A whole blood gene expression-based signature for smoking status
© Beineke et al.; licensee BioMed Central Ltd. 2012
Received: 1 June 2012
Accepted: 27 November 2012
Published: 3 December 2012
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.
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.
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).
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.
KeywordsSmoking Gene expression Coronary artery disease Whole blood
Tobacco use results in over 5 million deaths on an annual basis and is the leading cause of preventable death worldwide . Exposure to tobacco smoke, by both active and passive means, contributes to the increased risk and development of numerous diseases, including asthma, chronic obstructive pulmonary disease (COPD), and several types of cancer . A strong association also exists between smoking and cardiovascular disease; up to an 80% increased risk is observed for active smokers and 30% for passive smokers . Acute coronary syndromes (ACS), stable angina, stroke, carotid and peripheral atherosclerosis are all increased in smokers . Driving increased coronary disease risk are physiological changes that occur in response to smoking, including lipid oxidation/modification, vasomotor dysfunction, and inflammation .
Changes in gene expression in peripheral blood cells correlate with a number of systemic inflammatory and immune-related disorders, including cardiovascular disease [4–8]. We have recently described the development and validation of a peripheral blood gene expression score (GES) for the assessment of the likelihood of obstructive CAD in non-diabetic patients [7, 9]. The GES is derived from the expression levels of 23 genes as well as age and sex; the genes are grouped into highly correlated meta-genes which reflect both biological processes and cell type prevalence [7, 9]. The GES is related to the current likelihood of obstructive CAD [7, 9].
To better understand the physiological alterations induced by smoking and their relation to the development of CAD, we sought to identify changes in whole blood gene expression that correlate with self-reported smoking status. Herein we describe a set of genes expressed in whole blood that are strongly affected by smoking, and the development of a gene expression signature that is predictive of self-reported smoking status.
Patient selection and clinical methods
All patients were clinically referred for invasive angiography; angiograms were performed based on local, institutional protocols. The microarray cohort of 210 subjects (110 case:control pairs, matched for age and sex) and the qRT-PCR algorithm development and validation cohorts (1,071, 180 respectively) were part of PREDICT, a multi-center US study of patients referred for coronary angiography (http://www.clinicaltrials.gov, NCT00500617). The institutional review boards at all centers approved the study, and all patients gave written informed consent. Quantitative coronary angiography (QCA) was performed on all subjects as previously described .
Blood collection and RNA purification
Whole blood samples were collected in PAXgene® and EDTA tubes prior to coronary angiography. PAXgene® tubes were processed according to the manufacturer’s instructions, then frozen at −20°C. RNA was purified as previously described, using the Agencourt RNAdvance system  Plasma was isolated from EDTA tubes by centrifugation at 1800 g for 10 min, followed by the removal of the upper plasma layer and subsequent storage at −80°C.
Microarray samples were labeled and hybridized to 41K Human Whole Genome Arrays (Agilent, PN #G4112A) using the manufacturer’s protocol. Microarray data sets have been deposited in GEO (GSE 20686). Agilent processed signal values for array normalization were scaled to a trimmed mean of 100 and then log2 transformed. Standard array QC metrics (percent present, pair-wise correlation, and signal intensity) were used for quality assessment. Quantile normalization was subsequently used to further normalize the data .
To identify genes associated with smoking status, logistic regression was performed, adjusting for age and sex. Gene Set Enrichment Analysis (GSEA) was performed with 4 different gene sets (curated gene sets = 3272 sets; motif gene sets = 836 sets; computational gene sets = 881 sets; GO gene sets = 1454 sets) using 1000 permutations13; BINGO was used to assess enrichment of gene ontology terms in the set of 4214 significant array genes; a hypergeometric test was used to identify overrepresented terms and results were corrected for multiple testing using Benjamini & Hochberg False Discovery Rate (FDR) . Hierarchical clustering was performed using Gene Cluster 3.0 using mean-centered expression data in a complete linkage, correlation-based approach ; clusters were visualized using Java Treeview . The cell-type specificity of gene expression was evaluated using whole-blood normalized expression values derived from BioGPS .
Genes for qRT-PCR were selected from the microarray data based on statistical significance, gene ontology pathway analysis, and literature support.
Amplicon design and cDNA synthesis were performed as previously described [7, 8] qRT-PCR was performed on the Biomark microfluidic platform (Fluidigm, South San Francisco, CA). Prior to PCR, 2.5ul of cDNA was pre-amplified for 18 cycles using TaqMan® PreAmp Master Mix (Life Technologies, Carlsbad, CA) in a 10 ul reaction volume. PCR reactions were run in duplicate on Fluidigm 96X96 microfluidic gene expression chips, and median Cp values used for analysis.
Clinical/demographic factors were assessed for self-reported smoking status association using univariate logistic regression. Gene expression association with smoking status was assessed by logistic regression (sex/age adjusted). All statistical methods were performed using either the R software package, v. 2.09 or Minitab, v. 15.1.3.
Algorithm development and validation
Expression values for the 256 qRT-PCR genes were normalized to the mean of ACLY and TFCP2, two low-variability genes whose expression levels had previously been observed to correlate with laboratory processing effects. In a given sample, expression values for genes were truncated if values exceeded the 0.01 and 0.99 quantile. A predictive model was fit and cross-validated (10 fold, 1000 iterations) via forward stepwise logistic regression. Candidate predictors included all genes and also patient age and sex. The binary response variable (current/recent smokers vs. former and non-smokers) and 0.5 probability cut-point were prospectively defined for the analysis of the validation set. The formula for the GES algorithm is: (pr(Smoker)/(1-Pr(Smoker)) = 15.78306 + 0.3876 * SEX – 3.3368 * CLDND1-3.4034*LRRN3-1.4847 * MUC1 + 5.9209 * GOPC + 2.27166 * LEF1 where SEX =1 if male, 0 if female.
Plasma cotinine levels were measured in 180 PREDICT subjects using a commercially available ELISA assay (Calbiotech, Spring Valley, CA), following the manufacturer's recommended procedure.
Microarray identification of genes responsive to smoking
Clinical demographics of microarray subjects
(N = 100)
(N = 64)
(N = 4)
(N = 41)
Validation of array genes responsive to smoking by qRT-PCR
Gene expression model development and validation
Performance of GES and cotinine models
GES – Development Set
GES – Validation Set
Cotinine – Validation Set
Comparison of gene expression model performance to cotinine
This study presents gene discovery from microarrays and the development and validation from a large qRT-PCR data set of a whole blood-derived, qRT-PCR based gene expression score for the assessment of smoking status. The initial microarray analysis identified 4214 genes associated with self-reported smoking status. A number of biological pathways known to be affected by smoking showed GO enrichment within this set of genes, including apoptosis and cellular death, immune system development, leukocyte activation, hemopoiesis, stress response, and alterations in platelet activity (Additional file 1: Table S3) . When clustered, the most significant array genes partitioned into two main groups, which appeared to be partially driven by cell-type expression (Figure 3); notably most of the down-regulated genes appeared to be predominantly expressed in CD71+ and CD105+ cell types (Additional file 1: Table S4).
The majority of the genes selected to be analyzed by qRT-PCR (53%) showed a significant association with smoking. Expression levels of the most significant genes (e.g. LRRN3, CLDND1) were roughly equivalent in former smokers and subjects that had never smoked; likewise recently quit smokers appeared more like current smokers (Figure 3). In former smokers gene expression decreased with time elapsed since smoking cessation, however it did not reach non-smoker levels, suggesting that although the impact of smoking on gene expression diminishes over time, it may never be completed resolved (Figure 3). Alternatively, there may be a genetic effect on gene expression levels for genes that are associated with the ability to stop smoking. Prospective studies would be required to specifically dissociate these two possibilities.
The performance of the gene expression model remained fairly consistent across both the development set and validation sets, with a lower AUC seen in the validation set (Table 2). In both sets of subjects the expression model showed higher specificity and negative predictive value (NPV) versus sensitivity and positive predictive value (PPV). The use of cotinine levels to classify subjects provided a better AUC (Table 2), showing moderate concordance with the gene expression model (91% agreement, 95% CI 85.97-94.83; kappa = 0.53, p < 0.001, Figure 4). Interestingly, both methods produced independent sets of false positives (4 subjects by cotinine, 9 by GES; top left and bottom right quadrants, Figure 4). Levels of cotinine are elevated in passive smokers, and it is likely that gene expression may also be sensitive to second-hand smoke or other environmental factors [16, 17].
This study had a number of limitations. Self-reported smoking status is an imperfect gold-standard as subjects may not report their status correctly. The number of subjects in certain groups (e.g. recently quit) was limiting; larger numbers might have allowed for identification of better classifiers. A strong CD105+/CD71+ signature was seen in the microarray data, and although genes associated with this array signature were assessed by qRT-PCR (e.g. C5orf4), they were not chosen during model development; it is possible that other candidates from this group could add to algorithm performance. Clinical data relating to some aspects of smoking status was limited; lack of details regarding packs per day or date of smoking cessation prevented identification of subtler changes in gene expression in response to smoking, and lack of data for second-hand smoke exposure prevented assessment of this contribution to changes in gene expression. Finally, this study was not designed to assess whether the observed changes in gene expression were a result of direct exposure of circulating cells to toxins, or due to interactions with damaged lung tissue.
A GES for the determination of smoking status has limited clinical value per se, as self-reported smoking status is fairly reliable. One of the main goals of this study was to identify gene expression changes that correlate with smoking in the hope of understanding the underlying biology of smoking-related diseases. This has been previously done by examining changes in the expression levels of individual genes; the development of a GES however allows for easier comparison to other methods (e.g. cotinine), providing an assessment of the accuracy of gene expression as a marker for smoking [18, 19]. In addition, a GES also provides an avenue to assess expression changes in other pulmonary disease cohorts in relation to what is observed with smoking, and may also be useful in examining populations exposed to airborne pollutants.
The biology associated with the genes in the final expression model is intriguing. LRRN3 which encodes a leucine-rich repeat protein, and CLDN1, a claudin-domain containing gene, are both highly expressed in lymphocytes and were previously identified by Charlesworth et al. who used microarrays to examine changes in lymphocyte gene expression in response to smoking . Interestingly, CLDND1 is also up-regulated in lung squamous cell carcinomas . MUC1 encodes a membrane-bound protein that is a member of the mucin family; increases in MUC1 protein levels are associated with poor prognosis of non–small cell lung cancer . GOPC, a coiled-coil motif and PDZ containing protein, negatively regulates CFTR, mutations in which result in cystic fibrosis . Finally, LEF1 is a transcriptional enhancer also highly expressed in lymphocyte cells and is involved in the Wnt signaling pathway .
It is interesting to speculate on the relation between the observed changes in gene expression and the development of smoking-associated diseases. Expression levels of CLDND1 remain significantly associated with the presence of CAD in a multivariable model adjusting for smoking status as well as age and sex (unpublished observation); it remains to be determined whether these changes are causal or merely reflective. Likewise, changes in the expression levels of both CLDND1 and MUC1 are associated with the development of lung cancer; it would be interesting to assess the performance of the gene expression model in subjects with other smoking-related diseases such as lung cancer, asthma, and COPD. The validation set contained a number of subjects with false positive and false negative results assigned by both the gene expression model and cotinine; it would be interesting to study whether disease risk was altered in such patients.
Using microarray and qRT-PCR data sets, comprising over 1000 patients, we have investigated the relationship between peripheral blood cell gene expression and smoking status and derived a gene-expression based algorithm consisting of 5 genes which can accurately assign smoking status to patients. While others have reported the effect of smoking on gene expression in lymphocytes and monocyte-derived macrophages, to our knowledge the current study is the first to look at such changes in RNA isolated from whole blood and to derive a predictive GES [18, 19]. Further investigation into the biology behind the genes identified in this study may shed additional light on the relationship between smoking and increased cardiovascular disease risk, and assessment of the performance of the expression model in patients with other smoking-related disorders such as asthma, COPD, and lung cancer might lead to new diagnostic methods for these conditions.
Coronary artery disease
Chronic obstructive pulmonary disease
Acute coronary syndrome
Gene expression score
Area under the curve.
The authors gratefully acknowledge the contributions from the PREDICT Site Principal Investigators. In addition, we acknowledge all the patients who provided samples for the PREDICT study as well as the study site research coordinators and those who contributed to patient recruitment, clinical data acquisition and verification. The authors would also like to acknowledge M. Doctolero, R. Nuttall, D. Lee, P. Singh, and S. Htun for technical assistance in the laboratory.
- Mathers CD, Loncar D: Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 2006, 3 (11): e442-10.1371/journal.pmed.0030442.View ArticlePubMedPubMed CentralGoogle Scholar
- Fagerstrom K: The epidemiology of smoking: health consequences and benefits of cessation. Drugs. 2002, 62 (Suppl 2): 1-9.View ArticlePubMedGoogle Scholar
- Ambrose JA, Barua RS: The pathophysiology of cigarette smoking and cardiovascular disease: an update. J Am Coll Cardiol. 2004, 43 (10): 1731-1737. 10.1016/j.jacc.2003.12.047.View ArticlePubMedGoogle Scholar
- Baechler EC, Batliwalla FM, Karypis G, Gaffney PM, Ortmann WA, Espe KJ, Shark KB, Grande WJ, Hughes KM, Kapur V, et al: Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. Proc Natl Acad Sci U S A. 2003, 100 (5): 2610-2615. 10.1073/pnas.0337679100.View ArticlePubMedPubMed CentralGoogle Scholar
- Bennett L, Palucka AK, Arce E, Cantrell V, Borvak J, Banchereau J, Pascual V: Interferon and granulopoiesis signatures in systemic lupus erythematosus blood. J Exp Med. 2003, 197 (6): 711-723. 10.1084/jem.20021553.View ArticlePubMedPubMed CentralGoogle Scholar
- Deng MC, Eisen HJ, Mehra MR, Billingham M, Marboe CC, Berry G, Kobashigawa J, Johnson FL, Starling RC, Murali S, et al: Noninvasive discrimination of rejection in cardiac allograft recipients using gene expression profiling. Am J Transplant. 2006, 6 (1): 150-160. 10.1111/j.1600-6143.2005.01175.x.View ArticlePubMedGoogle Scholar
- Rosenberg S, Elashoff MR, Beineke P, Daniels SE, Wingrove JA, Tingley WG, Sager PT, Sehnert AJ, Yau M, Kraus WE, et al: Multicenter validation of the diagnostic accuracy of a blood-based gene expression test for assessing obstructive coronary artery disease in nondiabetic patients. Ann Intern Med. 2010, 153 (7): 425-434.View ArticlePubMedPubMed CentralGoogle Scholar
- Wingrove JA, Daniels SE, Sehnert AJ, Tingley W, Elashoff MR, Rosenberg S, Buellesfeld L, Grube E, Newby LK, Ginsburg GS, et al: Correlation of peripheral-blood gene expression with the extent of coronary artery stenosis. Circ Cardiovasc Genet. 2008, 1 (1): 31-38. 10.1161/CIRCGENETICS.108.782730.View ArticlePubMedGoogle Scholar
- Elashoff MR, Wingrove JA, Beineke P, Daniels SE, Tingley WG, Rosenberg S, Voros S, Kraus WE, Ginsburg GS, Schwartz RS, et al: Development of a blood-based gene expression algorithm for assessment of obstructive coronary artery disease in non-diabetic patients. BMC Med Genomics. 2011, 4 (1): 26-10.1186/1755-8794-4-26.View ArticlePubMedPubMed CentralGoogle Scholar
- Yang LH, Thorne NP: Normalization for Two-color cDNA microarray data. Statistics and Science: a Festschrift for Terry Speed, Volume 40. Edited by: Goldstein DR. 2003, Beachwood, OH: Institute of Mathematical Statistics, 403-418.View ArticleGoogle Scholar
- Maere S, Heymans K, Kuiper M: BiNGO: a cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 2005, 21 (16): 3448-3449. 10.1093/bioinformatics/bti551.View ArticlePubMedGoogle Scholar
- de Hoon MJ, Imoto S, Nolan J, Miyano S: Open source clustering software. Bioinformatics. 2004, 20 (9): 1453-1454. 10.1093/bioinformatics/bth078.View ArticlePubMedGoogle Scholar
- Saldanha AJ: Java treeview–extensible visualization of microarray data. Bioinformatics. 2004, 20 (17): 3246-3248. 10.1093/bioinformatics/bth349.View ArticlePubMedGoogle Scholar
- Wu C, Orozco C, Boyer J, Leglise M, Goodale J, Batalov S, Hodge CL, Haase J, Janes J, Huss JW, et al: BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome Biol. 2009, 10 (11): R130-10.1186/gb-2009-10-11-r130.View ArticlePubMedPubMed CentralGoogle Scholar
- Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005, 102 (43): 15545-15550. 10.1073/pnas.0506580102.View ArticlePubMedPubMed CentralGoogle Scholar
- Benowitz NL, Hukkanen J, Jacob P: 3rd: Nicotine chemistry, metabolism, kinetics and biomarkers. Handb Exp Pharmacol. 2009, 192: 29-60. 10.1007/978-3-540-69248-5_2.View ArticlePubMedGoogle Scholar
- Wang Z, Neuberg D, Su L, Kim JY, Chen JC, Christiani DC: Prospective study of metal fume-induced responses of global gene expression profiling in whole blood. Inhal Toxicol. 2008, 20 (14): 1233-1244. 10.1080/08958370802192874.View ArticlePubMedPubMed CentralGoogle Scholar
- Charlesworth JC, Curran JE, Johnson MP, Goring HH, Dyer TD, Diego VP, Kent JW, Mahaney MC, Almasy L, MacCluer JW, et al: Transcriptomic epidemiology of smoking: the effect of smoking on gene expression in lymphocytes. BMC Med Genomics. 2010, 3: 29-10.1186/1755-8794-3-29.View ArticlePubMedPubMed CentralGoogle Scholar
- Doyle I, Ratcliffe M, Walding A, Vanden Bon E, Dymond M, Tomlinson W, Tilley D, Shelton P, Dougall I: Differential gene expression analysis in human monocyte-derived macrophages: impact of cigarette smoke on host defence. Mol Immunol. 2010, 47 (5): 1058-1065. 10.1016/j.molimm.2009.11.008.View ArticlePubMedGoogle Scholar
- Liu Y, Sun W, Zhang K, Zheng H, Ma Y, Lin D, Zhang X, Feng L, Lei W, Zhang Z, et al: Identification of genes differentially expressed in human primary lung squamous cell carcinoma. Lung Cancer. 2007, 56 (3): 307-317. 10.1016/j.lungcan.2007.01.016.View ArticlePubMedGoogle Scholar
- Woenckhaus M, Merk J, Stoehr R, Schaeper F, Gaumann A, Wiebe K, Hartmann A, Hofstaedter F, Dietmaier W: Prognostic value of FHIT, CTNNB1, and MUC1 expression in non-small cell lung cancer. Hum Pathol. 2008, 39 (1): 126-136. 10.1016/j.humpath.2007.05.027.View ArticlePubMedGoogle Scholar
- Cheng J, Cebotaru V, Cebotaru L, Guggino WB: Syntaxin 6 and CAL mediate the degradation of the cystic fibrosis transmembrane conductance regulator. Mol Biol Cell. 2010, 21 (7): 1178-1187. 10.1091/mbc.E09-03-0229.View ArticlePubMedPubMed CentralGoogle Scholar
- Mao CD, Byers SW: Cell-context dependent TCF/LEF expression and function: alternative tales of repression, de-repression and activation potentials. Crit Rev Eukaryot Gene Expr. 2011, 21 (3): 207-236. 10.1615/CritRevEukarGeneExpr.v21.i3.10.View ArticlePubMedPubMed CentralGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/5/58/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.