Gene expression signature in peripheral blood detects thoracic aortic aneurysm

Abstract

Background: Thoracic aortic aneurysm (TAA) is usually asymptomatic and associated with high mortality. Adverse clinical outcome of TAA is preventable by elective surgical repair; however, identifying at-risk individuals is difficult. We hypothesized that gene expression patterns in peripheral blood cells may correlate with TAA disease status. Our goal was to identify a distinct gene expression signature in peripheral blood that may identify individuals at risk for TAA.

Methods and findings: Whole genome gene expression profiles from 94 peripheral blood samples (collected from 58 individuals with TAA and 36 controls) were analyzed. Significance Analysis of Microarray (SAM) identified potential signature genes characterizing TAA vs. normal, ascending vs. descending TAA, and sporadic vs. familial TAA. Using a training set containing 36 TAA patients and 25 controls, a 41-gene classification model was constructed for detecting TAA status and an overall accuracy of 78+/-6% was achieved. Testing this classifier on an independent validation set containing 22 TAA samples and 11 controls yielded an overall classification accuracy of 78%. These 41 classifier genes were further validated by TaqMan real-time PCR assays. Classification based on the TaqMan data replicated the microarray results and achieved 80% classification accuracy on the testing set.

Conclusions: This study identified informative gene expression signatures in peripheral blood cells that can characterize TAA status and subtypes of TAA. Moreover, a 41-gene classifier based on expression signature can identify TAA patients with high accuracy. The transcriptional programs in peripheral blood leading to the identification of these markers also provide insights into the mechanism of development of aortic aneurysms and highlight potential targets for therapeutic intervention. The classifier genes identified in this study, and validated by TaqMan real-time PCR, define a set of promising potential diagnostic markers, setting the stage for a blood-based gene expression test to facilitate early detection of TAA.

Figure 1. Hierarchical clustering of 61 whole blood samples analyzed by Applied Biosystem Expression Arrays using the 1199 differentially expressed genes determined by SAM analysis.

The level of expression of each gene in each sample, relative to the mean level of expression of that gene across all the samples, is represented using a red-black-green color scale as shown in the key (green: below mean; black: equal to mean; red: above mean). (A). Scaled down representation of the entire cluster of the 1199 signature genes and 61 whole blood samples. (B). Experimental dendrogram displaying the clustering of the samples into two main branches: the TAA branch (red) and the control branch (blue) with a few exceptions. (C). Gene expression pattern of representative genes within biological pathways that are statistically significantly over-represented (random overlapping p-value<0.05) by the up-regulated (red bars) or the down-regulated (blue bars) signature genes of TAA.

Figure 2. A set of 41 classifier genes were identified via 10-fold cross-validation on the 61-sample training set.

(A). Classification accuracy, sensitivity and specificity of the 41 classifier genes, error bar represents±1 standard deviation among 100 times of independent 10-fold cross-validation process; (B). 3D Plots of the first three principal components based on PCA analysis. The segregation between TAA and control samples is evident with only a few exceptions.

Figure 3. Validation of the 41-gene classification model using microarray analysis on an independent set of testing samples.

(A). Classification probabilities for each testing sample: TAA (case) vs. normal (control); (B). Contingency table summarizes the predicted and actual class membership for the testing set; (C). Classification accuracy, sensitivity and specificity.

Figure 4. Validation of the 41 classifier genes using TaqMan real-time PCR assays.

The expression profile of the 41 classifier genes was measured in each of the 82 samples by real-time PCR using TaqMan® Gene Expression Assays. Based on TaqMan assay data, the coefficient of the 41 classifier genes were re-learned from the 52 training samples and used to classify the status of 30 testing samples using the same method applied to microarray data. (A). Classification probabilities for each testing sample: TAA (case) vs. normal (control); (B). Contingency table summarizes the predicted and actual class membership for the testing set; (C). Classification accuracy, sensitivity and specificity.

Figure 5. Two-dimensional cluster diagrams.

(A).144 signature genes characterizing the ascending and descending TAA subtypes; (B). 113 signature genes characterizing the TAA with or without family history. Representative genes associated with over-represented molecular functions/biological processes/pathways are listed.