched vascular risk factor controls

Hierarchical cluster analysis of 480 gene probe sets differentially expressed in blood between patients with TIA and control subjects (false discovery rate ≤0.05, absolute fold change >1.2). Each column on the x-axis represents 1 patient, with 26 patients with TIA (blue) and 26 controls (orange). Each row on the y-axis represents individual probe sets (usually for individual genes). TIAs cluster separately from controls as indicated by the green arrow (top). Within subjects with TIA, at least 2 clusters are apparent as indicated by the red arrow. These 2 TIA clusters are labeled TIA1 and TIA2. One patient with TIA (ID: IT-062) clustered with controls. Two controls (ID: IT-155 and IT-177) clustered with TIAs. Diagnosis = blue (TIA) and orange (controls). Green = downregulation; ID = subject ID; Red = upregulation.

Figure 2. Predicted probability of transient ischemic attack (TIA) or control diagnosis based on a linear discriminant analysis (LDA) model

The LDA model was used to derive the 34 genes that optimally distinguished TIA from controls. Probabilities are based on 10-fold leave-one-out cross-validation analysis. The probability of predicted diagnosis is shown on the y-axis, and subjects are shown on the x-axis. Patients with TIA are shown on the right, and control subjects on the left. The predicted probability of TIA is shown in red, and the predicted probability of control is shown in blue. Patients with TIA could be distinguished from controls with 100% sensitivity and 100% specificity.