A whole blood gene expression-based signature for smoking status

Abstract

Background: Smoking is the leading cause of preventable death worldwide and has been shown to increase the risk of multiple diseases including coronary artery disease (CAD). We sought to identify genes whose levels of expression in whole blood correlate with self-reported smoking status.

Methods: Microarrays were used to identify gene expression changes in whole blood which correlated with self-reported smoking status; a set of significant genes from the microarray analysis were validated by qRT-PCR in an independent set of subjects. Stepwise forward logistic regression was performed using the qRT-PCR data to create a predictive model whose performance was validated in an independent set of subjects and compared to cotinine, a nicotine metabolite.

Results: Microarray analysis of whole blood RNA from 209 PREDICT subjects (41 current smokers, 4 quit ≤ 2 months, 64 quit > 2 months, 100 never smoked; NCT00500617) identified 4214 genes significantly correlated with self-reported smoking status. qRT-PCR was performed on 1,071 PREDICT subjects across 256 microarray genes significantly correlated with smoking or CAD. A five gene (CLDND1, LRRN3, MUC1, GOPC, LEF1) predictive model, derived from the qRT-PCR data using stepwise forward logistic regression, had a cross-validated mean AUC of 0.93 (sensitivity=0.78; specificity=0.95), and was validated using 180 independent PREDICT subjects (AUC=0.82, CI 0.69-0.94; sensitivity=0.63; specificity=0.94). Plasma from the 180 validation subjects was used to assess levels of cotinine; a model using a threshold of 10 ng/ml cotinine resulted in an AUC of 0.89 (CI 0.81-0.97; sensitivity=0.81; specificity=0.97; kappa with expression model = 0.53).

Conclusion: We have constructed and validated a whole blood gene expression score for the evaluation of smoking status, demonstrating that clinical and environmental factors contributing to cardiovascular disease risk can be assessed by gene expression.

Figures

Figure 1

Gene ontology analysis of 4214 array genes associated with smoking. The 4214 smoking-associated genes were analyzed using BINGO to identify significant biological processes. Significant processes (p < 0.001 after FDR correction) are colored with the gradient of p values reflected in the colors as indicated, and the biological process annotated. (A) Cellular component ontological terms (B) Biological Process ontological terms.

Figure 2

Hierarchical clustering of 209 subjects and 227 array genes associated with smoking (p < 0.001). The dendogram on top shows correlations between subjects; black bars at bottom denote current smokers; red bars denote recently quit smokers. Dendogram on the left shows correlations between genes; positions of representative cell-specific genes are shown on the right.

Figure 3

Expression levels of four most significant genes as assessed by qRT-PCR across 1074 PREDICT subjects grouped by self-reported smoking status. Expression levels are shown in Cp units on the Y axis, self-reported smoking status is shown on the X axis. (A) LRRN3; (B) CLDND1; (C) SASH1; (D) P2RY6.

Figure 4

Comparison of gene expression score to cotinine levels in validation set. The y-axis shows the log10 value of cotinine levels in the 180 subject validation set; the horizontal dashed line (−−-) denotes the 10ng/ml threshold used in the AUC analysis. The x-axis shows the GES in the 180 subject validation set; the vertical dashed line denotes the 50% probability threshold used in the AUC analysis. Black circles = non-smokers; red circles = former smokers (> 2 months quit); green circles = recently quit smokers (< 2 months quit); blue circles = current smokers. All smoking categories are self-reported.