Abstract Background System toxicology aims at understanding the mechanisms used by biological systems to respond to toxicants. Such understanding can be leveraged to assess the risk of chemicals, drugs, and consumer products in living organisms. In system toxicology, machine learning techniques and methodologies are applied to develop prediction models for classification of toxicant exposure of biological systems. Gene expression data (RNA/DNA microarray) are often used to develop such prediction models. Results The outcome of the present work is an experimental methodology to develop prediction models, based on robust gene signatures, for the classification of cigarette smoke exposure and cessation in humans. It is a result of the participation in the recent sbv IMPROVER SysTox Computational Challenge. By merging different gene selection techniques, we obtain robust gene signatures and we investigate prediction capabilities of different off-the-shelf machine learning techniques, such as artificial neural networks, linear models and support vector machines. We also predict six novel genes in our signature, and firmly believe these genes have to be further investigated as biomarkers for tobacco smoking exposure. Conclusions The proposed methodology provides gene signatures with top-ranked performances in the prediction of the investigated classification methods, as well as new discoveries in genetic signatures for bio-markers of the smoke exposure of humans. Electronic supplementary material The online version of this article (10.1186/s12859-018-2035-3) contains supplementary material, which is available to authorized users. Keywords: Toxicology, Gene signature, Smoking, Supervised learning, Feature selection Background System toxicology aims at understanding mechanisms, both at functional and genetic structural level, by which biological systems respond to toxicants. Such understanding can be leveraged to assess the risk of chemicals, drugs, and consumer products on living organisms. In particular, the identification of effective genomic biomarkers to aid prediction of toxicant/drug exposure levels in biological systems is an emerging research topic in system toxicology. The increasing interest in this field is motivated by the wide applicability of genomic biomarkers for both finding evidence of toxicity in drug therapies and monitoring therapeutic outcomes. Furthermore, in case of acute poisoning, it can be used to detect exposure degree to toxicants/drugs. Indeed, the exposure level evaluation by safety biomarkers may lead to the development of more efficient diagnostic tools for toxicodynamic monitoring like in case of patients receiving immunosuppressive therapy [[29]1]. This research area is relevant in many different applications, as shown by the identification of genomic biomarkers for a wide variety of toxicants, including nephrotoxic agents [[30]2], testicular toxicants [[31]3], for keratinocyte proliferation in papilloma murine skin model [[32]4], and smoke exposure [[33]5–[34]7]. Several works propose the use of transcriptome-based exposure response signatures, computed by processing gene expression data (RNA/DNA microarray), to develop toxicant exposure prediction models [[35]8–[36]10]. In most of these approaches, gene signatures are identified by differential expression, using statistical tests involving case and control populations. Due to inter-individual variations present in human populations, observed gene sets could result in not-robust signatures. Indeed, robust signatures should maintain high specificity and sensitivity across independent subject cohorts, laboratories, and nucleic acid extraction methods. In the present work we propose a methodology, as well as an experimental pipeline, for finding gene signatures for tobacco smoke exposure characterization and prediction. Our approach integrates different gene selection mechanisms, whose results are studied and compared to extract gene signatures more robust than those produced by a single methodology. In particular, the considered gene selection methods are based on a regression method (LASSO-LARS), a recursive elimination by support vector machines (RFE-SVM), and a feature selection by an ensemble of decision trees (Extra-Trees). While recent works start employing machine learning techniques for gene selection [[37]11–[38]13], the novelty of this work is to employ and merge the results from different gene selection methods, which are not limited to statistical analysis ones. The sbv IMPROVER project [[39]14] is a collaborative effort led and funded by Philip Morris International Research and Development which focuses on the verification of methods and concepts in systems biology research within an industrial framework. sbv IMPROVER project has recently proposed the SysTox Computational Challenge [[40]15] aiming at exploiting crowdsourcing as a pertinent approach to identify and verify chemical cigarette smoking exposure response markers from human whole blood gene expression data. The aim is to leverage these markers as a signature in computational models for predictive classification of new blood samples as part of the smoking exposed or non-exposed groups (see Fig. [41]1). In this application domain we investigated our methodology for gene expression data processing and selection as a machine learning problem of feature selection/reduction in a data space with high dimensionality (in the order of thousands of variables). In this context, we demonstrate how the blood gene signatures we found with our methodology have large overlaps with those found by other related works. In addition we identified new genes which are not mentioned in literature as possible biomarkers for tobacco smoke exposure. The functional annotation and terms enrichment analysis, together with toxicogenomics analysis (chemical-gene-disease-pathway association studies), showed that the expression levels of these new genes are affected by smoke exposure. In addition, based on our signatures we obtained higher performances in terms of area under precision-recall curve (AUPR) and matthews correlation coefficient (MCC) metrics by simply using a support vector machine (SVM) as a prediction model. Fig. 1. Fig. 1 [42]Open in a new tab SysTox challenge workflow. First stage (up row): gene selection (signature) from the gene expression data from humans blood samples of the training dataset. Second stage (bottom row): develop inductive prediction models bases on training data from gene signature and provide classification results on testing dataset Materials In the SysTox Computation Challenge [[43]15, [44]16] participants were asked to develop models to classify subjects as smokers versus non-current smokers (SvsNCS), and then former smokers versus never smokers (FSvsNS), based on the information from whole blood gene expression data from humans (subchallenge 1), or humans and rodents (subchallenge 2). The current investigation focuses only on tasks referring to subchallenge 1. Figure [45]1 depicts the workflow of mandatory tasks the challengers were asked to follow. The workflow is the same as in the two classification problems proposed by the challenge. In the first stage of the challenge, a training dataset of gene expression data from human (or human/rodent) blood samples was made available for download to participants. The first task to be done was gene selection from whole blood gene expression data contained in the training dataset. The result of this task is a robust gene signature to be used to reduce training and testing data dimensions. Participants had also to develop inductive prediction models based on training data limited to the gene signatures they had previously identified. Inductive models are developed based only on training data. Classification on each test sample could be carried out only with the previously developed model, without retraining. Inductive models are different from transductive models in which training and testing datasets are processed together and used to retrain models prior to classification prediction. After all participants had submitted their results, in terms of both gene signatures and prediction models, the second stage of the challenge started: testing dataset of gene expression data from human (or human/rodent) blood samples were made available to participants. By using their proposed signatures and predictors, participants had to produce predictions (in terms of probabilities) on testing (unlabeled) samples. After the competition closing, challenge organizers evaluated results submitted by participants only on a subset of testing samples which had been provided during the competition, the so called gold labels. Prediction models scores and rankings are reported on the sbv IMPROVER SysTox Challenge website. Human blood sample data are organized in two datasets: * H1 training dataset: a clinical case-control study conducted at the Queen Ann Street Medical Center (QASMC), London, UK and registered at ClinicalTrials.gov with the identifier [46]NCT01780298 [[47]5, [48]17]. The QASMC study aimed at identifying biomarkers to discriminate smokers with chronic obstructive pulmonary disease (COPD) (i.e., cigarette smoke with a ≥ 10 pack/year smoking history and COPD disease classified as GOLD Stage 1 or 2) from three groups of subjects which are matched by ethnicity, sex, and age (within 5 years) with the recruited COPD subjects: smokers (S), former smokers (FS), and never smokers (NS). All smoking subjects (S and FS) had a smoking history of at least 10 pack-years. FS quit smoking at least 1 year prior to sampling (∼ 78% of FS have stopped for more than 5 years). Patients included males (58%) and females (42%) aged between 40 and 70 years. * H2 testing dataset: a transcriptomics dataset (BLD-SMK-01) produced from PAXgene ^TM blood samples obtained from a biobank repository (BioServe Biotechnologies Ltd., Beltsville, MD, USA) [[49]5]. At the sampling time, the subjects were between 23 and 65 years of age. Subjects with a disease history and those taking prescription medications were excluded. Smokers (S) had smoked at least 10 cigarettes daily for at least three years. Former smokers (FS) quit smoking at least two years before the sampling and before cessation had smoked at least 10 cigarettes daily for at least three years. Smokers (S) and never smokers (NS) were matched by age and sex, while former smokers could not be properly matched due to the lower number of samples available for this group. Sample data of H1 and H2 consist of DNA microarray experiments obtained with GeneChip Human Genome U133 Plus 2.0 Array and GeneChip Mouse Genome 430 2.0 Array (Affymetrix), on blood samples. Microarray data of both H1 and H2 are available in the ArrayExpress database [[50]18], respectively under accession numbers E-MTAB-5278 and E-MTAB-5279. The distribution of training and testing labels and their categories are depicted in Fig. [51]2. For the human samples, 18604 gene expression data were provided. Fig. 2. Fig. 2 [52]Open in a new tab SysTox challenge datasets. Distributions of training labels and testing (gold) labels into classes of subjects: smokers (treated group), former smokers (cessation group), and never smokers (control group) Methods Gene selection The basic idea of our gene signature extraction approach is to identify an overlapping among the most discriminant genes we found out by applying three different feature selection techniques: 1. Feature selection by importances in forests of trees (Extra-Trees) [[53]19] 2. Cross-validated Lasso, using the LARS algorithm [[54]20] 3. Recursive Feature Elimination with SVM estimator [[55]21] Extra-Trees belong to the class of ensemble learning methods. They are based on bagging several instances of a black-box estimator (e.g. a decision tree) on random subsets of the original training set and then combining their individual predictions to form a final prediction. Bagging estimators is a very simple way to improve with respect to a single model without making it necessary to adapt the underlying base algorithm. In many cases, bagging methods reduce overfitting as well as the variance of a base estimator. In this work we use the feature selection facility of the Extra-Trees implementation available in the Python Scikit-learn [[56]22]. LASSO (Least Absolute Shrinkage and Selection Operator) is a regression method performing feature selection by regularization of regression parameters (e.g. constraining the sum of their absolute values). The computation of the LASSO solutions is a quadratic programming problem, and can be tackled by standard numerical analysis algorithms that estimate sparse coefficients. It is widely recognized that the Least Angle Regression procedure (LARS) is the better approach since it exploits the special structure of the LASSO problem, and it provides an efficient way to compute the solutions simultaneously for all values of the regularization parameter. In this work we use the LASSO method with LARS algorithm for feature selection. In the remaining of the paper we will refer to this feature selection method as LASSO-LARS. In particular we use its implementation available in the Python Scikit-learn library. Recursive Feature Elimination with SVM (RFE-SVM) By starting with the complete set of features, RFE-SVM repeats the following three steps until no more features are left: 1) train a SVM model; 2) compute a ranking of features as the squares of the hyperplane coefficients of the SVM model; and 3) remove the features with the worst ranks. In this work we use the RFE-SVM implementation available in Weka Data Mining Software [[57]23]. The three methods produce as outputs three lists of ranked genes in reversal order. Regardless of the ranking criteria (respectively as Decision Treed importance scores, LASSO coefficient estimates, and SVM hyperplane coefficients) the three lists of genes are cut-off to the first hundred of genes with higher ranks. Prediction models The focus of this work is on the data processing methodology to get a robust gene signature. The idea is that if the gene signature is biologically relevant, then classifiers will provide statistically significant results. Therefore, in order to assess the quality and robustness of our gene selection method, on the basis of signatures produced by it, we built a large set of prediction models exploiting well-known supervised learning techniques. We considered a set of nine classifiers, ranging from decision trees to support vector machines, from artificial neural networks to clustering and statistic methods. For the purpose, we used implementations of machine learning techniques available in the opensource Python Scikit-learn library [[58]22]. The list of classifiers, their parameters setting and acronyms are reported in Table [59]1. All methods run in their default parameter configuration, since we were not interested in fine-tuning of each classifier. Table 1. Prediction models Classifier Acronym Parameters Random forests RF split=gini, max depth=none, min samples leaf=1, min samples split=1, max features=auto, no. estimators=10 Gaussian Naive Bayes GNB none k–Nearest neighbors kNN no.neighbors=3, algorithm=auto, metric=minkowski, p=2, weights=uniform, leaf size=30 MultiLayer perceptron MLP activation=relu;algorithm=l-bfgs, α=1e-05, β1=0.9, beta2=0.999, ε=1e-08, hidden layer sizes=(100,) Support vector classifier SVC kernel=linear, C=0.1, tolerance=0.001 Logistic regression LR C=1.0 max iter=100 penalty=L2 tolerance=0.0001, multi class=OvR Linear discriminant analysis LDA solver=SVD, tolerance=0.0001 Gradient tree boosting GTB loos=deviance, subsample=1.0 learning rate=0.1, min sample split=2, mean sample leaf=1, max depth=3, estimators=100 Extremely randomized trees ERT split=gini, max depth=No, min samples leaf=1, min samples split=1, max features=auto, no. estimators=10 [60]Open in a new tab The set of nine prediction models built by means of supervised learning on expression data (from H1 training dataset) of gene signatures Biological and toxicological interpretation of gene signatures To understand the importance of gene signatures with respect to biological function and toxicological effects, we used Comparative Toxicogenomics Database (CTD) [[61]24] and Transcriptator web-application [[62]25] for the enrichment analysis of chemical association, diseases, pathways and gene ontology terms for our gene signatures. The CTD database is publicly available and provides knowledge about how environmental exposures affect human health. It contains both the curated and inferred information regarding chemical–gene/protein interactions, chemical–disease and gene–disease relationships. The functional gene ontology and pathway data related to genes are also included to study the possible mechanisms underlying environmentally influenced diseases. The curated information about gene-chemical interaction, gene-disease association and chemical-disease association is basically obtained through literature. Inferred relationships between gene-disease, gene-chemical and chemical-disease association are established via CTD. For example in case of gene-disease-chemical association network, gene A is associated with disease B because gene A has a curated interaction with chemical C, and chemical C has a curated association with disease B. The database provides inference scores for all inferred relationships. These scores reflect the degree of similarity between CTD chemical–gene–disease association networks and a similar scale-free random network. A high score, suggests a stronger connectivity. We obtained the chemical-gene-disease association information for all the gene signatures. Later we filter out genes only associated to “Tobacco smoke exposure” with inference score cutoff ≥ 50. We obtained the disease association, pathways enrichment and gene ontology enrichment for gene signatures and carried out comparison between them through set analysis using Venn diagram. Results and discussion Gene selection Each feature selection technique has been applied to the datasets, in both SvsNCS and FSvsNS classification problems, by setting a limit to the maximum number of selected genes (one hundred). For each problem the three sets found have been intersected to find a robust gene signature. In the case of SvsNCS problem the results of the first hundred top-ranked genes by applying the three selection criteria are presented in Tables [63]2, [64]3 and [65]4. The three lists of genes show an overlap (the gene names in bold in the table) in the topmost positions. The set of 14 genes shared by all three lists form the resulting gene signature we propose for the SvsNCS case study. In Fig. [66]3 we have reported the boxplot of expression data in the training dataset of the 14-gene signature obtained with our approach. Table 2. RFE-SVM SvsNCS signature AHRR LRRN3 SASH1 CDKN1C SEMA6B RAD52 FSTL1 DSC2 SYCE1L TMEM163 CRACR2B MOG ZP4 KIT P2RY6 AK8 PLA2G4C MIR4697HG SPAG6 ZNF618 CLEC10A COL5A1 B3GALT2 TREM2 TYR MMP3 LHX8 KCNJ2-AS1 ST6GALNAC1 SCIN SPRY2 ADRA2A GCNT3 PTGFR PACRG-AS1 LINC00599 NR4A1 CHI3L1 TPPP3 SLC25A20 NT5C1A TCEB3B BMP7 FANK1 TMTC1 FGD5 APCDD1L GYS2 TIMM8A PID1 SHISA6 MYO1E ADIRF-AS1 CTTNBP2 H19 P2RY12 DSTNP2 MAGI2-AS3 VSIG4 NR4A2 ICA1L GFRA2 [67]GSE1 NPIPB15 ZFP64 AFF3 FOXC2 CCR10 ARHGAP32 GPR15 RRNAD1 NOP9 HYPM PTGFRN SLC25A27 C3orf65 ZMYND12 TM4SF4 C6orf10 DUSP4 FUCA1 PALLD ETNPPL HMGCS2 LMOD3 EFNB1 FABP4 WNT2 FAM187B LINC01270 PRKG2 NMNAT2 CYP4A11 FAM19A2 S1PR5 LINC00544 LRPAP1 CTSV LOC200772 THBS2 [68]Open in a new tab Gene signature obtained with Recursive Feature ith SVM in in smokers versus non-current smoker case study. Gene names in bold are also present in the signatures found by Extra-Trees and LASSO-LARS methods Table 3. Extra-Trees SvsNCS signature LRRN3 LINC00599 P2RY6 CDKN1C GPR15 AHRR CTTNBP2 DSC2 CLEC10A PF4 RGL1 SASH1 FSTL1 PTGFRN C15orf54 MCOLN3 F2R P2RY1 GUCY1A3 NRG1 SEMA6B ESAM CR1L PID1 GP1BA MAPK14 PBX1 GNAZ GP6 TMEM163 RNASE1 SLC44A1 ASGR2 GUCY1B3 ZNF101 LTBP1 TRIP6 SRRD PRR5L CYSTM1 B3GALT2 GRAP2 ANKRD37 MKNK1 BEX2 SV2B FAXDC2 ST6GALNAC1 ICOS NFIB TRDC SLPI CDK2AP1 IL4R GPR20 SH2D1B TLR5 VIL1 ITGB5 IGSF9B CDR2 BTBD11 ELOVL7 ARL3 TUBB1 BZRAP1 ADAMDEC1 C2orf88 COCH LOC100506870 LOC100130938 CA2 P2RY12 SH3BGRL2 PCSK6 PRTFDC1 SAMD14 CYP4A11 ASAP2 H19 LOC283194 BLCAP GORASP1 TGM2 SLC26A8 ZAK PARD3 MB21D2 GP9 S100A12 FANK1 TNFSF4 ZNF618 FAM210B MYBPC3 SLC35G2 ASIC3 SLC6A4 CNST PAPSS2 [69]Open in a new tab Gene signature obtained with feature selection of Extra-Trees in smokers versus non-current smoker case study. Gene names in bold are also present in the signatures found by RFE-SVM and LASSO-LARS methods Table 4. LASSO-LARS SvsNCS signature CDKN1C GPR15 LRRN3 GPR63 P2RY6 SASH1 CLEC10A AHRR [70]GSE1 ARHGAP32 DSC2 CRACR2B PTGFR LHX8 FSTL1 SYCE1L APCDD1L OTC PID1 PTGFRN TMEM163 CCR10 P2RY12 B3GALT2 ST6GALNAC1 RAD52 TRDC BCLAF1 KNTC1 CLSTN3 ZNF536 ACAP1 DLGAP5 IFT140 LAPTM4A MTSS1 SETD1A CCP110 GPRASP1 USP34 SPCS2 PHACTR2 TM9SF4 HDAC9 SART3 BMS1 KIAA0232 DOCK4 TBC1D5 CEP104 PIEZO1 PTDSS1 VPRBP SECISBP2L SLK FAM65B KIAA0195 SNPH EIF4A3 RAPGEF5 RASSF2 KIAA0101 JADE3 KIAA0247 ZFYVE16 KIAA0513 LZTS3 RIMS3 SNX17 MLEC TOX DHX38 RAB11FIP3 HDAC4 FRMPD4 KMT2B TBKBP1 STARD8 ZSCAN12 RNF144A ATG13 KIAA0586 PCDHA9 MATR3 NOS1AP ZNF646 SDC3 KIAA0430 DZIP3 SAFB2 EIF5B IPO13 WSCD2 SLC25A44 CEP135 KIAA0040 TTI1 PPIP5K1 PHF14 FAM53B [71]Open in a new tab Gene signature obtained with Least Absolute Shrinkage and Selection Operator (with Least Angle Regression procedure) in smokers versus non-current smokers case study. Gene names in bold are also present in the signatures found by RFE-SVM and Extra-Trees methods Fig. 3. Fig. 3 [72]Open in a new tab SvsNCS signature. Boxplot distribution of expression data (from H1 training dataset) of genes from the signature obtained for the case study of smokers versus non-current smokers In the case of FSvsNS problem, the results of the first hundred top-ranked genes by applying the three selection criteria are presented in Tables [73]5, [74]6 and [75]7. In this case a small overlapping exists between the three lists of genes produced by the three selection criteria. In particular, only 4 genes are shared (the gene names in bold in the table). The set of 4 genes shared by all three lists form the resulting gene signature we propose for the FSvsNS case study. In Fig. [76]4 we have reported the boxplot of expression data in the training dataset of the 4-gene signature produced by our approach. The experiments showed that by removing the gene LCMT1-AS2 we obtained a more robust gene signature. Table 5. RFE-SVM FSvsNS signature SLC38A3 POU4F1 HSD11B1 GOLGA2P5 IL17RD CELF5 ADAMTS14 PTPN14 MB21D2 TBC1D29 RRP12 C4BPB KRT73 DCAF4 ZNF280B LOC648691 DDX11 TJP3 LINC01097 BCL2L12 RAB42 CLSPN ADAM23 CFD TAS2R9 CFAP46 VSIG4 GDF9 SI DOCK4-AS1 SH3PXD2A-AS1 CLUL1 MMP1 PLA2G2A RTN3 LY6G6D ANKRD6 IGSF9B ZNF582-AS1 C8orf88 REG3A ETV2 NDST3 C6orf99 WNT5B PAX4 NNAT HCG26 SLC5A11 TAAR3 TTC22 HAGHL C17orf78 EDN2 MTUS1 PLCD4 C1orf115 PLEK NS3BP SLC34A2 GGT5 ZNF470 SYN1 SCD MRAS FOXI1 LCMT1-AS2 HTN3 SH3D19 HIST1H4E SHISA6 MCOLN3 LOC100507534 SASH1 APEX1 C22orf31 RNF114 SRRM4 SCN2B HMBOX1 ATP6V1C2 HSF4 SLC17A5 SEPT2 TFAP4 WWTR1 FGF4 SRCIN1 SLC35F1 SLC16A2 TAS2R50 PCAT19 ADAMTS18 TMEM31 CAMK1G SLC25A31 SMR3B SLC17A4 XRCC6BP1 PTPRB [77]Open in a new tab Gene signature obtained with Recursive Feature Elimination with SVM in former smokers versus never smokers case study. Gene names in bold are also present in the signatures found by Extra-Trees and LASSO-LARS methods Table 6. Extra-Trees FSvsNS signature MMP1 PRR29 APCS HSD11B1 DLK2 NS3BP CNTN2 CLDN17 CHGA TMEM31 MAPK10 ZNF280B C20orf85 LDHD CLUL1 MAF WFIKKN2 CYP4B1 NTRK3-AS1 NKX6-1 FAM221A IFIT1 SLC16A1 HSD11B1L LCMT1-AS2 CLCN1 IGSF9B CENPU ZNF652 GPAM ENTPD7 FBXL19-AS1 PRKCE HCG26 NLRP14 B3GNT7 KLF14 SLCO4A1 SNCG SLC34A2 CEP76 CXorf36 ATF2 STAU2-AS1 SIGLEC11 RWDD3 ASB16 FGB HIST1H4H ERN2 CLRN1-AS1 SLC50A1 DOK4 FASTKD1 MB21D2 HDAC1 KIF2A GMIP CT83 CYP2A13 MED6 CHDC2 FGF13-AS1 IFNA21 DEPDC5 CEP250 MCM3AP KRT75 GLP1R RAD51B CFAP20 TMEM184A HOMEZ LINC00922 CRP MAST1 CBL SDF4 KRT19 CELF5 CDCA8 ACTL8 MRPS12 ACER1 SYCE3 AP4E1 TYK2 LOC283914 SLC12A1 SCN2A PLAC4 OXCT1 ABCA11P GLB1 TCEAL7 LRRC32 BHLHE22 LINC01012 TBK1 TMEM225 [78]Open in a new tab Gene signature obtained with feature selection of Extra-Trees in former smokers versus never smokers case study. Gene names in bold are also present in the signatures found by RFE-SVM and LASSO-LARS methods Table 7. LASSO-LARS FSvsNS signature POU4F1 PTPRB CLUL1 SLC38A3 PTPN14 GDF9 LCMT1-AS2 C4BPB LINC00901 HSD11B1 HSF4 ADAMTS18 SEPT2 LOC648691 EDN2 LINC00319 DOCK4-AS1 TMEM246 PBK LINC00964 SLC7A11 IL17RD TBC1D29 PTPN3 NS3BP KIAA0513 KIAA0586 IFT140 LAPTM4A RNF144A MATR3 RIMS3 SETD1A CCP110 GPRASP1 USP34 SNX17 DHX38 KNTC1 HDAC9 PIEZO1 SART3 DOCK4 CEP104 VPRBP SECISBP2L RAB11FIP3 ZNF646 TMEM63A UTP14C SEMA3E NOS1AP GPRIN2 ARHGAP32 ACAP1 ZFYVE16 PCDHA9 KIAA0247 LZTS3 MLEC TOX HDAC4 FRMPD4 JADE3 KMT2B TBKBP1 KIAA0101 STARD8 ZSCAN12 SNPH ZNF536 FAM65B RASSF2 RAPGEF5 SLK KIAA0195 BCLAF1 EIF4A3 ATG13 TM9SF4 CLSTN3 KIAA0232 TBC1D5 PHACTR2 KIAA0226 ADAMTSL2 KIAA0430 MDC1 IQCB1 ZNF516 PDE4DIP CEP135 LPIN2 DZIP3 TTLL4 SAFB2 EIF5B IPO13 WSCD2 SDC3 [79]Open in a new tab Gene signature obtained with Least Absolute Shrinkage and Selection Operator (with Least Angle Regression procedure) in former smokers versus never smokers case study. Gene names in bold are also present in the signatures found by RFE-SVM and Extra-Trees methods Fig. 4. Fig. 4 [80]Open in a new tab FSvsNS signature. Boxplot distribution of expression data (from H1 training dataset) of genes from the signature obtained for the case study of former smokers versus never smokers Signatures biological interpretation With respects to the SvsNCS problem, the lists of the first hundred of top-ranked genes are reported in Tables [81]2, [82]3 and [83]4. As we may note, these gene lists share 14 genes which are associated to very high ranks in all of them. To analyze these signatures, we obtained the gene-chemical association results from CTD database and we selected genes which interact with tobacco smoke pollution with higher inference score. Later, we carried out inferred gene-disease association, pathways and gene ontology enrichments analysis. The results are provided in the supplementary tables reported, in the ‘Additional files’ section, from ‘Additional files [84]1, [85]2 and [86]3’. The comparative analysis of disease association, pathway and gene ontology terms enrichment of the signatures obtained with the three gene selection techniques (Extra-Trees, LASSO-LARS and RFE-SVM), provide a clear and robust picture of the signature associated with smoking effects. From our analysis (Fig. [87]5), we infer that though the overall overlap between the gene signatures from these methods is small, yet the gene signatures from the three methods shares a good amount of gene-disease association and most of these genes are involved in the same diseases. Fig. 5. Fig. 5 [88]Open in a new tab Diseases-pathways-GO-terms association to SVM, Extra-Trees and LASSO-LARS signature. Comparative analysis of gene-disease-pathways-gene ontology terms associated to the gene signatures which were obtained with RFE-SVM, Extra-Trees and LASSO-LARS selection methods in the case study of smokers versus non-current smokers We also observed that the diseases associated to these genes are respiratory tract, pregnancy complications, cardio-vascular, neoplasm, fetal disorder, congenital abnormalities, endocrine system diseases. Similarly, these genes share 74 common pathways, and some of these pathways (cell cycle, chemokine receptors bind chemokines, cytokine signaling in immune system, cytokine-cytokine receptor interaction, mitotic G1-G1/S phases, platelet activation, signaling and aggregation, post-translational protein modification, PPARA activates gene expression, Rap1 signaling pathway and Ras signaling pathway) are known to be involved in cancer progression. The gene ontology enrichment and comparative analysis also suggest that most of these genes are involved in protein binding, membrane, localization, ion binding, regulation of biological process and signal transduction. In the light of these results, we deduce that the three gene signatures produced by our selection criteria, with respect to the smokers versus non-current smokers case study, although different still share the same biological and toxicological characteristics. The overlap analysis among the three methods reported more stronger gene signature. We selected the genes common to all three methods and carried out the enrichment analysis. The enrichment analysis of the gene signature we identified for the SvsNCS problem shows that all 14 genes are enriched (see Table [89]8) in biological processes, such as cellular response to chemical stimulus, and in molecular functions, such as protein binding, ion binding, molecular transducer activity. Table 8. SvsNCS signature biological interpretation Gene name Gene description Chemical interaction CLEC10A C-type lectin domain containing 10A Benzo(a)pyrene GPR15 G protein-coupled receptor 15 Tobacco Smoke Pollution B3GALT2 beta-1,3-galactosyltransferase 2 Tobacco Smoke Pollution, Tretinoin, Valproic Acid, Vehicle Emissions CDKN1C cyclin-dependent kinase inhibitor 1C (p57, Kip2) Tetrachlorodibenzodioxin, tert-Butylhydroperoxide, Valproic Acid DSC2 desmocollin 2 Tetrachlorodibenzodioxin, Valproic Acid LRRN3 leucine rich repeat neuronal 3 Tobacco Smoke Pollution AHRR aryl-hydrocarbon receptor repressor; programmed cell death 6 Benzo(a)pyrene TMEM163 transmembrane protein 163 Valproic Acid, Benzo(a)pyrene PID1 phosphotyrosine interaction domain containing 1 Valproic Acid, Benzo(a)pyrene FSTL1 follistatin-like 1 Methylnitronitrosoguanidine co-treated with Cadmium Chloride P2RY6 pyrimidinergic receptor P2Y, G-protein coupled, 6 Benzo(a)pyrene PTGFRN prostaglandin F2 receptor inhibitor Benzo(a)pyrene, Tetrachlorodibenzodioxin, Valproic Acid ST6GALNAC1 ST6 N-acetylgalactosaminide alpha-2,6-sialyltransferase 1 Acetaminophen, Clofibrate, Phenylmercuric Acetate SASH1 SAM and SH3 domain containing 1 Benzo(a)pyrene [90]Open in a new tab Enrichment analysis of the proposed gene signature in the smokers versus non-current smokers case study It is worth to notice that 4 genes from the proposed gene signature were already known in literature as biomarkers for cigarette smoke exposure. Indeed, genes LRRN3, SASH1, TNFRSF17, CDKN1C have been studied in [[91]5], while LRRN3 gene was already known as biomarker in [[92]26]. These genes were also found as biomarkers by the three winning teams participating in the SysTox Computational Challenge. Moreover these genes occupy the first positions in all the signatures that we identified. This is a further confirmation that our gene ranking criteria are in agreement with other approaches published in literature. Similarly, we obtained the gene signatures for FSvsNS case study, by applying RFE-SVM, Extra-Trees and LASSO-LARS selection methods. The gene signatures are provided in Tables [93]5, [94]6 and [95]7 and they share only four genes. In case of former smoker versus never smokers study, the enrichment analysis of the found gene signature shows that three genes which are included in our signatures (ADAMTS14, SLC38A3, HSD11B1), are known to contain SNPs or somatic mutations and differential expressed in lung/bladder cancers. The toxicogenomics gene-chemical-disease association study and the resulting biological and toxicogenomics data are provided in the supplementary tables reported, in the ‘Additional file’ section, from ‘Additional files [96]4, [97]5, [98]6’. Table [99]9 shows the overlapping matrix of the gene signature resulting from our method with genes signatures produced by Philip Morris International (PMI) and by the three winning teams of the challenge (T264, T225 and T259) [[100]27]. As we can see, in the overlap matrix our signature shares 8 out of 14 genes with the three teams (CLEC10A, GPR15, CDKN1C, LRRN3, AHRR, PID1, P2RY6, and SASH1). The remaining 6 genes (B3GALT2, DSC2, TMEM163, FSTL1, PTGFRN and ST6GALNAC1) were neither found by PMI nor by the winning teams. In the remaining of the document we will refer to the set of 8 genes shared by the three winning teams of the challenge as the common gene signature, while the set of 6 genes proposed only by us will be referred as specific gene signature. The completed set of 14 genes resulting from our method is referred as total gene signature. Table 9. Signature overlaps among methods Gene Our PMI T264 T225 T259 CLEC10A ✓ ✓ ✓ ✓ GPR15 ✓ ✓ ✓ ✓ B3GALT2 ✓ CDKN1C ✓ ✓ ✓ ✓ ✓ DSC2 ✓ LRRN3 ✓ ✓ ✓ ✓ ✓ AHRR ✓ ✓ ✓ ✓ TMEM163 ✓ PID1 ✓ ✓ ✓ ✓ FSTL1 ✓ P2RY6 ✓ ✓ ✓ ✓ PTGFRN ✓ ST6GALNAC1 ✓ SASH1 ✓ ✓ ✓ ✓ ✓ RGL1 ✓ ✓ ✓ SEMA6B ✓ ✓ ✓ CTTNBP2 ✓ ✓ F2R ✓ ✓ [101]Open in a new tab Overlap matrix of the proposed gene signature with those produced by PMI and by the three winning teams of the SysTox Computational Challenge (for the smokers versus non-current smokers case study) We focused on these genes and carried out gene-chemical-pathways association studies using CTD database. The results are showed in Figs. [102]6 and [103]7 and in the supplementary tables reported, in the ‘Additional files’ section, from ‘Additional files [104]7, [105]8, [106]9, [107]10, and [108]11’. Interestingly, we observe in Fig. [109]6 that the common gene signature has stronger affinity for smoke, tobacco smoke and Benzo(a)pyrene, the later being a constituent of cigarette smoke. By including in the analysis the 6 genes found only by us, we observe in Fig. [110]7 that the total gene signature still shows a stronger affinity for smoke and tobacco smoke. Fig. 6. Fig. 6 [111]Open in a new tab Disease-chemical association of common gene signature. Disease and chemical association of 8 genes (common gene signature) from our signature which are shared by the three winning teams of the challenge (smokers versus non-current smokers case study) Fig. 7. Fig. 7 [112]Open in a new tab Disease-chemical association of total gene signature. Disease and chemical association of our signature which includes 6 genes not-shared by the three winning teams of the challenge (smokers versus non-current smokers case study) We also determined the disease association for these 14 genes with inference score greater than threshold (≥ 50) with respect to respiratory tract disease and respiratory insufficiency. Both these diseases of respiratory tract are well characterized in literature as a negative result of tobacco smoking. We also carried out the pathways enrichment analysis for both the common gene signature and the specific gene signature in the case study of smokers versus non-current smokers. Biological and toxicogenomics analysis suggest that these 6 genes specific to our analysis are also very interesting with respect to smoking and could be further investigated as potential biomarkers for tobacco smoking exposure. On comparing the enriched pathways in both common and specific gene signature with respect to the whole set of pathways associated with tobacco smoking, we determined the significant overlapped pathways for these 14 genes. Some of the main pathways are Class A/1 (rhodopsin-like receptors), GPCR downstream signaling, GPCR ligand binding, signal transduction and signaling by GPCR. The results are shown in Fig. [113]8. Out of 28 enriched pathways in specific gene signatures and 29 pathways in common gene signature, 18 and 26 pathways in both the signatures sets are effected by tobacco smoke. Most of these tobacco smoking associated pathways are involved in biological pathways such as cell signaling, platelet activation signaling and aggregation, post-translational protein modification, signaling by BMP, developmental biology, cell cycle, mitotic cyclin D associated events in G1, fatty acid, triacylglycerol and ketone body metabolism, G alpha (q) signalling events, innate immune system, metabolism, metabolism of lipids and lipoproteins and mitotic G1-G1/S phases. All these pathways are associated with the proper functioning of the cell. Fig. 8. Fig. 8 [114]Open in a new tab Pathways overlap in pathways dataset. Overlap of pathways information of common (8) and specific (6) gene signatures (obtained for the case study of smokers versus non-current smokers) with tobacco smoking exposure related complete pathways dataset The tabular results of pathways information associated with common and specific gene signature as well as the overlap analysis with tobacco smoking is provided in the ‘Additional file [115]11’. Biological interpretation of these gene signatures using information from CTD database helps in the strengthening of our prediction model. More interestingly, we obtained a greater number of genes in our signature for smoker versus non-current smokers case study. The 6 genes which are not reported by other participants of the challenge, but suggested by our method, are also interesting and share the same biological and toxicological properties as the other genes of the signatures shared by the other participants. By taking into account these additional genes in our prediction model, we do have better chance to characterize smokers versus non-current smokers and surely this help in strengthening our prediction models over those proposed by the challengers. With regards to the former smoker versus never smokers classification problem, we compared the gene signatures from the three selection methods and extracted three overlapping genes: CLUL1, NS3BP and HSD11B1. Biological and toxicological analysis of these three genes (see Table [116]10) suggests their chemical associations with valproic acid and tetrachlorodibenzodioxin. The later chemical is usually formed as a side product in organic synthesis and burning of organic materials and is a carcinogenic in nature. CLUL1 is involved in “Prenatal Exposure Delayed Effects” due to its chemical interactions with tetrachlorodibenzodioxin and bisphenol A. HSD11B1 is also involved in “Prenatal Exposure Delayed Effects” and it is also known to have chemical interactions with tetrachlorodibenzodioxin and bisphenol A. Table 10. FSvsNS signature biological interpretation Gene name Gene description Chemical interaction CLUL1 clusterin like 1 Valproic Acid, bisphenol A NS3BP NS3 binding protein Not Available HSD11B1 hydroxysteroid 11-beta dehydrogenase 1 Hydrocortisone, bisphenol A, Tetrachlorodibenzodioxin [117]Open in a new tab Enrichment analysis of the proposed gene signature in the former smokers versus never smokers case study Prediction models Once the datasets for both SvsNCS and FSvsNS classification problems were reduced in such a way to contain only expression data of genes beloning to our signatures, we started a set of experiments with different classification methods. For the experiments we chose a subset of classifiers available in the Python Scikit-learn package. The list of classifiers, their parameters settings and acronyms are reported in Table [118]1. For both classification problems, we trained the classifiers on the H1 training dataset shrunk to the signature data. This supervised training procedure yielded to the construction of inductive prediction models for the two case studies. Later, the built models were used to classify (gold) samples from the H2 testing dataset, which of course had been previously reduced to the signature data. With respect to the smokers versus non-current smokers classification problem, the prediction results of the nine selected classifier, in terms of AUPR and MCC scores, are summarized in Table [119]11. The table reports also the scores obtained by the three winners of the challenge (T264, T225 and T259) for comparison. As we can see, the SVC classifier provided the best prediction performance (in both AUPR and MCC metric). Table 11. Performance of classifiers using SvsNCS signature RF GNB kNN MLP SVC LR LDA GTB ERT T264 T225 T259 AUPR 0.961 0.938 0.9140 0.9043 0.9746 0.9537 0.9484 0.9650 0.9580 0.96 0.97 0.95 MCC 0.9012 0.8766 0.8025 0.8272 0.9259 0.8148 0.8765 0.9136 0.8642 0.90 0.77 0.79 [120]Open in a new tab Performance measures, in terms of AUPR and MCC scores, of nine classifiers using the signature obtained for the case study of smokers versus non-current smokers. Results are compared to the scores obtained by winners of SysTox Computational Challenge. Best results in boldface With respect to the former smokers versus never smokers classification problem, the AUPR and MCC scores of the selected classifiers are summarized in Table [121]12. As before, the table compares our results to the scores obtained by the three winners of the challenge. In this second case study, our results are more impressive, since the prediction scores are far better than those obtained by the other challengers. Table 12. Performance of classifiers using FSvsNS signature RF GNB kNN MLP SVC LR LDA GTB ERT T264 T225 T259 AUPR 0.6366 0.6357 0.6594 0.6710 0.7321 0.7024 0.6581 0.5528 0.6774 0.58 0.50 0.47 MCC 0.0845 0.1092 0.1310 0.0307 0.2883 0.2318 0.1472 -0.0644 0.1092 0.07 0.02 -0.02 [122]Open in a new tab Performance measures, in terms of AUPR and MCC scores, of nine classifiers using the signature obtained for the case study of former smokers versus never smokers. Results are compared to the scores obtained by winners of SysTox Computational Challenge. Best results in boldface Conclusions The focus of this work is our contribution to the crowdsourcing initiative, namely the SysTox Computational Challenge, proposed by sbv IMPROVER project. The challenge initiative aims at identifying by crowdsourcing chemical cigarette smoking exposure biomarkers from human whole blood gene expression data. In this context, this work proposed a methodology, as well as an experimental pipeline, to extract robust gene signatures from whole blood gene expression data. In addition, this work showed how to build predictive models based on robust gene signatures. Our models discriminate smokers from non-current smokers, as well as former smokers from never smokers subjects. In our computational approach we crossed three very different gene selection techniques to obtain robust gene signatures. Later, in order to assess the quality and robustness of the found gene signatures, we build, on the basis of expression data of selected genes of our signatures, nine prediction models implemented with different supervised machine learning techniques. With regards to the SvsNCS classification problem we obtained high scores for the majority of the explored learning techniques, with AUPR and MCC scores comparable to (even better than) those obtained by the SysTox Challenge winners. Surprisingly, for what concerns the FSvsNS classification problem, the prediction models build on the basis of the found signatures performed far better than those proposed by the challenge winners. The results obtained by our computational approach are strengthened by the functional annotation terms enrichment analysis, as well as by the toxicogenomics analysis (chemical-gene-disease-pathway association studies) for both the SvsNCS and FSvsNS gene signature. In case of SvsNCS, we obtained highly enriched functional terms such as regulation of steroid genesis, orphan nuclear receptors, nerve growth factor, DNA damage, signal transduction, and membrane associated terms. In the present understanding of negative effects of cigarette smoking on humans, the enriched terms and related genes are known to be associated with either cancer progression or nervous system. On the other hand, in case of FSvsNS, the enriched biological terms are generally associated with inflammatory response, extracellular regions, disulfide bonding. As expected, there are not such harmful effects observed in former smoker when compared to never smokers. The interesting observation about this list is that some of these genes such as ADAMS14, SLC38A3, HSD11B1 accommodate structure variation (SNPs) due to tobacco smoking exposure for longer period of time frame. Additional files [123]Additional file 1^ (59.4KB, csv) Gene-disease-chemical study of Extra-Trees signature in SvsNCS. Gene-disease-chemical association studies for gene signature predicted by Extra-Trees method for smokers versus non-current smokers case study. (CSV 59 kb) [124]Additional file 2^ (29.6KB, csv) Gene-disease-chemical study of LASSO-LARS signature in SvsNCS. Gene-disease-chemical association studies for gene signature predicted by LASSO-LARS method for smokers versus non-current smokers case study. (CSV 30 kb) [125]Additional file 3^ (50KB, csv) Gene-disease-chemical study of RFE-SVM signature in SvsNCS. Gene-disease-chemical association studies for gene signature predicted by RFE-SVM method for smokers versus non-current smokers case study. (CSV 50 kb) [126]Additional file 4^ (44.4KB, csv) Gene-disease-chemical of Extra-Trees signature in FSvsNS. Gene-disease-chemical association studies for gene signature predicted by Extra-Trees method for former smokers versus never smokers case study. (CSV 44 kb) [127]Additional file 5^ (15.1KB, csv) Gene-disease-chemical of LASSO-LARS signature in FSvsNS. Gene-disease-chemical association studies for gene signature predicted by LASSO-LARS method for former smokers versus never smokers case study. (CSV 15 kb) [128]Additional file 6^ (31.9KB, csv) Gene-disease-chemical of RFE-SVM signature in FSvsNS. Gene-disease-chemical association studies for gene signature predicted by RFE-SVM method for former smokers versus never smokers case study. (CSV 32 kb) [129]Additional file 7^ (11KB, csv) GO-enrichment of common gene signature. Gene ontology enrichment analysis for 8 genes in common with other participants of the SysTox Computational Challenge. (CSV 11 kb) [130]Additional file 8^ (9.4KB, csv) GO-enrichment of specific gene signature. Gene ontology enrichment analysis for 6 genes not in common with other participants of the SysTox Computational Challenge. (CSV 9 kb) [131]Additional file 9^ (2.2KB, csv) Pathways-enrichment of common gene signature. Pathways enrichment analysis for 8 genes in common with other participants of the SysTox Computational Challenge. (CSV 2 kb) [132]Additional file 10^ (2.2KB, csv) Pathways-enrichment of specific gene signature. Pathways enrichment analysis for 6 genes not in common with other participants of the sbv IMPROVER SysTox Computational Challenge. (CSV 2 kb) [133]Additional file 11^ (93.3KB, csv) Pathways mapping of common versus specific gene signature. Mapping pathways enrichment for both common and specific gene signature with respect to the complete pathways set associated with tobacco smoke pollution. (CSV 93 kb) Acknowledgements