From tobacco smoking to mutational signature: the role of epigenetic changes in human cancers

From tobacco smoking to mutational signature: the role of epigenetic changes in human cancers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research article From tobacco smoking to mutational signature: the role of epigenetic changes in human cancers Zhishan Chen, Wanqing Wen, Qiuyin Cai, Jirong Long, Ying Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.2.21105/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Tobacco smoking is associated with a unique mutational signature in the human cancer genome. It is unclear whether tobacco smoking -altered DNA methylations and gene expressions affect smoking-related mutational signature. Methods We evaluated the smoking-related DNA methylation sites reported from five previous studies using peripheral blood cells to identify possible target genes. Using the mediation analysis approach, we evaluated whether the association of tobacco smoking with mutational signature was mediated through altered DNA methylation and expression of these target genes. Results Based on data obtained from 21,108 blood samples, we identified 374 smoking-related DNA methylation sites, annotated to 248 target genes. Using data from DNA methylations, gene expressions and smoking-related mutational signature generated from ~7,700 tumor tissue samples across 26 cancer types from The Cancer Genome Atlas (TCGA), we found 11 of the 248 target genes whose expressions were associated with smoking-related mutational signature at a Bonferroni-correction P < 0.001. This included four for head and neck cancer, and seven for lung adenocarcinoma. In lung adenocarcinoma, our results showed that smoking increased the expression of three genes, AHRR , GPR15 , and HDGF, and decreased the expression of two genes, CAPN8 , and RPS6KA1 , which were consequently associated with increased smoking-related mutational signature. Additional evidence showed that the elevated expression of AHRR (cg14817490) and GPR15 (cg19859270), were associated with smoking-altered hypomethylations. Lastly, we showed that the elevated expression of HDGF and decreases expression of RPS6KA1, were associated with poor survival of lung cancer patients. Conclusions Our findings provide novel insights into the contributions of tobacco smoking to carcinogenesis through complex molecular mechanisms of the elevated mutational signature by altered DNA methylations and gene expressions. Cancer Biology Oncology gene expression methylation tobacco smoking mutational signature mediation analysis Figures Figure 1 Figure 2 Figure 3 Background Somatic mutations are one of the most common causes of carcinogenesis in humans [ 1 , 2 ]. Recent studies using data from The Cancer Genome Atlas (TCGA) have created a landscape of somatic mutations in each cancer genome, ranging from hundreds to thousands of somatic mutations across multiple cancer types [ 2 , 3 ]. To explore the biological processes of somatic mutations, Alexandrov and colleagues developed a mathematical framework to deconvolute them into mutational signatures. The approach characterized 96 mutation classifications that included six substitution types, together with a flanking base pair to the mutated base [ 3 ]. More than 30 mutational signatures have been identified across cancer types in TCGA [ 3 , 4 ]. Certain mutational signatures were associated with tobacco smoking, exposure to ultraviolet (UV) light, aging, deficient mismatch repair (MMR), mutations in POLE , increased activity of the APOBEC family of cytidine deaminases, and DNA polymerase POLH [ 3 – 5 ]. In particular, a smoking-related mutational signature featured by predominantly C > A mutations with a transcriptional strand bias was observed in multiple human cancer types, including lung adenocarcinoma, lung small cell carcinomas, head and neck squamous, liver, larynx, oral cavity, and esophagus cancers [ 3 , 6 , 7 ]. Tobacco smoking is a well-known risk factor for multiple cancer types, especially lung cancer [ 8 – 10 ]. DNA methylation, one of the major forms of epigenetic modification, essentially plays a regulatory role in gene expression. It has been a focus of multiple studies as a potential underlying molecular mechanism for tobacco smoking-related cancers. Previous epigenome-wide association studies (EWAS) have reported thousands of DNA methylations at CpG sites associated with tobacco smoking in blood, buccal cell and tumor-adjacent normal lung tissue samples [ 11 – 18 ]. These epidemiological studies showed that tobacco smoking was consistently associated with DNA hypomethylated CpG sites in specific genes such as AHRR (encoding aryl-hydrocarbon receptor repressor) and GPR15 (encoding G protein-coupled receptor 15) in multiple studies [ 19 ]. In particular, Stueve and colleagues identified seven smoking-associated hypomethylated CpG sites in adjacent normal tissues from 237 lung cancer patients. Of note, five of the seven sites including a hypomethylated CpG site in AHRR had been reported by previous blood-based EWAS, which suggests that methylation biomarkers identified from blood samples might reflect methylation changes in the target tissues [ 15 ]. In our study, we evaluated the previously reported smoking-related DNA methylations from a total of 21,108 blood samples to identify candidate target genes [ 11 – 13 , 17 , 18 ]. Using data from DNA methylations, gene expressions and smoking-related mutational signature generated from approximately 7,700 tumor samples across 26 cancer types, we evaluated the associations of expression of these target genes with the smoking-related mutational signature for each cancer type. Using a mediation approach, we further evaluated whether the association of tobacco smoking with the mutational signature may be mediated through an altered expression of these target genes. Similar analyses were performed to evaluate the association of tobacco smoking with the gene expression mediated through smoking-altered DNA methylation. Methods Data resources We collected the previously reported smoking-related methylations in blood samples from five previous EWAS, including Joehanes et al., 2016 (N = 15,907) [ 13 ], Zeilinger et al., 2013 (N = 2,272) [ 18 ], Besingi and Johansson, 2014 (N = 432) [ 12 ], Tsaprouni et al., 2014 (N = 920) [ 17 ], and Ambatipudi et al., 2016 (N = 940) [ 11 ]. In the discovery stage, we only used the 2,622 methylations at CpG sites reported from the study with the largest sample size (N = 15,907). In the replication stage, we only used methylations at CpG sites where we observed consistent associations in at least one other study at an adjusted P < 0.05 (Fig. 1 ). We annotated methylation sites to their target genes based on the annotation from the Bioconductor package FDb.InfiniumMethylation.hg19 (version 2.2.0). This study utilized multiple dimension datasets, including matched gene expression, DNA methylation, and clinical data that included age, gender and tobacco smoking. This was generated from 7757 samples in 26 cancer types from TCGA. The sample size for each cancer type was listed: adrenocortical carcinoma (n = 78), bladder urothelial carcinoma (n = 407), breast invasive carcinoma (n = 972), cervical squamous cell carcinoma and endocervical adenocarcinoma (n = 278), colon adenocarcinoma (n = 272), lymphoid neoplasm diffuse large B-cell lymphoma (n = 37), esophageal carcinoma (n = 181), glioblastoma multiforme (n = 161), head and neck squamous cell carcinoma (n = 495), kidney chromophobe (n = 65), kidney renal clear cell carcinoma (n = 321), kidney renal papillary cell carcinoma (n = 274), acute myeloid leukemia (n = 125), brain lower grade glioma (n = 506), liver hepatocellular carcinoma (n = 354), lung adenocarcinoma (n = 507), lung squamous cell carcinoma (n = 474), ovarian serous cystadenocarcinoma (n = 211), pancreatic adenocarcinoma (n = 171), prostate adenocarcinoma (n = 492), rectum adenocarcinoma (n = 86), sarcoma (n = 233), skin cutaneous melanoma (n = 445), stomach adenocarcinoma (n = 411), uterine corpus endometrial carcinoma (n = 173), and uterine carcinosarcoma (n = 28). All the data were downloaded from TCGA using the Broad Institute Genome Data Analysis Center (GDAC) Firehose portal (stamp data/analyses__2016_01_28) through Firebrowse. Detailed information about datasets, analyses, and data sources are described at Firebrowse ( http://gdac.broadinstitute.org/ ). For gene expressions, the normalized expression levels for genes in tumor tissue samples were measured by RNA-Seq by Expectation Maximization (RSEM). To create a better distribution for downstream analysis, a log2 transfer of the RSEM values was applied. We further transformed the gene expression levels across samples for each cancer type using an inverse normalizing transformation method. For DNA methylation, the data (Level 3) from the Illumina Infinium HumanMethylation450 BeadChip array for each sample in TCGA was measured. The Beta value of the methylation levels of each of the methylation sites were transformed to M value based on the equation , using the function beta2m from the bioconductor package lumi (version 2.32.0) for the downstream analysis. A total of 30 somatic mutational signatures for each sample in TCGA have been characterized from mSignatureDB ( http://tardis.cgu.edu.tw/msignaturedb ). We downloaded the data and only analyzed the known tobacco-associated “mutational signature 4” reported in the mSignatureDB, corresponding to tobacco-associated mutational signature in this study. We measured the enrichment score of this mutational signature for each sample (details described in our previous work [ 20 ]). The analysis of predicted neoantigen load We downloaded the number of neoantigen loads for each sample from TCIA and applied log2 transfer to fit it into a better distribution. Mutational neoantigens were predicted by the use of HLA typing and MHC class I/II binding capabilities. The established neoantigen prediction algorithm NetMHCcons [21] was applied to missense somatic mutations to estimate their binding affinity to the HLA alleles. A more detailed analysis of the processing has been described in previous literature [22, 23]. Statistical analysis The distribution for relative contribution of smoking-related mutational signature to overall mutation burden is severely right-skewed. To better fit regression models, we used the ordinal semi-parametric regression models [24] to evaluate the associations of smoking-related mutational signature with tobacco smoking, gene expression and DNA methylation. The analyses were implemented in the ‘orm’ function from the ‘rms’ library of the R package [24]. To evaluate the enrichment of the significant associations for the 248 smoking-related target genes, we compared the proportion of the significant associations from samples of 248 gene randomly selected from the whole genome - this process was repeated 1000 times. To explore the mediation effects of DNA methylation on the association of tobacco smoking with smoking-related gene expression and the mediation effects of the smoking-related gene expression on the association of tobacco smoking with the smoking-related mutational signature, we conducted mediation analyses using the R package ‘mediation’ [25] to estimate the average direct effect (ADE) and the average causal mediation effect (ACME) of the mediators, which represent the population averages of these causal mediation and direct effects. All the analyses were adjusted for age and gender. To estimate the association between the smoking-related gene expression and overall survival of lung cancer patients, we conducted survival analysis using the Cox proportional hazards model with the adjustment of age, gender and tobacco smoking. Results Identifying blood-based DNA methylations associated with tobacco smoking To identify smoking-related DNA methylations at CpG sites, we evaluated previously reported methylations in blood samples from five EWAS, including Joehanes et al., 2016 (N = 15,907), Zeilinger et al., 2013 (N = 2,272), Besingi and Johansson, 2014 (N = 432), Tsaprouni, 2014 (N = 920), and Ambatipudi et al., 2016 (N = 940) (Fig. 1 A) [ 11 – 13 , 17 , 18 ]. For our discovery data, we used a total of 2,622 methylations at CpG sites reported by Joehanes et al’s study, which had the largest sample size. In the replication stage, we kept only those methylations at CpG sites which showed consistent associations in at least one of the remaining four studies (at the significance level of Bonferroni-correction P < 0.05) (Supplementary table 1; see Materials and Methods). In the end, we identified a total of 374 smoking-related DNA methylations at CpG sites, annotated to 248 target genes (Fig. 1 A; Supplementary table 2). Of the 374 DNA methylations, the majority were hypomethylated CpG sites (n = 252, 67.4%), compared to hypermethylated CpG sites (n = 122, 32.6%). Identifying genes associated with the smoking-related mutational signature in a pan-cancer study The smoking-related mutational signature was characterized in TCGA samples in previous studies [ 3 , 26 ] (Fig. 1 B). Utilizing this study, we used the relative contribution of the mutational signature to overall mutation burden, with values ranging from 0 to 1, for each sample across 26 cancer types in TCGA (see Materials and Methods). Using regression analyses, adjusting for gender and age, we observed that tobacco smoking was significantly associated with increased smoking-related mutational signature in lung adenocarcinoma ( P = 1.75 × 10 − 9 ; Fig. 1 C). In line with previous studies, we observed that the contributions of smoking-related mutational signature to the overall mutation burdens varied in different cancers, with the most enrichments being observed in lung adenocarcinoma (median of contribution: 42%) and lung carcinoma (median of contribution: 35%) (Fig. 1 D). Using regression analyses, adjusting for gender and age (see Materials and Methods), we evaluated the associations between the expressions of the identified 248 smoking-related target genes and smoking-related mutational signature for each cancer type. Of these target genes, we found that 234 genes were associated with smoking-related mutational signature in 19 cancer types (at a P < 0.05) (Supplementary table 3), suggesting that these genes were over-represented ( P < 0.001 for the enrichment analysis, see Materials and Methods). At a more strict threshold of a P < 1 × 10 − 4 , a total of 59 genes were identified in six cancer types: breast (n = 2), colon (n = 1), head and neck (n = 24), lung adenocarcinoma (n = 28), lung carcinoma (n = 2), and melanoma (n = 2) (Fig. 1 E; Supplementary table 3). In the end, using a Bonferroni-correction of P < 0.001 (corresponding to a raw P = 5.0 × 10 − 8 , given 20,000 tests in a genome-wide level), we identified four genes for head and neck cancer and seven genes for lung adenocarcinoma. Specifically, for head and neck cancer, the expression levels of three genes, NFE2L2, RMND5A and SLC44A1 , were associated with increased smoking-related mutational signature, while an inverse association was observed for one gene, ARRB1 (Fig. 1 F, Table 1 ). For lung adenocarcinoma, we found that the expression levels of three genes, GPR15, HDGF , and AHHR , were associated with increased smoking-related mutational signature, while an inverse association was observed for the other four genes, NWD1, KCNQ1, CAPN8 and RPS6KA1 (Fig. 1 F, Table 1 ). GPR15 showed the most significant association with a P < 2.22 × 10 − 16 (Table 1 ). Table 1 Associations between smoking-associated mutational signature and expression of candidate genes (Bonferroni-correction P < 0.01) . Cancer type Gene Beta P head and neck (N = 495) NFE2L2 0.54 4.1 × 10 − 11 RMND5A 0.56 2.0 × 10 − 10 SLC44A1 0.56 2.9 × 10 − 10 ARRB1 -0.46 5.1 × 10 − 8 FAM60A 0.44 5.8 × 10 − 8 RHOG -0.43 5.9 × 10 − 8 lung adenocarcinoma (N = 507) GPR15 0.44 2.2 × 10 − 16 NWD1 -0.40 2.0 × 10 − 13 HDGF 0.42 1.9 × 10 − 12 AHRR 0.34 6.6 × 10 − 10 KCNQ1 -0.29 3.9 × 10 − 8 CAPN8 -0.27 4.4 × 10 − 8 RPS6KA1 -0.30 5.0 × 10 − 8 “N” refers to sample size for each cancer type. A regression analysis was constructed to include tobacco smoking-associated mutational signature as a dependent variable and gene expression levels as the independent variable for each gene of each cancer type. Mediation effects of the identified seven genes on the association of smoking with mutational signature in lung adenocarcinoma For the identified seven genes for lung adenocarcinoma, we evaluated the associations between their expression and tobacco smoking (see Materials and Methods). We found that tobacco smoking was significantly associated with an increased expression of AHRR , GPR15 and HDGF with a P = 6.9 × 10 − 5 , P = 2.7 × 10 − 7 and P = 3.3 × 10 − 4 , respectively, and a decreased expression of CAPN8 and RPS6KA1 with a P = 9.6 × 10 − 4 and P = 0.01, respectively (Fig. 2 A; Supplementary table 4). Using a mediation analysis approach, we further estimated the ACME of the expression of these genes that would be altered by smoking on the mutational signature. We found that they showed significant mediation effects on the association of smoking with the signature (Fig. 2 B, C). Specifically, we observed a significant percentage of ACME for the smoking-related gene expressions: 13.4% (95% CI: 0.046 and 0.256) with a P = 2.0 × 10 − 4 for AHRR , 9.8% (95% CI: 2.4% and 21.7%) with a P = 2.2 × 10 − 3 for CAPN8 , 22.8% (95% CI: 11.3% and 39.4%) with a P < 1 × 10 − 4 for GPR15 , 12.3% (95% CI: 4.7% and 24.6%) with a P = 8.0 × 10 − 4 for HDGF , and 8.6% (95% CI: 0.5 and 20.6%) with a P = 0.032 for RPS6KA1 (Fig. 2 C; Table 2 ). Table 2 Results from a mediation analysis of the direct effects of tobacco smoking and causal mediation (indirect) effects of gene expression that would be altered by tobacco smoking, on the mutational signature in lung adenocarcinoma ( P < 0.05) . Gene Effect * Beta 95% CI P Lower Upper AHRR ACME 4.5 × 10 − 4 1.6 × 10 − 4 8.3 × 10 − 4 < 1.0 × 10 − 4 ADE 2.9 × 10 − 3 1.7 × 10 − 3 4.1 × 10 − 3 < 1.0 × 10 − 4 Total Effect 3.3 × 10 − 3 2.1 × 10 − 3 4.5 × 10 − 3 < 1.0 × 10 − 4 Prop 13.4% 4.6% 25.6% 2.0 × 10 − 4 CAPN8 ACME 3.4 × 10 − 4 8.2 × 10 − 5 6.8 × 10 − 4 < 1.0 × 10 − 4 ADE 3.0 × 10 − 3 1.8 × 10 − 3 4.2 × 10 − 3 < 1.0 × 10 − 4 Total Effect 3.3 × 10 − 3 2.1 × 10 − 3 4.5 × 10 − 3 < 1.0 × 10 − 4 Prop 9.8% 2.4% 21.7% 2.2 × 10 − 3 GPR15 ACME 7.7 × 10 − 4 3.9 × 10 − 4 1.2 × 10 − 3 < 1.0 × 10 − 4 ADE 2.6 × 10 − 3 1.4 × 10 − 3 3.7 × 10 − 3 < 1.0 × 10 − 4 Total Effect 3.4 × 10 − 3 2.2 × 10 − 3 4.4 × 10 − 3 < 1.0 × 10 − 4 Prop 22.8% 11.3% 39.4% < 1.0 × 10 − 4 HDGF ACME 4.2 × 10 − 4 1.6 × 10 − 4 7.6 × 10 − 4 < 1.0 × 10 − 4 ADE 2.9 × 10 − 3 1.8 × 10 − 3 4.1 × 10 − 3 < 1.0 × 10 − 4 Total Effect 3.4 × 10 − 3 2.2 × 10 − 3 4.5 × 10 − 3 < 1.0 × 10 − 4 Prop 12.3% 4.7% 24.6% 8.0 × 10 − 4 RPS6KA1 ACME 3.0 × 10 − 4 1.8 × 10 − 5 6.7 × 10 − 4 0.040 ADE 3.0 × 10 − 3 1.9 × 10 − 3 4.2 × 10 − 3 < 1.0 × 10 − 4 Total Effect 3.3 × 10 − 3 2.1 × 10 − 3 4.5 × 10 − 3 < 1.0 × 10 − 4 Prop 8.6% 5% 20.6% 0.032 “ * ”: “ACME” refers to the average causal mediation effects. “ADE” refers to the average direct effects. “Prop” refers to the proportion of ACME related to total affects. Mediation effects of smoking-related DNA methylation on the association of smoking with gene expression in lung adenocarcinoma In the above mediation analysis, we found that five genes, AHRR , CAPN8 , GPR15 , HDGF , and RPS6KA1 , mediated the association between smoking and mutational signature in lung adenocarcinoma. For these, six smoking-related DNA methylations, cg11554391, cg14817490, cg21446172, cg19859270, cg00867472 and cg13092108, have been reported in blood cells [ 11 – 13 , 17 , 18 ]. We further evaluated the associations between these methylations and tobacco smoking in lung adenocarcinoma. In line with previous findings, we found that consumed tobacco smoking was significantly associated with hypomethylations at the CpG sites: cg11554391 ( AHRR ), cg14817490 ( AHRR) , and cg19859270 ( GPR15) ( P < 0.05 for all; Fig. 3 A; Supplementary table 4). Next, we evaluated the association between the methylation at each CpG site and gene expression. Interestingly, our results showed that the smoking-altered hypomethylation was associated with an elevated expression for each gene ( P < 0.05 for all): AHRR ( cg11554391 or cg14817490) and GPR15 (cg19859270), indicating that these smoking-altered hypomethylations likely play an up-regulation role in their gene expression (Fig. 3 B; Supplementary table 5). In particular, these hypomethylated CpG sites are located in regions with evidence of enhancer activities associated with their target genes (Supplementary Fig. 1). Using a mediation analysis approach, we further estimated the ACME of the methylations that would be altered by smoking on gene expressions. We found that the methylations at two CpG sites, AHRR (cg14817490, P = 0.03) and GPR15 (cg19859270, P < 1 × 10 − 4 ), showed significant mediation effects on the association of smoking with gene expression (Fig. 3 C, D; Table 3 ). Specifically, we observed a significant percentage of ACME for both smoking-related DNA methylations: 8.5% (95% CI: 8% and 24.5%) with a P = 0.03 for AHRR , and 15.9% (95% CI: 5.2% and 32.9%) with a P < 1.0 × 10 − 4 for GRP15 (Fig. 3 D; Table 3 ). Table 3 Results from a mediation analysis of the direct effects of tobacco smoking and causal mediation (indirect) effects of DNA methylations that would be altered by tobacco smoking, on the gene expression in lung adenocarcinoma ( P < 0.05) . CpG Effect * Beta 95% CI P Lower Upper cg14817490 ( AHRR ) ACME 6.5 × 10 − 4 5.7 × 10 − 5 1.5 × 10 − 3 0.03 ADE 6.5 × 10 − 3 3.1 × 10 − 3 1.0 × 10 − 2 < 1.0 × 10 − 4 Total Effect 7.2 × 10 − 3 3.8 × 10 − 3 1.1 × 10 − 2 < 1.0 × 10 − 4 Prop 8.5% 8% 24.5% 0.03 cg19859270 ( GPR15 ) ACME 1.5 × 10 − 3 4.6 × 10 − 4 2.9 × 10 − 3 < 1.0 × 10 − 4 ADE 7.8 × 10 − 3 4.4 × 10 − 3 1.1 × 10 − 2 < 1.0 × 10 − 4 Total Effect 9.3 × 10 − 3 5.8 × 10 − 3 1.3 × 10 − 2 < 1.0 × 10 − 4 Prop 15.9% 5.2% 32.9% < 1.0 × 10 − 4 “ * ” ACME refers to the average causal mediation effects. ADE refers to the average direct effects (ADE). “Prop” refers to the proportion of ACME related to total affects. Supplementary Data Expression of HDGF and RPS6KA1 associated with overall survival of lung cancer patients To explore the association between overall survival of lung cancer patients and the identified five genes that mediated the association between smoking and mutational signature in lung adenocarcinoma, we conducted the Cox regression analysis using data from TCGA (see Materials and Methods). Our results revealed that the elevated expression level of HDGF was associated with the reduced overall survival of lung cancer patients, while an opposite trend was observed for RPS6KA1 when comparing the high level of gene expression (> median) versus low level ( < = median) (Hazard Ratio [HR] = 1.45 and HR = 0.61, P = 0.02 and P = 1.5 × 10 − 3 for HDGF and RPS6KA1 , respectively) (Supplementary Fig. 2A, B). These findings are in line our initial results that tobacco smoking increased expression level of HDGF and decreased expression level of RPS6KA1 . No significant associations with overall survival of lung cancer patients were observed for other three genes. Discussion In the present study, a total of 374 smoking-related methylations annotated to 248 target genes were identified using strict statistical criteria from previous EWASs in blood samples. Using data from TCGA, we identified a total of 11 candidate genes of 248 target genes whose expressions were associated with smoking-related mutational signature, including four in head and neck cancer and seven in lung adenocarcinoma. Of seven genes for lung adenocarcinoma, our results further showed that smoking increased the expression of three genes, AHRR , GPR15 , and HDGF , and decreased the expression of two genes, CAPN8 , and RPS6KA1 . These smoking-altered gene expressions were consequently associated with increased smoking-related mutational signature. In addition, our results showed that the elevated expression of AHRR (cg14817490) and GPR15 (cg19859270), were associated with smoking-altered hypomethylations. Our analysis focused on the identified 374 blood-based methylations associated with tobacco smoking, which have strong evidence of statistical associations from previous studies. In particular, the initial discovery of methylations associated with tobacco smoking is based on a study with the largest sample size we have found so far (N = 15,907) (see Materials and Methods) [ 13 ]. In addition to studies of blood, two studies have investigated methylations associated with tobacco smoking in buccal cells (N = 790) [ 16 ] and tumor adjacent normal lung tissue (N = 237) [ 15 ]. Notably, both studies had limited sample sizes and were insufficient in statistical power to identify smoking-related methylation sites, while they have revealed evidence that blood-based methylation biomarkers could reflect changes in their target tissues. Recently, Ma and Li performed pathway enrichment analyses based on 320 smoking-affected genes identified in blood. Their results showed that 104 of these genes were significantly enriched in pathways associated with the etiology of different cancers [ 27 ]. Consistent with these findings, two recent epidemiology studies showed that smoking-related hypomethylations in blood cells were associated with lung cancer risk [ 28 , 29 ]. Thus, our study shows a connection of blood-based methylations with tobacco smoking-related mutational signature in tumor tissue. Our study not only provides an understanding of the molecular mechanisms underlying tobacco smoking carcinogenesis, but also can potentially lead to a new avenue for target intervention. Using mediation analyses, we concluded that two genes, AHRR and GPR15 , significantly contributed to smoking-related mutational signature, mediated by smoking-altered methylation and gene expression in lung adenocarcinoma. These conclusions are supported by multiple layers of evidence from previous literature. It is known that the AHRR gene encodes a suppressor of the aryl hydrocarbon receptor (AHR). It is involved in the AHR signaling cascade, which plays an essential role in dioxin toxicity, including polycyclic aromatic hydrocarbons (PAHs), an important class of smoking carcinogens [ 30 , 31 ]. It has been documented that the AHRR gene is associated with tobacco smoking, based on EWAS from blood, buccal cell and normal lung tissue [ 11 – 18 ]. In recent studies, the hypomethylated CpG sites in the AHRR gene in pre-diagnostic peripheral blood samples was reported to be associated with lung cancer risk [ 28 , 29 ]. Based on in vitro experiments in lung tissue from both humans and mice, the evaluated AHRR expression has been validated by tobacco smoking-altered methylations [ 14 ]. In addition to AHRR , GPR15 encodes an orphan G-protein-coupled receptor involved in the regulation of innate immunity and T-cell trafficking in the intestinal epithelium [ 32 , 33 ]. Previous studies have shown that the GPR15 gene is associated with tobacco smoking, which is based on EWAS from blood, while no studies have reported it for lung cancer [ 11 , 12 , 16 – 18 ]. Our finding was the first to identify this smoking-related novel gene that significantly contributed to smoking-related mutational signature in lung cancer. Our results showed three additional genes, CAPN8 , HDGF and RPS6KA1 , contributed to smoking-related mutational signature, mediated by gene expression altered by tobacco smoking in lung adenocarcinoma. Tobacco smoking-related methylations in these genes have been reported in the previous EWAS in blood samples. However, we did not observe that these methylations were associated with tobacco smoking in lung adenocarcinoma, although consistent association directions were observed for HDG and RPS6KA1 (Data not shown). Notably, unlike the studies in large sample size from blood studies, the statistical analysis in detecting association between DNA methylation and tobacco smoking is still challenge in tumor tissues due to possible factors, such as tumor heterogeneity, potential confounders, and limited sample size. In fact, our focus on the analysis of the reported blood-based smoking-related DNA methylation sites could identify reliably smoking-related target genes and reduce the possibility of reverse causation. Nevertheless, given the tissue-specificities of some methylations in blood, further studies with a large sample size are still needed to replicate the associations for these candidate tobacco smoking-related genes in lung adenocarcinoma. In fact, our results showed that smoking-related methylations of these genes were associated with decreased expressions of these genes ( P < 0.01 for all), indicating that they may play a down-regulation role in their gene expression in lung adenocarcinoma (Supplementary Fig. 3). Further in vitro or in vivo functional assays are needed to validate the genes that are affected by tobacco smoking in lung cancer. It is known that neoantigens (or neoepitopes) result from missense somatic mutations in cancer cells [ 34 ]. However, how smoking-related mutational signature contribute to neoantigen loads remain unclear. We additionally evaluated the associations between smoking-related mutation signature and predicted neoantigen loads (see Materials and Methods). We observed that smoking-related mutational signature were significantly associated with increased neoantigen loads in three cancer types, head and neck, lung adenocarcinoma, and lung carcinoma (see Materials and Methods). An inverse association was observed in melanoma ( P < 1 × 10 − 4 for all; Supplementary Fig. 4A, B; Supplementary Table 6). The most significant association was observed in lung adenocarcinoma with a P < 2.2 × 10 − 16 . In addition, we also observed that neoantigen loads were associated with all five identified genes ( P < 1 × 10 − 5 ) and tobacco smoking ( P = 2.16 × 10 − 11 ) in lung adenocarcinoma (Supplementary Fig. 4C, D). In particular, the expressions of AHRR and GPR15 had associations with an increased predicted neoantigen load with P = 7.6 × 10 − 10 and P = 7.7 × 10 − 7 , respectively (Supplementary Fig. 4D). Thus, our findings may provide new clues to explore the biological and immunological mechanisms through which smoking-related mutational signature may be involved in carcinogenesis, and provide potential genomic biomarkers for the development of cancer prevention and immunotherapy. Conclusions Our results showed that the smoking-altered DNA methylations and gene expressions play an important role in contributing to smoking-related mutational signature in human cancers. Our results also indicated that tobacco-smoking plays an important role in clinical significance, likely affecting genes with the impact on overall survival of lung cancer patients. Our findings have provided novel insights into the contributions of tobacco smoking to carcinogenesis, especially lung cancer, through complex molecular mechanisms of the elevated mutational signature by altered DNA methylations and gene expressions. Abbreviations AHRR, Aryl-Hydrocarbon Receptor Repressor CAPN8, Calpain 8 EWAS, Epigenome-Wide Association Studies GPR15, G Protein-Coupled Receptor 15 HDGF, Heparin Binding Growth Factor HR, Hazard Ratio RPS6KA1, Ribosomal Protein S6 Kinase A1 TCGA, The Cancer Genome Atlas Declarations Acknowledgements We thank TCGA for providing valuable data resources for the research. We thank Marshal Younger for assistance with editing and manuscript preparation. The data analyses were conducted using the Advanced Computing Center for Research and Education (ACCRE) at Vanderbilt University. Availability of data and material The normalized expressions of gene and DNA methylation were downloaded from the TCGA using the Broad Institute Genome Data Analysis Center (GDAC) Firehose portal through Firebrowse (stamp data/analyses__2016_01_28, http://gdac.broadinstitute.org ). Somatic mutational signatures were downloaded from mSignatureDB ( http://tardis.cgu.edu.tw/msignaturedb ). Neoantigen data was downloaded from TCIA. Funding This work was supported by the research development fund from Vanderbilt University Medical Center. Contributions Conception and design: XG; Acquisition of data and material support: XG and ZC; Analysis and interpretation of data: XG, ZC and WW; Generation of tables/figures: ZC; Writing, review, and/or revision of the manuscript: XG, ZC, WW, QC, JL, YW, WL, XS and WZ; Study supervision: XG. All authors read and approved the final manuscript. Competing interests No potential conflicts of interest were disclosed. Consent for publication Not applicable. Ethics approval and consent to participate Not applicable. References Garraway LA, Lander ES: Lessons from the cancer genome. Cell 2013, 153(1):17-37. Martincorena I, Campbell PJ: Somatic mutation in cancer and normal cells. Science 2015, 349(6255):1483-1489. Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SAJR, Behjati S, Biankin AV, Bignell GR, Bolli N, Borg A, Borresen-Dale AL et al : Signatures of mutational processes in human cancer. Nature 2013, 500(7463):415-+. Alexandrov LB, Jones PH, Wedge DC, Sale JE, Campbell PJ, Nik-Zainal S, Stratton MR: Clock-like mutational processes in human somatic cells. Nat Genet 2015, 47(12):1402-1407. Supek F, Lehner B: Clustered Mutation Signatures Reveal that Error-Prone DNA Repair Targets Mutations to Active Genes. Cell 2017, 170(3):534-547 e523. Alexandrov LB, Ju YS, Haase K, Van Loo P, Martincorena I, Nik-Zainal S, Totoki Y, Fujimoto A, Nakagawa H, Shibata T et al : Mutational signatures associated with tobacco smoking in human cancer. Science 2016, 354(6312):618-622. Helleday T, Eshtad S, Nik-Zainal S: Mechanisms underlying mutational signatures in human cancers. Nat Rev Genet 2014, 15(9):585-598. Gandini S, Botteri E, Iodice S, Boniol M, Lowenfels AB, Maisonneuve P, Boyle P: Tobacco smoking and cancer: A meta-analysis. Int J Cancer 2008, 122(1):155-164. Hecht SS: Lung carcinogenesis by tobacco smoke. Int J Cancer 2012, 131(12):2724-2732. Sasco AJ, Secretan MB, Straif K: Tobacco smoking and cancer: a brief review of recent epidemiological evidence. Lung Cancer-J Iaslc 2004, 45:S3-S9. Ambatipudi S, Cuenin C, Hernandez-Vargas H, Ghantous A, Le Calvez-Kelm F, Kaaks R, Barrdahl M, Boeing H, Aleksandrova K, Trichopoulou A et al : Tobacco smoking-associated genome-wide DNA methylation changes in the EPIC study. Epigenomics-Uk 2016, 8(5):599-618. Besingi W, Johansson A: Smoke-related DNA methylation changes in the etiology of human disease. Human Molecular Genetics 2014, 23(9):2290-2297. Joehanes R, Just AC, Marioni RE, Pilling LC, Reynolds LM, Mandaviya PR, Guan WH, Xu T, Elks CE, Aslibekyan S et al : Epigenetic Signatures of Cigarette Smoking. Circ-Cardiovasc Gene 2016, 9(5):436-447. Shenker NS, Polidoro S, van Veldhoven K, Sacerdote C, Ricceri F, Birrell MA, Belvisi MG, Brown R, Vineis P, Flanagan JM: Epigenome-wide association study in the European Prospective Investigation into Cancer and Nutrition (EPIC-Turin) identifies novel genetic loci associated with smoking. Hum Mol Genet 2013, 22(5):843-851. Stueve TR, Li WQ, Shi J, Marconett CN, Zhang T, Yang C, Mullen D, Yan C, Wheeler W, Hua X et al : Epigenome-wide analysis of DNA methylation in lung tissue shows concordance with blood studies and identifies tobacco smoke-inducible enhancers. Hum Mol Genet 2017, 26(15):3014-3027. Teschendorff AE, Yang Z, Wong A, Pipinikas CP, Jiao YM, Jones A, Anjum S, Hardy R, Salvesen HB, Thirlwell C et al : Correlation of Smoking-Associated DNA Methylation Changes in Buccal Cells With DNA Methylation Changes in Epithelial Cancer. Jama Oncol 2015, 1(4):476-485. Tsaprouni LG, Yang TP, Bell J, Dick KJ, Kanoni S, Nisbet J, Vinuela A, Grundberg E, Nelson CP, Meduri E et al : Cigarette smoking reduces DNA methylation levels at multiple genomic loci but the effect is partially reversible upon cessation. Epigenetics-Us 2014, 9(10):1382-1396. Zeilinger S, Kuhnel B, Klopp N, Baurecht H, Kleinschmidt A, Gieger C, Weidinger S, Lattka E, Adamski J, Peters A et al : Tobacco smoking leads to extensive genome-wide changes in DNA methylation. PLoS One 2013, 8(5):e63812. Gao X, Jia M, Zhang Y, Breitling LP, Brenner H: DNA methylation changes of whole blood cells in response to active smoking exposure in adults: a systematic review of DNA methylation studies. Clin Epigenetics 2015, 7:113. Chen Z, Wen W, Beeghly-Fadiel A, Shu XO, Diez-Obrero V, Long J, Bao J, Wang J, Liu Q, Cai Q et al : Identifying Putative Susceptibility Genes and Evaluating Their Associations with Somatic Mutations in Human Cancers. Am J Hum Genet 2019, 105(3):477-492. Karosiene E, Lundegaard C, Lund O, Nielsen M: NetMHCcons: a consensus method for the major histocompatibility complex class I predictions. Immunogenetics 2012, 64(3):177-186. Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, Hackl H, Trajanoski Z: Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Rep 2017, 18(1):248-262. Chen Z, Wen W, Bao J, Kuhs KL, Cai Q, Long J, Shu XO, Zheng W, Guo X: Integrative genomic analyses of APOBEC-mutational signature, expression and germline deletion of APOBEC3 genes, and immunogenicity in multiple cancer types. BMC Med Genomics 2019, 12(1):131. Harrell FE: Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis, 2nd Edition. Springer Ser Stat 2015:1-582. Imai K, Keele L, Tingley D: A General Approach to Causal Mediation Analysis. Psychol Methods 2010, 15(4):309-334. Huang PJ, Chiu LY, Lee CC, Yeh YM, Huang KY, Chiu CH, Tang P: mSignatureDB: a database for deciphering mutational signatures in human cancers. Nucleic Acids Res 2018, 46(D1):D964-D970. Ma Y, Li MD: Establishment of a Strong Link Between Smoking and Cancer Pathogenesis through DNA Methylation Analysis. Sci Rep 2017, 7(1):1811. Baglietto L, Ponzi E, Haycock P, Hodge A, Bianca Assumma M, Jung CH, Chung J, Fasanelli F, Guida F, Campanella G et al : DNA methylation changes measured in pre-diagnostic peripheral blood samples are associated with smoking and lung cancer risk. Int J Cancer 2017, 140(1):50-61. Fasanelli F, Baglietto L, Ponzi E, Guida F, Campanella G, Johansson M, Grankvist K, Johansson M, Assumma MB, Naccarati A et al : Hypomethylation of smoking-related genes is associated with future lung cancer in four prospective cohorts. Nat Commun 2015, 6:10192. Haarmann-Stemmann T, Bothe H, Kohli A, Sydlik U, Abel J, Fritsche E: Analysis of the transcriptional regulation and molecular function of the aryl hydrocarbon receptor repressor in human cell lines. Drug Metab Dispos 2007, 35(12):2262-2269. Murray IA, Patterson AD, Perdew GH: Aryl hydrocarbon receptor ligands in cancer: friend and foe. Nat Rev Cancer 2014, 14(12):801-814. Kim SV, Xiang WV, Kwak C, Yang Y, Lin XW, Ota M, Sarpel U, Rifkin DB, Xu R, Littman DR: GPR15-mediated homing controls immune homeostasis in the large intestine mucosa. Science 2013, 340(6139):1456-1459. Koks S, Koks G: Activation of GPR15 and its involvement in the biological effects of smoking. Exp Biol Med (Maywood) 2017, 242(11):1207-1212. Chen DS, Mellman I: Oncology Meets Immunology: The Cancer-Immunity Cycle. Immunity 2013, 39(1):1-10. Supplementary Data Supplementary Table 1 : A collection of candidate blood-based methylations at CpG sites reported from five previous epigenome wide association studies. Supplementary Table 2 : A list of 374 candidate blood-based methylation CpG sites and genes identified from both discovery and replication studies, at an adjusted P < 0.05. Supplementary Table 3: Associations between smoking-associated mutational signature and expression of candidate genes for each cancer type ( P < 0.05). Supplementary Table 4: Associations between tobacco smoking and expression of candidate genes and associations between tobacco smoking and methylation of candidate CpG sites. Supplementary Table 5: Association between expression of candidate genes and methylation at each CpG site. Supplementary Table 6: Associations between smoking-associated mutational signature and predicted neoantigen load for each cancer type. Supplementary Figure 1: The epigenetic landscape of regions with methylations at three candidate CpG sites. Supplementary Figure 2: Gene expression of HDGF and RPS6KA1 associated with overall survival of lung cancer patients. Supplementary Figure 3: Associations between gene expressions and methylations at three CpG sites. Supplementary Figure 4: Smoking-related mutational signature contributed to neoantigen load in multiple cancer types. Supplementary Files SupplementaryFigure3.pdf SupplementaryFigure2.tif SupplementaryFigure4.tif SupplementaryFigure1.pdf SupplementaryTables.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-11703","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":296594,"identity":"ace12f5f-4233-432a-8a2e-097e1d6c6490","order_by":1,"name":"Zhishan Chen","email":"","orcid":"","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Zhishan","middleName":"","lastName":"Chen","suffix":""},{"id":296595,"identity":"0106999b-f3bb-4711-85ea-530bb0563758","order_by":2,"name":"Wanqing Wen","email":"","orcid":"","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Wanqing","middleName":"","lastName":"Wen","suffix":""},{"id":296596,"identity":"e523e2d6-2d07-47e3-8b98-5ffdd16b82a2","order_by":3,"name":"Qiuyin Cai","email":"","orcid":"","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Qiuyin","middleName":"","lastName":"Cai","suffix":""},{"id":296597,"identity":"b2693c32-f37d-418c-bd82-8f0828de7c1a","order_by":4,"name":"Jirong Long","email":"","orcid":"","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Jirong","middleName":"","lastName":"Long","suffix":""},{"id":296598,"identity":"518e5201-1b8c-435b-ad78-6ef5513701d4","order_by":5,"name":"Ying Wang","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Wang","suffix":""},{"id":296599,"identity":"0fe42ce7-cd7c-42dd-b055-d5aab0068b72","order_by":6,"name":"Weiqiang Lin","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Weiqiang","middleName":"","lastName":"Lin","suffix":""},{"id":296600,"identity":"f10e1d48-3832-4013-98fa-3ed384580450","order_by":7,"name":"Xiao-ou Shu","email":"","orcid":"","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Xiao-ou","middleName":"","lastName":"Shu","suffix":""},{"id":296601,"identity":"d59f783d-9b29-41b6-9f33-aa1f09eba147","order_by":8,"name":"Wei Zheng","email":"","orcid":"","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zheng","suffix":""},{"id":296602,"identity":"f8c32568-05bb-4755-8cb1-589683f9b1cf","order_by":9,"name":"Xingyi Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACg8NgEkQwHyBZC1sCcVokG+BMHgPitPCz8x5+zVPAYLfheM83ad4cOwZ+6eMX8GphY+ZLswaan7zhzNlt0rzbkhkk+3IKCGjhMTMGaTG4kbvZmHfbAQaDMzwJ+B2G0JLzmDgtks08xo+BWuyAWhgfQ7SwH8CrxeAwjxnjHAOJBMkzxwwfzt2WzCPZw4NXB4PB+TPGH978sbHnO9784MDbbXZy/DzsD/DrAYaAFA+DRGIDlMdDTAQxf/zBwGCPJEDYllEwCkbBKBhZAABlkj+9aFAquQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-5269-1294","institution":"Vanderbilt University Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Xingyi","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2020-01-14 17:08:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.2.21105/v1","doiUrl":"https://doi.org/10.21203/rs.2.21105/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":379169,"identity":"c7e59b50-328f-4c17-8e47-a9f468e25dc6","added_by":"auto","created_at":"2020-01-17 20:14:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":156091,"visible":true,"origin":"","legend":"Identification of genes and their associations with smoking-related mutational signature. A) A flow chart to illustrate the identification of candidate smoking-related DNA methylations from the previously reported blood-based methylations in five EWAS. “N” represents the sample size for each study. B) Smoking-related mutational signature displayed according to the 96 substitution classifications characterized by six substitution types, together with a flanking base pair to the mutated base (Alexandrov et al 2013). C) A scatter plot indicating tobacco smoking correlated with known smoking-related mutational signature in lung adenocarcinoma. The dotted line refers to association coefficient. Each point represents one sample. The x axis represents the number of packs per year for each sample, the y axis represents the contribution of smoking-related mutational signature to overall mutation burden for each sample. The color from red to green refers to a higher to lower density of samples (this note applies to all other figure legends). D) Box plots of the enrichment score of smoking-related mutational signature across 26 cancer types. E) Bar plots indicating the P value of associations between the candidate genes and smoking-related mutational signature in six cancer types. Only genes with a P value of less than 1 × 10-4 were presented. The dashed dot box highlights the genes with significant associations at a Bonferroni-correction P \u003c 0.001. F) Scatter plots for each gene with significant associations at a Bonferroni-correction P \u003c 0.001. From the left to the right panel, four genes in head and neck and seven genes in lung adenocarcinoma are presented.","description":"","filename":"FigurePage1.png","url":"https://assets-eu.researchsquare.com/files/36d27fce-2f98-441b-b8f4-e4148e1edb3c/v1/Figure_Page_1.png"},{"id":379171,"identity":"f62ce4c8-431f-48f6-8f48-01968c215b65","added_by":"auto","created_at":"2020-01-17 20:14:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54066,"visible":true,"origin":"","legend":"Mediation analysis illustrating the effect of the expression of five genes that would be altered by smoking on smoking-related mutational signature in lung adenocarcinoma. A) Scatter plots indicating the statistical significance between five candidate genes and tobacco smoking in lung adenocarcinoma. B) A diagram to illustrate a mediation analysis framework, where gene expression can be a mediator to affect smoking-related mutational signature. C) Five candidate genes are presented with significant mediation effect, at P \u003c 0.05. “ACME” refers to the average causal mediation effects via gene expression on smoking-related mutational signature.","description":"","filename":"FigurePage2.png","url":"https://assets-eu.researchsquare.com/files/36d27fce-2f98-441b-b8f4-e4148e1edb3c/v1/Figure_Page_2.png"},{"id":379173,"identity":"3c142af6-9ac9-4970-b825-4fbb6ce506c8","added_by":"auto","created_at":"2020-01-17 20:14:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":66485,"visible":true,"origin":"","legend":"Mediation analysis illustrating the effect of tobacco smoking-altered methylation on gene expression in lung adenocarcinoma. A) Scatter plots indicating the statistical significance of associations between methylations at three candidate CpG sites and tobacco smoking in lung adenocarcinoma. B) Scatter plots indicating negative correlations between DNA methylation at three candidate CpG sites and gene expression in lung adenocarcinoma. C) A diagram to illustrate a mediation analysis framework, where DNA methylation can be a mediator to affect the expression of tobacco smoking-altered genes. D) Two candidate CpG sites are presented with significant mediation effects on gene expression, at P \u003c 0.05. “ACME” refers to the average causal mediation effects via DNA methylation on gene expression.","description":"","filename":"FigurePage3.png","url":"https://assets-eu.researchsquare.com/files/36d27fce-2f98-441b-b8f4-e4148e1edb3c/v1/Figure_Page_3.png"},{"id":13485730,"identity":"cd9b232f-b740-40b7-9db9-31659afd4879","added_by":"auto","created_at":"2021-09-16 22:03:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":993490,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-11703/v1/9c938823-1399-4ec3-9641-b83893ede0c8.pdf"},{"id":379175,"identity":"94f9b6c4-bba9-4941-9075-f509e81fd193","added_by":"auto","created_at":"2020-01-17 20:14:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2995022,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure3.pdf","url":"https://assets-eu.researchsquare.com/files/36d27fce-2f98-441b-b8f4-e4148e1edb3c/v1/Supplementary Figure 3.pdf"},{"id":379174,"identity":"083c743b-8697-4a55-a85b-6a876757b025","added_by":"auto","created_at":"2020-01-17 20:14:04","extension":"tif","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2270352,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure2.tif","url":"https://assets-eu.researchsquare.com/files/36d27fce-2f98-441b-b8f4-e4148e1edb3c/v1/Supplementary Figure 2.tif"},{"id":379172,"identity":"f56222c7-53ce-460c-9ebc-7a788e6883b4","added_by":"auto","created_at":"2020-01-17 20:14:04","extension":"tif","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1980130,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure4.tif","url":"https://assets-eu.researchsquare.com/files/36d27fce-2f98-441b-b8f4-e4148e1edb3c/v1/Supplementary Figure 4.tif"},{"id":379170,"identity":"ace60a89-3172-40b4-9ac4-e5eb92c43753","added_by":"auto","created_at":"2020-01-17 20:14:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1274932,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.pdf","url":"https://assets-eu.researchsquare.com/files/36d27fce-2f98-441b-b8f4-e4148e1edb3c/v1/Supplementary Figure 1.pdf"},{"id":379168,"identity":"1de12332-399c-447a-af27-471119ac125a","added_by":"auto","created_at":"2020-01-17 20:14:03","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":306273,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/36d27fce-2f98-441b-b8f4-e4148e1edb3c/v1/Supplementary Tables.xlsx"}],"financialInterests":"","formattedTitle":"From tobacco smoking to mutational signature: the role of epigenetic changes in human cancers","fulltext":[{"header":"Background","content":" \u003cp\u003eSomatic mutations are one of the most common causes of carcinogenesis in humans [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Recent studies using data from The Cancer Genome Atlas (TCGA) have created a landscape of somatic mutations in each cancer genome, ranging from hundreds to thousands of somatic mutations across multiple cancer types [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. To explore the biological processes of somatic mutations, Alexandrov and colleagues developed a mathematical framework to deconvolute them into mutational signatures. The approach characterized 96 mutation classifications that included six substitution types, together with a flanking base pair to the mutated base [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. More than 30 mutational signatures have been identified across cancer types in TCGA [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Certain mutational signatures were associated with tobacco smoking, exposure to ultraviolet (UV) light, aging, deficient mismatch repair (MMR), mutations in \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ePOLE\u003c/span\u003e, increased activity of the APOBEC family of cytidine deaminases, and DNA polymerase POLH [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In particular, a smoking-related mutational signature featured by predominantly C\u0026thinsp;\u0026gt;\u0026thinsp;A mutations with a transcriptional strand bias was observed in multiple human cancer types, including lung adenocarcinoma, lung small cell carcinomas, head and neck squamous, liver, larynx, oral cavity, and esophagus cancers [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTobacco smoking is a well-known risk factor for multiple cancer types, especially lung cancer [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. DNA methylation, one of the major forms of epigenetic modification, essentially plays a regulatory role in gene expression. It has been a focus of multiple studies as a potential underlying molecular mechanism for tobacco smoking-related cancers. Previous epigenome-wide association studies (EWAS) have reported thousands of DNA methylations at CpG sites associated with tobacco smoking in blood, buccal cell and tumor-adjacent normal lung tissue samples [\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These epidemiological studies showed that tobacco smoking was consistently associated with DNA hypomethylated CpG sites in specific genes such as \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAHRR\u003c/span\u003e (encoding aryl-hydrocarbon receptor repressor) and \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eGPR15\u003c/span\u003e (encoding G protein-coupled receptor 15) in multiple studies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In particular, Stueve and colleagues identified seven smoking-associated hypomethylated CpG sites in adjacent normal tissues from 237 lung cancer patients. Of note, five of the seven sites including a hypomethylated CpG site in \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAHRR\u003c/span\u003e had been reported by previous blood-based EWAS, which suggests that methylation biomarkers identified from blood samples might reflect methylation changes in the target tissues [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, we evaluated the previously reported smoking-related DNA methylations from a total of 21,108 blood samples to identify candidate target genes [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Using data from DNA methylations, gene expressions and smoking-related mutational signature generated from approximately 7,700 tumor samples across 26 cancer types, we evaluated the associations of expression of these target genes with the smoking-related mutational signature for each cancer type. Using a mediation approach, we further evaluated whether the association of tobacco smoking with the mutational signature may be mediated through an altered expression of these target genes. Similar analyses were performed to evaluate the association of tobacco smoking with the gene expression mediated through smoking-altered DNA methylation.\u003c/p\u003e "},{"header":"Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData resources\u003c/h2\u003e \u003cp\u003eWe collected the previously reported smoking-related methylations in blood samples from five previous EWAS, including Joehanes et al., 2016 (N\u0026thinsp;=\u0026thinsp;15,907) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], Zeilinger et al., 2013 (N\u0026thinsp;=\u0026thinsp;2,272) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], Besingi and Johansson, 2014 (N\u0026thinsp;=\u0026thinsp;432) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], Tsaprouni et al., 2014 (N\u0026thinsp;=\u0026thinsp;920) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and Ambatipudi et al., 2016 (N\u0026thinsp;=\u0026thinsp;940) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In the discovery stage, we only used the 2,622 methylations at CpG sites reported from the study with the largest sample size (N\u0026thinsp;=\u0026thinsp;15,907). In the replication stage, we only used methylations at CpG sites where we observed consistent associations in at least one other study at an adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We annotated methylation sites to their target genes based on the annotation from the Bioconductor package FDb.InfiniumMethylation.hg19 (version 2.2.0).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis study utilized multiple dimension datasets, including matched gene expression, DNA methylation, and clinical data that included age, gender and tobacco smoking. This was generated from 7757 samples in 26 cancer types from TCGA. The sample size for each cancer type was listed: adrenocortical carcinoma (n\u0026thinsp;=\u0026thinsp;78), bladder urothelial carcinoma (n\u0026thinsp;=\u0026thinsp;407), breast invasive carcinoma (n\u0026thinsp;=\u0026thinsp;972), cervical squamous cell carcinoma and endocervical adenocarcinoma (n\u0026thinsp;=\u0026thinsp;278), colon adenocarcinoma (n\u0026thinsp;=\u0026thinsp;272), lymphoid neoplasm diffuse large B-cell lymphoma (n\u0026thinsp;=\u0026thinsp;37), esophageal carcinoma (n\u0026thinsp;=\u0026thinsp;181), glioblastoma multiforme (n\u0026thinsp;=\u0026thinsp;161), head and neck squamous cell carcinoma (n\u0026thinsp;=\u0026thinsp;495), kidney chromophobe (n\u0026thinsp;=\u0026thinsp;65), kidney renal clear cell carcinoma (n\u0026thinsp;=\u0026thinsp;321), kidney renal papillary cell carcinoma (n\u0026thinsp;=\u0026thinsp;274), acute myeloid leukemia (n\u0026thinsp;=\u0026thinsp;125), brain lower grade glioma (n\u0026thinsp;=\u0026thinsp;506), liver hepatocellular carcinoma (n\u0026thinsp;=\u0026thinsp;354), lung adenocarcinoma (n\u0026thinsp;=\u0026thinsp;507), lung squamous cell carcinoma (n\u0026thinsp;=\u0026thinsp;474), ovarian serous cystadenocarcinoma (n\u0026thinsp;=\u0026thinsp;211), pancreatic adenocarcinoma (n\u0026thinsp;=\u0026thinsp;171), prostate adenocarcinoma (n\u0026thinsp;=\u0026thinsp;492), rectum adenocarcinoma (n\u0026thinsp;=\u0026thinsp;86), sarcoma (n\u0026thinsp;=\u0026thinsp;233), skin cutaneous melanoma (n\u0026thinsp;=\u0026thinsp;445), stomach adenocarcinoma (n\u0026thinsp;=\u0026thinsp;411), uterine corpus endometrial carcinoma (n\u0026thinsp;=\u0026thinsp;173), and uterine carcinosarcoma (n\u0026thinsp;=\u0026thinsp;28). All the data were downloaded from TCGA using the Broad Institute Genome Data Analysis Center (GDAC) Firehose portal (stamp data/analyses__2016_01_28) through Firebrowse. Detailed information about datasets, analyses, and data sources are described at Firebrowse (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gdac.broadinstitute.org/\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor gene expressions, the normalized expression levels for genes in tumor tissue samples were measured by RNA-Seq by Expectation Maximization (RSEM). To create a better distribution for downstream analysis, a log2 transfer of the RSEM values was applied. We further transformed the gene expression levels across samples for each cancer type using an inverse normalizing transformation method.\u003c/p\u003e \u003cp\u003eFor DNA methylation, the data (Level 3) from the Illumina Infinium HumanMethylation450 BeadChip array for each sample in TCGA was measured. The Beta value of the methylation levels of each of the methylation sites were transformed to M value based on the equation ,\u003c/p\u003e\n\n\u003cp style=\"text-align: center;\"\u003e\n \u003cimg src=\"data:image/png;base64,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\" style=\"width: 145px;\"\u003e\n \u003cbr\u003e\n\u003c/p\u003e\n\n\nusing the function beta2m from the bioconductor package lumi (version 2.32.0) for the downstream analysis.\u003c/p\u003e \u003cp\u003eA total of 30 somatic mutational signatures for each sample in TCGA have been characterized from mSignatureDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tardis.cgu.edu.tw/msignaturedb\u003c/span\u003e\u003c/span\u003e). We downloaded the data and only analyzed the known tobacco-associated \u0026ldquo;mutational signature 4\u0026rdquo; reported in the mSignatureDB, corresponding to tobacco-associated mutational signature in this study. We measured the enrichment score of this mutational signature for each sample (details described in our previous work [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]).\u003c/p\u003e \u003c/div\u003e \n\u003ch2\u003eThe analysis of predicted neoantigen load\u003c/h2\u003e\n\u003cp\u003eWe downloaded the number of neoantigen loads for each sample from TCIA and applied log2 transfer to fit it into a better distribution. Mutational neoantigens were predicted by the use of HLA typing and MHC class I/II binding capabilities. The established neoantigen prediction algorithm NetMHCcons [21] was applied to missense somatic mutations to estimate their binding affinity to the HLA alleles. A more detailed analysis of the processing has been described in previous literature [22, 23].\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eThe distribution for relative contribution of smoking-related mutational signature to overall mutation burden is severely right-skewed. To better fit regression models, we used the ordinal semi-parametric regression models [24] to evaluate the associations of smoking-related mutational signature with tobacco smoking, gene expression and DNA methylation. The analyses were implemented in the \u0026lsquo;orm\u0026rsquo; function from the \u0026lsquo;rms\u0026rsquo; library of the R package [24]. To evaluate the enrichment of the significant associations for the 248 smoking-related target genes, we compared the proportion of the significant associations from samples of 248 gene randomly selected from the whole genome - this process was repeated 1000 times. To explore the mediation effects of DNA methylation on the association of tobacco smoking with smoking-related gene expression and the mediation effects of the smoking-related gene expression on the association of tobacco smoking with the smoking-related mutational signature, we conducted\u0026nbsp; mediation analyses using the R package \u0026lsquo;mediation\u0026rsquo; [25] to estimate the average direct effect (ADE) and the average causal mediation effect (ACME) of the mediators, which represent the population averages of these causal mediation and direct effects. All the analyses were adjusted for age and gender. To estimate the association between the smoking-related gene expression and overall survival of lung cancer patients, we conducted survival analysis using the Cox proportional hazards model with the adjustment of age, gender and tobacco smoking.\u003c/p\u003e"},{"header":"Results","content":" \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eIdentifying blood-based DNA methylations associated with tobacco smoking\u003c/h2\u003e \u003cp\u003eTo identify smoking-related DNA methylations at CpG sites, we evaluated previously reported methylations in blood samples from five EWAS, including Joehanes et al., 2016 (N\u0026thinsp;=\u0026thinsp;15,907), Zeilinger et al., 2013 (N\u0026thinsp;=\u0026thinsp;2,272), Besingi and Johansson, 2014 (N\u0026thinsp;=\u0026thinsp;432), Tsaprouni, 2014 (N\u0026thinsp;=\u0026thinsp;920), and Ambatipudi et al., 2016 (N\u0026thinsp;=\u0026thinsp;940) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. For our discovery data, we used a total of 2,622 methylations at CpG sites reported by Joehanes et al\u0026rsquo;s study, which had the largest sample size. In the replication stage, we kept only those methylations at CpG sites which showed consistent associations in at least one of the remaining four studies (at the significance level of Bonferroni-correction \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Supplementary table 1; see Materials and Methods). In the end, we identified a total of 374 smoking-related DNA methylations at CpG sites, annotated to 248 target genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA; Supplementary table 2). Of the 374 DNA methylations, the majority were hypomethylated CpG sites (n\u0026thinsp;=\u0026thinsp;252, 67.4%), compared to hypermethylated CpG sites (n\u0026thinsp;=\u0026thinsp;122, 32.6%).\u003c/p\u003e \u003ch2\u003eIdentifying genes associated with the smoking-related mutational signature in a pan-cancer study\u003c/h2\u003e \u003cp\u003eThe smoking-related mutational signature was characterized in TCGA samples in previous studies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Utilizing this study, we used the relative contribution of the mutational signature to overall mutation burden, with values ranging from 0 to 1, for each sample across 26 cancer types in TCGA (see Materials and Methods). Using regression analyses, adjusting for gender and age, we observed that tobacco smoking was significantly associated with increased smoking-related mutational signature in lung adenocarcinoma (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;=\u0026thinsp;1.75\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). In line with previous studies, we observed that the contributions of smoking-related mutational signature to the overall mutation burdens varied in different cancers, with the most enrichments being observed in lung adenocarcinoma (median of contribution: 42%) and lung carcinoma (median of contribution: 35%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Using regression analyses, adjusting for gender and age (see Materials and Methods), we evaluated the associations between the expressions of the identified 248 smoking-related target genes and smoking-related mutational signature for each cancer type. Of these target genes, we found that 234 genes were associated with smoking-related mutational signature in 19 cancer types (at a \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Supplementary table 3), suggesting that these genes were over-represented (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for the enrichment analysis, see Materials and Methods). At a more strict threshold of a \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, a total of 59 genes were identified in six cancer types: breast (n\u0026thinsp;=\u0026thinsp;2), colon (n\u0026thinsp;=\u0026thinsp;1), head and neck (n\u0026thinsp;=\u0026thinsp;24), lung adenocarcinoma (n\u0026thinsp;=\u0026thinsp;28), lung carcinoma (n\u0026thinsp;=\u0026thinsp;2), and melanoma (n\u0026thinsp;=\u0026thinsp;2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE; Supplementary table 3).\u003c/p\u003e \u003cp\u003eIn the end, using a Bonferroni-correction of \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (corresponding to a raw \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;=\u0026thinsp;5.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e, given 20,000 tests in a genome-wide level), we identified four genes for head and neck cancer and seven genes for lung adenocarcinoma. Specifically, for head and neck cancer, the expression levels of three genes, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eNFE2L2, RMND5A\u003c/span\u003e and \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eSLC44A1\u003c/span\u003e, were associated with increased smoking-related mutational signature, while an inverse association was observed for one gene, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eARRB1\u003c/span\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For lung adenocarcinoma, we found that the expression levels of three genes, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eGPR15, HDGF\u003c/span\u003e, and \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAHHR\u003c/span\u003e, were associated with increased smoking-related mutational signature, while an inverse association was observed for the other four genes, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eNWD1, KCNQ1, CAPN8 and RPS6KA1\u003c/span\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eGPR15\u003c/span\u003e showed the most significant association with a \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;\u0026lt;\u0026thinsp;2.22\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eAssociations between smoking-associated mutational signature and expression of candidate genes (Bonferroni-correction\u003c/span\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e\u0026lt;\u0026thinsp;0.01)\u003c/span\u003e.\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCancer type\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eGene\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eBeta\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cdiv class=\"SimplePara\"\u003ehead and neck\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e(N\u0026thinsp;=\u0026thinsp;495)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eNFE2L2\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.54\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.1\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eRMND5A\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.56\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eSLC44A1\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.56\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.9\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eARRB1\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.46\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.1\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eFAM60A\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.44\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.8\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eRHOG\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.43\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.9\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cdiv class=\"SimplePara\"\u003elung adenocarcinoma\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e(N\u0026thinsp;=\u0026thinsp;507)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eGPR15\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.44\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.2\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eNWD1\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.40\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eHDGF\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.42\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.9\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;12\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAHRR\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.34\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e6.6\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eKCNQ1\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.29\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.9\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eCAPN8\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.27\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.4\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eRPS6KA1\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.30\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u0026ldquo;N\u0026rdquo; refers to sample size for each cancer type. A regression analysis was constructed to include tobacco smoking-associated mutational signature as a dependent variable and gene expression levels as the independent variable for each gene of each cancer type.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003ch2\u003eMediation effects of the identified seven genes on the association of smoking with mutational signature in lung adenocarcinoma\u003c/h2\u003e \u003cp\u003eFor the identified seven genes for lung adenocarcinoma, we evaluated the associations between their expression and tobacco smoking (see Materials and Methods). We found that tobacco smoking was significantly associated with an increased expression of \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAHRR\u003c/span\u003e, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eGPR15\u003c/span\u003e and \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eHDGF\u003c/span\u003e with a \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;=\u0026thinsp;6.9\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;=\u0026thinsp;2.7\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e and \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;=\u0026thinsp;3.3\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, respectively, and a decreased expression of \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eCAPN8\u003c/span\u003e and \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eRPS6KA1\u003c/span\u003e with a \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;=\u0026thinsp;9.6\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e and \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.01, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA; Supplementary table 4). Using a mediation analysis approach, we further estimated the ACME of the expression of these genes that would be altered by smoking on the mutational signature. We found that they showed significant mediation effects on the association of smoking with the signature (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, C). Specifically, we observed a significant percentage of ACME for the smoking-related gene expressions: 13.4% (95% CI: 0.046 and 0.256) with a \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;=\u0026thinsp;2.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e for \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAHRR\u003c/span\u003e, 9.8% (95% CI: 2.4% and 21.7%) with a \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;=\u0026thinsp;2.2\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e for \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eCAPN8\u003c/span\u003e, 22.8% (95% CI: 11.3% and 39.4%) with a \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e for \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eGPR15\u003c/span\u003e, 12.3% (95% CI: 4.7% and 24.6%) with a \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;=\u0026thinsp;8.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e for \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eHDGF\u003c/span\u003e, and 8.6% (95% CI: 0.5 and 20.6%) with a \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.032 for \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eRPS6KA1\u003c/span\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eResults from a mediation analysis of the direct effects of tobacco smoking and causal mediation (indirect) effects of gene expression that would be altered by tobacco smoking, on the mutational signature in lung adenocarcinoma (\u003c/span\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e\u0026lt;\u0026thinsp;0.05)\u003c/span\u003e.\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cdiv class=\"SimplePara\"\u003eGene\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cdiv class=\"SimplePara\"\u003eEffect \u003csup\u003e*\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cdiv class=\"SimplePara\"\u003eBeta\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e95% CI\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eLower\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eUpper\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAHRR\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eACME\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.5\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.6\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e8.3\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;1.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eADE\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.9\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.7\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.1\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;1.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTotal Effect\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.3\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.1\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.5\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;1.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eProp\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e13.4%\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.6%\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e25.6%\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eCAPN8\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eACME\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.4\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e8.2\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e6.8\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;1.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eADE\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.8\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.2\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;1.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTotal Effect\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.3\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.1\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.5\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;1.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eProp\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e9.8%\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.4%\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e21.7%\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.2\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eGPR15\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eACME\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e7.7\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.9\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.2\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;1.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eADE\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.6\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.4\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.7\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;1.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTotal Effect\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.4\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.2\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.4\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;1.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eProp\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e22.8%\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e11.3%\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e39.4%\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;1.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eHDGF\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eACME\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.2\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.6\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e7.6\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;1.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eADE\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.9\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.8\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.1\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;1.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTotal Effect\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.4\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.2\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.5\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;1.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eProp\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e12.3%\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.7%\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e24.6%\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e8.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eRPS6KA1\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eACME\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.8\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e6.7\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.040\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eADE\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.9\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.2\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;1.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTotal Effect\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.3\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.1\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.5\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;1.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eProp\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e8.6%\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e5%\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e20.6%\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.032\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u0026ldquo;\u003csup\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e*\u003c/span\u003e\u003c/sup\u003e\u0026rdquo;: \u0026ldquo;ACME\u0026rdquo; refers to the average causal mediation effects. \u0026ldquo;ADE\u0026rdquo; refers to the average direct effects. \u0026ldquo;Prop\u0026rdquo; refers to the proportion of ACME related to total affects.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003ch2\u003eMediation effects of smoking-related DNA methylation on the association of smoking with gene expression in lung adenocarcinoma\u003c/h2\u003e \u003cp\u003eIn the above mediation analysis, we found that five genes, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAHRR\u003c/span\u003e, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eCAPN8\u003c/span\u003e, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eGPR15\u003c/span\u003e, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eHDGF\u003c/span\u003e, and \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eRPS6KA1\u003c/span\u003e, mediated the association between smoking and mutational signature in lung adenocarcinoma. For these, six smoking-related DNA methylations, cg11554391, cg14817490, cg21446172, cg19859270, cg00867472 and cg13092108, have been reported in blood cells [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. We further evaluated the associations between these methylations and tobacco smoking in lung adenocarcinoma. In line with previous findings, we found that consumed tobacco smoking was significantly associated with hypomethylations at the CpG sites: cg11554391 (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAHRR\u003c/span\u003e), cg14817490 (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAHRR)\u003c/span\u003e, and cg19859270 (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eGPR15)\u003c/span\u003e (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA; Supplementary table 4). Next, we evaluated the association between the methylation at each CpG site and gene expression. Interestingly, our results showed that the smoking-altered hypomethylation was associated with an elevated expression for each gene (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all): \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAHRR (\u003c/span\u003ecg11554391 or cg14817490) and \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eGPR15\u003c/span\u003e (cg19859270), indicating that these smoking-altered hypomethylations likely play an up-regulation role in their gene expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB; Supplementary table 5). In particular, these hypomethylated CpG sites are located in regions with evidence of enhancer activities associated with their target genes (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing a mediation analysis approach, we further estimated the ACME of the methylations that would be altered by smoking on gene expressions. We found that the methylations at two CpG sites, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAHRR\u003c/span\u003e (cg14817490, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.03) and \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eGPR15\u003c/span\u003e (cg19859270, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), showed significant mediation effects on the association of smoking with gene expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Specifically, we observed a significant percentage of ACME for both smoking-related DNA methylations: 8.5% (95% CI: 8% and 24.5%) with a \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.03 for \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAHRR\u003c/span\u003e, and 15.9% (95% CI: 5.2% and 32.9%) with a \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e for \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eGRP15\u003c/span\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eResults from a mediation analysis of the direct effects of tobacco smoking and causal mediation (indirect) effects of DNA methylations that would be altered by tobacco smoking, on the gene expression in lung adenocarcinoma (\u003c/span\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e\u0026lt;\u0026thinsp;0.05)\u003c/span\u003e.\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cdiv class=\"SimplePara\"\u003eCpG\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cdiv class=\"SimplePara\"\u003eEffect \u003csup\u003e*\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cdiv class=\"SimplePara\"\u003eBeta\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e95% CI\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eLower\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eUpper\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cdiv class=\"SimplePara\"\u003ecg14817490\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e(\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAHRR\u003c/span\u003e)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eACME\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e6.5\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.7\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.5\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.03\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eADE\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e6.5\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.1\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;1.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTotal Effect\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e7.2\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.8\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.1\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;1.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eProp\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e8.5%\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e8%\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e24.5%\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.03\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cdiv class=\"SimplePara\"\u003ecg19859270\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e(\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eGPR15\u003c/span\u003e)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eACME\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.5\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.6\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.9\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;1.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eADE\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e7.8\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.4\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.1\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;1.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTotal Effect\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e9.3\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.8\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.3\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;1.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eProp\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e15.9%\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.2%\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e32.9%\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;1.0\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u0026ldquo;\u003csup\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e*\u003c/span\u003e\u003c/sup\u003e\u0026rdquo; ACME refers to the average causal mediation effects. ADE refers to the average direct effects (ADE). \u0026ldquo;Prop\u0026rdquo; refers to the proportion of ACME related to total affects.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eSupplementary Data\u003c/span\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003ch2\u003eExpression of\u003c/span\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eHDGF\u003c/span\u003e \u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eand\u003c/span\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eRPS6KA1\u003c/span\u003e \u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eassociated with overall survival of lung cancer patients\u003c/h2\u003e \u003cp\u003eTo explore the association between overall survival of lung cancer patients and the identified five genes that mediated the association between smoking and mutational signature in lung adenocarcinoma, we conducted the Cox regression analysis using data from TCGA (see Materials and Methods). Our results revealed that the elevated expression level of \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eHDGF\u003c/span\u003e was associated with the reduced overall survival of lung cancer patients, while an opposite trend was observed for \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eRPS6KA1\u003c/span\u003e when comparing the high level of gene expression (\u0026gt;\u0026thinsp;median) versus low level (\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;median) (Hazard Ratio [HR]\u0026thinsp;=\u0026thinsp;1.45 and HR\u0026thinsp;=\u0026thinsp;0.61, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.02 and \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;=\u0026thinsp;1.5\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e for \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eHDGF\u003c/span\u003e and \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eRPS6KA1\u003c/span\u003e, respectively) (Supplementary Fig.\u0026nbsp;2A, B). These findings are in line our initial results that tobacco smoking increased expression level of \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eHDGF\u003c/span\u003e and decreased expression level of \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eRPS6KA1\u003c/span\u003e. No significant associations with overall survival of lung cancer patients were observed for other three genes.\u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion","content":" \u003cp\u003eIn the present study, a total of 374 smoking-related methylations annotated to 248 target genes were identified using strict statistical criteria from previous EWASs in blood samples. Using data from TCGA, we identified a total of 11 candidate genes of 248 target genes whose expressions were associated with smoking-related mutational signature, including four in head and neck cancer and seven in lung adenocarcinoma. Of seven genes for lung adenocarcinoma, our results further showed that smoking increased the expression of three genes, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAHRR\u003c/span\u003e, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eGPR15\u003c/span\u003e, and \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eHDGF\u003c/span\u003e, and decreased the expression of two genes, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eCAPN8\u003c/span\u003e, and \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eRPS6KA1\u003c/span\u003e. These smoking-altered gene expressions were consequently associated with increased smoking-related mutational signature. In addition, our results showed that the elevated expression of \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAHRR\u003c/span\u003e (cg14817490) and \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eGPR15\u003c/span\u003e (cg19859270), were associated with smoking-altered hypomethylations.\u003c/p\u003e \u003cp\u003eOur analysis focused on the identified 374 blood-based methylations associated with tobacco smoking, which have strong evidence of statistical associations from previous studies. In particular, the initial discovery of methylations associated with tobacco smoking is based on a study with the largest sample size we have found so far (N\u0026thinsp;=\u0026thinsp;15,907) (see Materials and Methods) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In addition to studies of blood, two studies have investigated methylations associated with tobacco smoking in buccal cells (N\u0026thinsp;=\u0026thinsp;790) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and tumor adjacent normal lung tissue (N\u0026thinsp;=\u0026thinsp;237) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Notably, both studies had limited sample sizes and were insufficient in statistical power to identify smoking-related methylation sites, while they have revealed evidence that blood-based methylation biomarkers could reflect changes in their target tissues. Recently, Ma and Li performed pathway enrichment analyses based on 320 smoking-affected genes identified in blood. Their results showed that 104 of these genes were significantly enriched in pathways associated with the etiology of different cancers [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Consistent with these findings, two recent epidemiology studies showed that smoking-related hypomethylations in blood cells were associated with lung cancer risk [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Thus, our study shows a connection of blood-based methylations with tobacco smoking-related mutational signature in tumor tissue. Our study not only provides an understanding of the molecular mechanisms underlying tobacco smoking carcinogenesis, but also can potentially lead to a new avenue for target intervention.\u003c/p\u003e \u003cp\u003eUsing mediation analyses, we concluded that two genes, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAHRR\u003c/span\u003e and \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eGPR15\u003c/span\u003e, significantly contributed to smoking-related mutational signature, mediated by smoking-altered methylation and gene expression in lung adenocarcinoma. These conclusions are supported by multiple layers of evidence from previous literature. It is known that the \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAHRR\u003c/span\u003e gene encodes a suppressor of the aryl hydrocarbon receptor (AHR). It is involved in the AHR signaling cascade, which plays an essential role in dioxin toxicity, including polycyclic aromatic hydrocarbons (PAHs), an important class of smoking carcinogens [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. It has been documented that the \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAHRR\u003c/span\u003e gene is associated with tobacco smoking, based on EWAS from blood, buccal cell and normal lung tissue [\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In recent studies, the hypomethylated CpG sites in the \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAHRR\u003c/span\u003e gene in pre-diagnostic peripheral blood samples was reported to be associated with lung cancer risk [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Based on \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ein vitro\u003c/span\u003e experiments in lung tissue from both humans and mice, the evaluated \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAHRR\u003c/span\u003e expression has been validated by tobacco smoking-altered methylations [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In addition to \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAHRR\u003c/span\u003e, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eGPR15\u003c/span\u003e encodes an orphan G-protein-coupled receptor involved in the regulation of innate immunity and T-cell trafficking in the intestinal epithelium [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Previous studies have shown that the \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eGPR15\u003c/span\u003e gene is associated with tobacco smoking, which is based on EWAS from blood, while no studies have reported it for lung cancer [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Our finding was the first to identify this smoking-related novel gene that significantly contributed to smoking-related mutational signature in lung cancer.\u003c/p\u003e \u003cp\u003eOur results showed three additional genes, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eCAPN8\u003c/span\u003e, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eHDGF\u003c/span\u003e and \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eRPS6KA1\u003c/span\u003e, contributed to smoking-related mutational signature, mediated by gene expression altered by tobacco smoking in lung adenocarcinoma. Tobacco smoking-related methylations in these genes have been reported in the previous EWAS in blood samples. However, we did not observe that these methylations were associated with tobacco smoking in lung adenocarcinoma, although consistent association directions were observed for \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eHDG\u003c/span\u003e and \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eRPS6KA1\u003c/span\u003e (Data not shown). Notably, unlike the studies in large sample size from blood studies, the statistical analysis in detecting association between DNA methylation and tobacco smoking is still challenge in tumor tissues due to possible factors, such as tumor heterogeneity, potential confounders, and limited sample size. In fact, our focus on the analysis of the reported blood-based smoking-related DNA methylation sites could identify reliably smoking-related target genes and reduce the possibility of reverse causation. Nevertheless, given the tissue-specificities of some methylations in blood, further studies with a large sample size are still needed to replicate the associations for these candidate tobacco smoking-related genes in lung adenocarcinoma. In fact, our results showed that smoking-related methylations of these genes were associated with decreased expressions of these genes (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for all), indicating that they may play a down-regulation role in their gene expression in lung adenocarcinoma (Supplementary Fig.\u0026nbsp;3). Further \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ein vitro\u003c/span\u003e or \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ein vivo\u003c/span\u003e functional assays are needed to validate the genes that are affected by tobacco smoking in lung cancer.\u003c/p\u003e \u003cp\u003eIt is known that neoantigens (or neoepitopes) result from missense somatic mutations in cancer cells [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, how smoking-related mutational signature contribute to neoantigen loads remain unclear. We additionally evaluated the associations between smoking-related mutation signature and predicted neoantigen loads (see Materials and Methods). We observed that smoking-related mutational signature were significantly associated with increased neoantigen loads in three cancer types, head and neck, lung adenocarcinoma, and lung carcinoma (see Materials and Methods). An inverse association was observed in melanoma (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e for all; Supplementary Fig.\u0026nbsp;4A, B; Supplementary Table\u0026nbsp;6). The most significant association was observed in lung adenocarcinoma with a \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;\u0026lt;\u0026thinsp;2.2\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e. In addition, we also observed that neoantigen loads were associated with all five identified genes (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) and tobacco smoking (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;=\u0026thinsp;2.16\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e) in lung adenocarcinoma (Supplementary Fig.\u0026nbsp;4C, D). In particular, the expressions of \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAHRR\u003c/span\u003e and \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eGPR15\u003c/span\u003e had associations with an increased predicted neoantigen load with \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;=\u0026thinsp;7.6\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e and \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u0026thinsp;=\u0026thinsp;7.7\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e, respectively (Supplementary Fig.\u0026nbsp;4D). Thus, our findings may provide new clues to explore the biological and immunological mechanisms through which smoking-related mutational signature may be involved in carcinogenesis, and provide potential genomic biomarkers for the development of cancer prevention and immunotherapy.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eOur results showed that the smoking-altered DNA methylations and gene expressions play an important role in contributing to smoking-related mutational signature in human cancers. Our results also indicated that tobacco-smoking plays an important role in clinical significance, likely affecting genes with the impact on overall survival of lung cancer patients. Our findings have provided novel insights into the contributions of tobacco smoking to carcinogenesis, especially lung cancer, through complex molecular mechanisms of the elevated mutational signature by altered DNA methylations and gene expressions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAHRR, Aryl-Hydrocarbon Receptor Repressor\u003c/p\u003e\n\u003cp\u003eCAPN8, Calpain 8\u003c/p\u003e\n\u003cp\u003eEWAS, Epigenome-Wide Association Studies\u003c/p\u003e\n\u003cp\u003eGPR15, G Protein-Coupled Receptor 15\u003c/p\u003e\n\u003cp\u003eHDGF, Heparin Binding Growth Factor\u003c/p\u003e\n\u003cp\u003eHR, Hazard Ratio\u003c/p\u003e\n\u003cp\u003eRPS6KA1, Ribosomal Protein S6 Kinase A1\u003c/p\u003e\n\u003cp\u003eTCGA, The Cancer Genome Atlas\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank TCGA for providing valuable data resources for the research. We thank Marshal Younger for assistance with editing and manuscript preparation. The data analyses were conducted using the Advanced Computing Center for Research and Education (ACCRE) at Vanderbilt University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe normalized expressions of gene and DNA methylation were downloaded from the TCGA using the Broad Institute Genome Data Analysis Center (GDAC) Firehose portal through Firebrowse (stamp data/analyses__2016_01_28, \u003ca href=\"http://gdac.broadinstitute.org\"\u003ehttp://gdac.broadinstitute.org\u003c/a\u003e). Somatic mutational signatures were downloaded from mSignatureDB (\u003ca href=\"http://tardis.cgu.edu.tw/msignaturedb\"\u003ehttp://tardis.cgu.edu.tw/msignaturedb\u003c/a\u003e). Neoantigen data was downloaded from TCIA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the research development fund from Vanderbilt University Medical Center.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: XG; Acquisition of data and material support: XG and ZC; Analysis and interpretation of data: XG, ZC and WW; Generation of tables/figures: ZC; Writing, review, and/or revision of the manuscript: XG, ZC, WW, QC, JL, YW, WL, XS and WZ; Study supervision: XG. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflicts of interest were disclosed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGarraway LA, Lander ES: Lessons from the cancer genome. \u003cem\u003eCell \u003c/em\u003e2013, 153(1):17-37.\u003c/li\u003e\n\u003cli\u003eMartincorena I, Campbell PJ: Somatic mutation in cancer and normal cells. \u003cem\u003eScience \u003c/em\u003e2015, 349(6255):1483-1489.\u003c/li\u003e\n\u003cli\u003eAlexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SAJR, Behjati S, Biankin AV, Bignell GR, Bolli N, Borg A, 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\u003cem\u003eInt J Cancer \u003c/em\u003e2017, 140(1):50-61.\u003c/li\u003e\n\u003cli\u003eFasanelli F, Baglietto L, Ponzi E, Guida F, Campanella G, Johansson M, Grankvist K, Johansson M, Assumma MB, Naccarati A\u003cem\u003e et al\u003c/em\u003e: Hypomethylation of smoking-related genes is associated with future lung cancer in four prospective cohorts. \u003cem\u003eNat Commun \u003c/em\u003e2015, 6:10192.\u003c/li\u003e\n\u003cli\u003eHaarmann-Stemmann T, Bothe H, Kohli A, Sydlik U, Abel J, Fritsche E: Analysis of the transcriptional regulation and molecular function of the aryl hydrocarbon receptor repressor in human cell lines. \u003cem\u003eDrug Metab Dispos \u003c/em\u003e2007, 35(12):2262-2269.\u003c/li\u003e\n\u003cli\u003eMurray IA, Patterson AD, Perdew GH: Aryl hydrocarbon receptor ligands in cancer: friend and foe. \u003cem\u003eNat Rev Cancer \u003c/em\u003e2014, 14(12):801-814.\u003c/li\u003e\n\u003cli\u003eKim SV, Xiang WV, Kwak C, Yang Y, Lin XW, Ota M, Sarpel U, Rifkin DB, Xu R, Littman DR: GPR15-mediated homing controls immune homeostasis in the large intestine mucosa. \u003cem\u003eScience \u003c/em\u003e2013, 340(6139):1456-1459.\u003c/li\u003e\n\u003cli\u003eKoks S, Koks G: Activation of GPR15 and its involvement in the biological effects of smoking. \u003cem\u003eExp Biol Med (Maywood) \u003c/em\u003e2017, 242(11):1207-1212.\u003c/li\u003e\n\u003cli\u003eChen DS, Mellman I: Oncology Meets Immunology: The Cancer-Immunity Cycle. \u003cem\u003eImmunity \u003c/em\u003e2013, 39(1):1-10.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Data","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e: A collection of candidate blood-based methylations at CpG sites reported from five previous epigenome wide association studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 2\u003c/strong\u003e: A list of 374 candidate blood-based methylation CpG sites and genes identified from both discovery and replication studies, at an adjusted \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 3:\u003c/strong\u003e Associations between smoking-associated mutational signature and expression of candidate genes for each cancer type (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 4: \u003c/strong\u003eAssociations between tobacco smoking and expression of candidate genes and associations between tobacco smoking and methylation of candidate CpG sites.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 5: \u003c/strong\u003eAssociation between expression of candidate genes and methylation at each CpG site.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 6: \u003c/strong\u003eAssociations between smoking-associated mutational signature and predicted neoantigen load for each cancer type.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1: \u003c/strong\u003eThe epigenetic landscape of regions with methylations at three candidate CpG sites.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Figure 2: \u003c/strong\u003eGene expression of \u003cem\u003eHDGF\u003c/em\u003e and \u003cem\u003eRPS6KA1\u003c/em\u003e associated with overall survival of lung cancer patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Figure 3: \u003c/strong\u003eAssociations between gene expressions and methylations at three CpG sites.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Figure 4: \u003c/strong\u003eSmoking-related mutational signature contributed to neoantigen load in multiple cancer types.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"gene expression, methylation, tobacco smoking, mutational signature, mediation analysis","lastPublishedDoi":"10.21203/rs.2.21105/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.2.21105/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground \u003c/p\u003e\u003cp\u003eTobacco smoking is associated with a unique mutational signature in the human cancer genome. It is unclear whether tobacco smoking -altered DNA methylations and gene expressions affect smoking-related mutational signature.\u003c/p\u003e\u003cp\u003eMethods \u003c/p\u003e\u003cp\u003eWe evaluated the smoking-related DNA methylation sites reported from five previous studies using peripheral blood cells to identify possible target genes. Using the mediation analysis approach, we evaluated whether the association of tobacco smoking with mutational signature was mediated through altered DNA methylation and expression of these target genes.\u003c/p\u003e\u003cp\u003eResults \u003c/p\u003e\u003cp\u003eBased on data obtained from 21,108 blood samples, we identified 374 smoking-related DNA methylation sites, annotated to 248 target genes. Using data from DNA methylations, gene expressions and smoking-related mutational signature generated from ~7,700 tumor tissue samples across 26 cancer types from The Cancer Genome Atlas (TCGA), we found 11 of the 248 target genes whose expressions were associated with smoking-related mutational signature at a Bonferroni-correction P \u0026lt; 0.001. This included four for head and neck cancer, and seven for lung adenocarcinoma. In lung adenocarcinoma, our results showed that smoking increased the expression of three genes, AHRR , GPR15 , and HDGF, and decreased the expression of two genes, CAPN8 , and RPS6KA1 , which were consequently associated with increased smoking-related mutational signature. Additional evidence showed that the elevated expression of AHRR (cg14817490) and GPR15 (cg19859270), were associated with smoking-altered hypomethylations.\u0026nbsp;Lastly, we showed that the elevated expression of HDGF and decreases expression of RPS6KA1, were associated with poor survival of lung cancer patients.\u003c/p\u003e\u003cp\u003eConclusions \u003c/p\u003e\u003cp\u003eOur findings provide novel insights into the contributions of tobacco smoking to carcinogenesis through complex molecular mechanisms of the elevated mutational signature by altered DNA methylations and gene expressions.\u003c/p\u003e","manuscriptTitle":"From tobacco smoking to mutational signature: the role of epigenetic changes in human cancers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-01-17 20:14:02","doi":"10.21203/rs.2.21105/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ca7057e0-8e99-4b28-9f96-5853b9c5dddc","owner":[],"postedDate":"January 17th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50625,"name":"Cancer Biology"},{"id":50626,"name":"Oncology"}],"tags":[],"updatedAt":"","versionOfRecord":[],"versionCreatedAt":"2020-01-17 20:14:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-11703","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"identity":"rs-11703","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}