the False Discovery Rate (FDR), and Gene Ontology (GO)[3] to classify the gene functional categories. We then verified whether the comparison between former (F) and never (N) smokers identified similar genes. We performed Gene Set Enrichment Analysis (GSEA)[4] to identify common gene patterns where the single-gene analysis revealed only few overlapping genes. We further explored whether the genes that differentiated lung tumors of smokers from never smokers (C/N and F/N) also differentiated early stage tumor tissue (T) from paired non-tumor (NT) tissue to confirm the role of these genes in smoking-related lung carcinogenesis. We finally explored the impact of the smoking signature on survival from lung cancer in smokers. We validated C/N genes by Real Time-PCR in 68 samples used for the present microarray analysis, and confirmed them in 40 independent samples from EAGLE and a Mayo Clinic study of lung cancer. Materials and Methods Study population and sample collection This study included 105 subjects from EAGLE, a large population-based study of lung cancer conducted in the Lombardy region of Italy. EAGLE lung cancer cases were enrolled from the following 13 hospitals: A.O. Ospedale Niguarda Ca’ Granda, Milano; A.O. Spedali Civili, Brescia; Istituto Clinico Humanitas, Rozzano (MI); Ospedale di Circolo e Fondazione Macchi, Varese; Fondazione IRCCS Ospedale Maggiore Policlinico, Mangiagalli and Regina Elena, Milano; Istituto Scientifico Universitario Ospedale San Raffaele, Milano; A.O. Ospedale Luigi Sacco, Milano; A.O. San Paolo, Milano; A.O. Ospedale San Carlo Borromeo, Milano; IRCCS Policlinico San Matteo, Pavia; A.O. San Gerardo, Monza; A.O. Ospedale Fatebenefratelli, Milano; Ospedale San Giuseppe, Milano. The healthy controls in EAGLE were randomly selected from the same residential area of the lung cancer cases. After description of the EAGLE study by the study personnel, and discussion with potential participants, written informed consent was obtained under a protocol approved by the Institutional Review Board of each participating hospital and by the National Cancer Institute (Bethesda, MD). Subjects in this gene expression study, 44–79 years old, had histologically confirmed primary adenocarcinoma of the lung, stages I–IV, and provided detailed smoking and medical history information. Overall, 180 adenocarcinoma and non-tumor tissue samples were selected for the analyses, including duplicate or triplicate samples from 14 subjects for quality control. Samples had been snap-frozen in liquid nitrogen within 20 minutes of surgical resection. A single pathologist confirmed the hospital-based diagnosis of adenocarcinoma, estimated the presence of malignant cells in each sample based on H&E-stained fresh frozen sections, and classified the samples as Tumor (T) and Non-Tumor (NT). From the original 180 samples, 148 provided sufficient quantity of high-quality RNA for microarray analyses; 13 additional samples were excluded because of technical problems. Normalization was conducted on the remaining 135 microarrays; corresponding CEL files and information conform to the MIAME guidelines are publicly available on the GEO database (accession num- ber = GSE10072). After normalization, 13 samples were excluded because of low percentage of tumor cells in the tumor tissues. This report is based on 122 samples, of which 15 duplicates/triplicates were averaged, resulting in 107 final expression values from 58 tumor and 49 non-tumor tissues from 20 never smokers, 26 former smokers, and 28 current smokers. Quality assurance and distribution of cell types across smoking groups are described in Appendix S1A, S1B, and S1C. Smoking Signature in Lung Statistical analysis All statistical analyses were accomplished using R program language. Gene expression data were processed and normalized using Bioconductor Affy package, based on the Robust Multichip Average (RMA) method[5] for single-channel Affymetrix chips. All 22,283 probe sets based on RMA summary measure were used in class comparison analyses. Average linkage hierarchical clustering of samples was based on one minus Pearson correlation as the dissimilarity metric. An ANOVA analysis adjusting for sex was used to test whether genes were differentially expressed between smoking groups (C/N and F/N), between tumor tissue and non-tumor tissue (T/NT), or by pack years of cigarette smoking. Further analyses adjusted by tumor grade or excluding 6 subjects with emphysema or chronic bronchitis or 3 subjects who received chemotherapy prior to the study were conducted, with essentially unaltered results. For analyses including paired tissues (T/NT tissue samples from the same subjects), a linear mixed effects model was used to account for intra-person correlation. To limit false positive findings, genes were considered statistically significant if their p-values were less than the stringent threshold of 0.001. Under the hypothesis of no difference in expression profiles, and considering the analysis of 22,283 probes, we expect that by chance the average number of false positive findings will be #23. We used the Benjamini-Hochberg[2] procedure to calculate the False Discovery Rate (FDR). We further restricted significant genes to those which showed at least 1.5 fold ratio of geometric means of expression between two groups. Gene selection based on p,0.001 (two-sided) and fold- change .1.5 are referred to as ‘‘stringent criteria’’. The Cox Proportional Hazards model[6] was used to estimate the effect of gene expression changes in C/N on survival from lung cancer in smokers. Of the 74 subjects included in this study (all stages), 34 (22 smokers) were alive, and 40 (32 smokers) were deceased as of May 2007. Among the deceased subjects, 36 died of lung cancer. The remaining 4 (2 smokers) died of other cancers and were censored at time of death in the analysis. The time from lung cancer to death or date of last follow-up was between 28 days and 5.0 years for the deceased subjects, and 3.7 and 5.7 years for the subjects alive in May 2007. The relative risk of gene expression was defined as the hazard ratio associated with one standard deviation change of the expression. Analyses were adjusted for stage, sex, and smoking. Age was similarly distributed across the groups and was not adjusted for in the analysis. Analysis of total plasma cotinine concentration by gas chromatography/mass spectrometry We verified the self-reported current smoking status by measuring plasma cotinine levels. The total cotinine (free plus cotinine N- glucuronide) concentration in plasma was quantified by GC/MS analysis using a method similar to that used for urinary cotinine,[7] with the addition of a solid phase extraction step carried out on an MCX column (Waters Corporation, Milford, MA). One individual who reported to have quit smoking 2.6 years before the study had high cotinine levels (135 ng/ml) and was re- classified as a current smoker. Gene Set Enrichment Analysis Gene Set Enrichment Analysis (GSEA)[4] was used to compare expression in groups of genes (gene-sets), between different tissues or between different comparison groups within the same tissue. GSEA analysis reveals a pattern of common gene-sets even when single-gene analysis reveals very few overlapping genes between groups. We PLoS ONE | www.plosone.org 2 February 2008 | Volume 3 | Issue 2 | e1651