Smoking-related Genes Research Articles

Integrating Mendelian Randomization with Single-cell Sequencing Data Reveals the Causal Effect and Related Mechanisms of Smoking on Parkinson's Disease.

Smoking (nicotine) has been reported to possibly be neuroprotective and conducive to patients with early Parkinson's disease (PD). However, the causal effect of smoking on PD and the molecular mechanisms of smoking-related genes (SRGs) are vague. First, genome-wide association study summary data on smoking (ukb-b-6244) and PD (ieu-b-7) were retrieved from the Integrative Epidemiology Unit OpenGWAS database for Mendelian randomization (MR) analysis. Sensitivity analyses were performed to validate the results of the MR analyses. Subsequently, a differential analysis of PD patients and controls was performed to identify differentially expressed SRGs (DE-SRGs). Finally, the expression of DE-SRGs was analyzed in annotated cell types. The MR analysis revealed that smoking was a protective factor causally related to PD (P=0.008, odds ratio=0.288). Furthermore, a total of five DE-SRGs enriched in Toll-like receptor signaling pathways were identified in GSE7621 dataset. Regarding single-cell analysis of GSE184950 dataset, a total of nine cell types were annotated. The expression of LRRN1 in oligodendrocyte progenitor cells and oligodendrocytes, respectively, differed significantly between PD patients and controls. Our study supported a causal relationship between smoking and PD and found that five SRGs (MAPK8IP1, LRRN1, LINC00324, HIST1H2BK, and YOD1) enriched in Toll-like receptor signaling pathways might be beneficial in PD. In addition, single-cell sequencing indicated that four SRGs were differentially expressed in different cell types. All four genes except MAPK8IP1 were significantly correlated with the 10 genes calculated by scPagwas. Thus, this evidence provides a theoretical basis for further research on the effect of nicotine (smoking) on PD. In order to explore the potential etiology and pathogenesis of Parkinson's disease, this study combined Mendelian randomization, transcriptomics and single-cell sequencing analysis to explore the association between exposure factors and Parkinson's disease, observe and confirm the relationship and mechanism between the two from the perspective of genetics, and provide more reliable evidence for causal inference.

A multi-ancestry cerebral cortex transcriptome-wide association study identifies genes associated with smoking behaviors.

Transcriptome-wide association studies (TWAS) have provided valuable insight in identifying genes that may impact cigarette smoking. Most of previous studies, however, mainly focused on European ancestry. Limited TWAS studies have been conducted across multiple ancestries to explore genes that may impact smoking behaviors. In this study, we used cis-eQTL data of cerebral cortex from multiple ancestries in MetaBrain, including European, East Asian, and African samples, as reference panels to perform multi-ancestry TWAS analyses on ancestry-matched GWASs of four smoking behaviors including smoking initiation, smoking cessation, age of smoking initiation, and number of cigarettes per day in GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN). Multiple-ancestry fine-mapping approach was conducted to identify credible gene sets associated with these four traits. Enrichment and module network analyses were further performed to explore the potential roles of these identified gene sets. A total of 719 unique genes were identified to be associated with at least one of the four smoking traits across ancestries. Among those, 249 genes were further prioritized as putative causal genes in multiple ancestry-based fine-mapping approach. Several well-known smoking-related genes, including PSMA4, IREB2, and CHRNA3, showed high confidence across ancestries. Some novel genes, e.g., TSPAN3 and ANK2, were also identified in the credible sets. The enrichment analysis identified a series of critical pathways related to smoking such as synaptic transmission and glutamate receptor activity. Leveraging the power of the latest multi-ancestry GWAS and eQTL data sources, this study revealed hundreds of genes and relevant biological processes related to smoking behaviors. These findings provide new insights for future functional studies on smoking behaviors.

A novel prognostic signature based on smoking-associated genes for predicting prognosis and immune microenvironment in NSCLC smokers

BackgroundAs a highly heterogeneous tumor, non-small cell lung cancer (NSCLC) is famous for its high incidence and mortality worldwide. Smoking can cause genetic changes, which leading to the occurrence and progress of NSCLC. Nevertheless, the function of smoking-related genes in NSCLC needs more research.MethodsWe downloaded transcriptome data and clinicopathological parameters from Gene Expression Omnibus (GEO) databases, and screened smoking-related genes. Lasso regression were applied to establish the 7-gene signature. The associations between the 7-gene signature and immune microenvironment analysis, survival analysis, drug sensitivity analysis and enriched molecular pathways were studied. Ultimately, cell function experiments were conducted to research the function of FCGBP in NSCLC.ResultsThrough 7-gene signature, NSCLC samples were classified into high-risk group (HRG) and low-risk group (LRG). Significant difference in overall survival (OS) between HRG and LRG was found. Nomograms and ROC curves indicated that the 7-gene signature has a stable ability in predicting prognosis. Through the analysis of immune microenvironment, we found that LRG patients had better tumor immune activation. FCGBP showed the highest mutation frequency among the seven prognostic smoking related genes (LRRC31, HPGD, FCGBP, SPINK5, CYP24A1, S100P and FGG), and was notable down-regulated in NSCLC smokers compared with non-smoking NSCLC patients. The cell experiments confirmed that FCGBP knockdown promoting proliferation, migration, and invasion in NSCLC cells.ConclusionThis smoking-related prognostic signature represents a promising tool for assessing prognosis and tumor microenvironment in smokers with NSCLC. The role of FCGBP in NSCLC was found by cell experiments, which can be served as diagnostic biomarker and immunotherapy target for NSCLC.

Common immunological and prognostic features of lung and bladder cancer via smoking-related genes: PRR11 gene as potential immunotherapeutic target.

Smoking is a well-known risk factor for non-small-cell lung cancer (NSCLC) and bladder urothelial carcinoma (BLCA). Despite this, there has been no investigation into a prognostic marker based on smoking-related genes that could universally predict prognosis in these cancers and correlate with immune checkpoint therapy. This study aimed to identify smoking-related differential genes in NSCLC and BLCA, analyse their roles in patient prognosis and immune checkpoint therapy through subgroup analyses, and shed light on PRR11 as a crucial prognostic gene in both cancers. By examining PRR11 co-expressed genes, a prognostic model was constructed and its impact on immunotherapy for NSCLC and BLCA was evaluated. Molecular docking and tissue microarray analyses were conducted to explore the correlation between PRR11 and its reciprocal gene SPDL1. Additionally, miRNAs associated with PRR11 were analysed. The study confirmed a strong link between smoking-related genes, prognosis, and immune checkpoint therapy in NSCLC and BLCA. PRR11 was identified as a key smoking-associated gene that influences the efficacy of immune checkpoint therapy by modulating the stemness of these cancers. A prognostic model based on PRR11 co-expressed genes in BLCA was established and its prognostic value was validated in NSCLC. Furthermore, it was found that PRR11 regulates PDL1 via SPDL1, impacting immunotherapeutic efficacy in both cancers. The involvement of hsa-miR-200b-3p in the regulation of SPDL1 expression by PRR11 was also highlighted. Overall, the study elucidates that PRR11 modulates patient immunotherapy by influencing PDL1 expression through its interaction with SPDL1, with potential upstream regulation by hsa-miR-200b-3p.

A novel four‑gene biomarker for tobacco smoking -induced colorectal cancer progression.

Cigarette smoking greatly promotes the progression and poor prognosis of colorectal cancer (CRC) patients, with the molecular mechanism still not fully clear. In this study, CRC cells were exposed to tobacco specific nitrosamine 4‑(methylnitrosamino)‑1‑(3‑pyridyl) 1‑butanone (NNK), and the differentially expressed smoking-related genes were identified based on both NNK-induced CRC cells and a total of 763 CRC tissues from TCGA cohort. Cox regression analysis, ROC curve and Kaplan-Meier plot were used to establish the risk score model for CRC prognosis. Moreover, qRT-PCR, western blotting, colony formation, migration and invasion assays were performed to verify the core differentially expressed smoking-related gene and its molecular function in NNK-induced CRC progression. Results indicated NNK significantly enhanced CRC cell proliferation, migration and invasion. Moreover, a four-gene signature containing AKR1B10, CALB2, PLAC1, GNA15 was established as CRC prognosis marker. Among these four genes, AKR1B10 was further validated as the core gene, and its expression was significantly inhibited after NNK exposure in CRC cells. Results of gene enrichment analysis and western blotting suggested AKR1B10 might reduce the malignant progression of NNK-induced CRC cells through inhibiting Wnt signaling pathway by promoting E-Cadherin expression and inhibiting the expression of N-Cadherin, β-Catenin, Vimentin and Snail. In conclusion, a new four smoking-related genes can be jointly used as prognostic markers for CRC. AKR1B10 served as a tumor suppressor, can be used as a potential target to inhibit NNK-induced CRC malignant progression through regulating Wnt signaling pathway. This study demonstrates tobacco-derived NNK dependence would promote the malignant progression of colorectal cancer through regulating the expressions of AKR1B10/Wnt signaling pathway. And a novel four-gene signature is established for the prognosis prediction of smoking CRC patients. These findings have important translational implications given the continued use of tobacco and the difficulty in smoking cessation worldwide, which can be applied to alleviate the adverse effects induced by tobacco dependence on colorectal cancer patients.

Associations between polygenic risk scores and accelerated brain ageing in smokers.

Smoking contributes to a variety of neurodegenerative diseases and neurobiological abnormalities, suggesting that smoking is associated with accelerated brain aging. However, the neurobiological mechanisms affected by smoking, and whether they are genetically influenced, remain to be investigated. Using structural magnetic resonance imaging data from the UK Biobank (n = 33 293), a brain age predictor was trained on non-smoking healthy groups and tested on smokers to obtain the BrainAge Gap (BAG). The cumulative effect of multiple common genetic variants associated with smoking was then calculated to acquire a polygenic risk score (PRS). The relationship between PRS, BAG, total gray matter volume (tGMV), and smoking parameters was explored and further genes included in the PRS were annotated to identify potential molecular mechanisms affected by smoking. The BrainAge in smokers was predicted with very high accuracy (r = 0.725, MAE = 4.16). Smokers had a greater BAG (Cohen's d = 0.074, p < 0.0001) and higher PRS (Cohen's d = 0.63, p < 0.0001) than non-smokers. A higher PRS was associated with increased amount of smoking, mediated by BAG and tGMV. Several neurotransmitters and ion channel pathways were enriched in the group of smoking-related genes involved in addiction, brain synaptic plasticity, and some neurological disorders. By using a simplified single indicator of the entire brain (BAG) in combination with the PRS, this study highlights the greater BAG in smokers and its linkage with genes and smoking behavior, providing insight into the neurobiological underpinnings and potential features of smoking-related aging.

An integrative approach for identification of smoking-related genes involving bladder cancer.

Tobacco smoking is one of the most important environmental risk factors involving bladder tumorigenesis. However, smoking-related genes in bladder carcinogenesis and corresponding genetic effects on bladder cancer risk remain unclear. Weighted correlation network analysis (WGCNA) underlying transcriptome of bladder cancer tissues was applied to identify smoking-related genes. The logistic regression model was utilized to estimate genetic effects of single nucleotide polymorphisms (SNPs) in smoking-related genes on bladder cancer risk in the Chinese and European populations with a total of 6510 cases and 6569 controls, as well as the interaction with smoking status. Transcriptome of cells and tissues was used to profile the expression pattern of candidate genes and their genetic variants. Our results demonstrated that a total of 24 SNPs in 14 smoking-related genes were associated with the risk of bladder cancer, of which rs9348451 in CDKAL1 exhibited an interaction with smoking status (ORinteraction = 1.38, Pinteraction = 1.08 × 10-2) and tobaccosmoking might combine with CDKAL1 rs9348451 to increase the risk of bladder cancer (Ptrend = 4.27 × 10-4). Moreover, rs9348451 was associated with CDKAL1 expression in bladder cancer, especially in smokers (P < 0.001). Besides, CDKAL1 was upregulated in bladder cancer compared to normal adjacent tissues, as well as upregulated via treatment of cigarette smoke extracts. This study highlights the important role of nurture and nature, as well as their interaction on tumorigenesis, which provides a new way to decipher the etiology of bladder cancer with smoking status.

A Signature for Smoking Status of Coronary Heart Disease Patients through Weighted Gene Coexpression Network Analysis

Background Smoking is one of the risk factors of coronary heart disease (CHD), while its underlying mechanism is less well defined. Purpose To identify and testify 6 key genes of CHD related to smoking through weighted gene coexpression network analysis (WGCNA), protein-protein interaction (PPI) network analysis, and pathway analysis. Methods CHD patients' samples were first downloaded from Gene Expression Omnibus (GEO). Then, genes of interest were obtained after analysis of variance (ANOVA). Thereafter, 23 coexpressed modules that were determined after genes with similar expression were incorporated via WGCNA. The biological functions of genes in the modules were researched by enrichment analysis. Pearson correlation analysis and PPI network analysis were used to screen core genes related to smoking in CHD. Results The violet module was the most significantly associated with smoking (r = −0.28, p = 0.006). Genes in this module mainly participated in biological functions related to the heart. Altogether, 6 smoking-related core genes were identified through bioinformatics analyses. Their expressions in animal models were detected through the animal experiment. Conclusion This study identified 6 core genes to serve as underlying biomarkers for monitoring and predicting smoker's CHD risk.

Identification of SRXN1 and KRT6A as Key Genes in Smoking-Related Non-Small-Cell Lung Cancer Through Bioinformatics and Functional Analyses.

BackgroundLung cancer is the leading cause of cancer-related mortality worldwide. Although cigarette smoking is an established risk factor for lung cancer, few reliable smoking-related biomarkers for non-small-cell lung cancer (NSCLC) are available. An improved understanding of these biomarkers would further the development of new biomarker-targeted therapies and lead to improvements in overall patient survival.MethodsWe performed bioinformatic analysis to screened potential target genes, then quantitative PCR, western, siRNA, CCK-8, flow cytometry, tumorigenicity assays in nude mice were performed to validated the function.ResultsIn this study, we identified 83 smoking-related genes (SRGs) based on an integration analysis of two Gene Expression Omnibus (GEO) datasets, and 27 hub SRGs with potential carcinogenic effects by analyzing a dataset of smokers with NSCLC in The Cancer Genome Atlas (TCGA) database. A survival analysis revealed three genes with potential prognostic value, namely SRXN1, KRT6A and JAKMIP3. A univariate Cox analysis revealed significant associations of elevated SRXN1 and KRT6A expression with prognosis. A receiver operating characteristic (ROC) curve analysis indicated the high diagnostic value of SRXN1 and KRT6A for smoking and cancer. Quantitative PCR and western blotting validated the increased expression of SRXN1 and KRT6A mRNA and protein, respectively, in lung cancer cell lines and NSCLC tissues. In patients with NSCLC, SRXN1 and KRT6A expression was associated with the tumor–node–metastasis (TNM) stage, presence of metastasis, history of smoking and daily smoking consumption. Furthermore, inhibition of SRXN1 or KRT6A suppressed viability and enhanced apoptosis in the A549 human lung carcinoma cell line. Tumorigenicity assays in nude mice confirmed that the siRNA-mediated downregulation of SRXN1 and KRT6A expression inhibited tumor growth in vivo.ConclusionsIn summary, SRXN1 and KRT6A act as oncogenes in NSCLC and might be potential biomarkers of smoking exposure and the early diagnosis and prognosis of NSCLC in smokers, which is vital for lung cancer therapy.

Immune cell-specific smoking-related expression characteristics are revealed by re-analysis of transcriptomes from the CEDAR cohort

Smoking is known to affect whole-blood expression and methylation profiles. Although whole-genome methylation studies indicated that effects observed in blood may be driven by changes within leukocyte subtypes, these phenomena have not been explored using expression profiling. This study reanalyzed data from the Correlated Expression and Disease Association Research (CEDAR) patient cohort recruited by Momozawa et al. (E-MTAB-6667). Data from gene expression profiling of immunomagnetically sorted CD4+, CD8+, CD14+, CD15+, and CD19+ cells were processed. Differential expression analyses were conducted in each immune cell type, followed by gene ontology analysis and supplementary investigations. Ninety-four differentially expressed genes were found (CD8+ n = 58, CD14+ n = 20, CD4+ n = 14, CD19+ n = 2). Two key smoking-related genes were overexpressed in specific cell types: LRRN3 (CD4+, CD8+) and MMP25 (CD8+, CD14+). In CD4+ cells smoking was associated with reduced expression of the NK cell receptor KLRB1, suggesting CD4+ subpopulation shifts and differences in interferon signaling (reduced IRF1 and IL18RAP in smokers). Key results and their integration with an immune protein-protein interaction network revealed that smoking influences integrins in CD8+ cells (ITGB7, ITGAL, ITGAM, ITGB2). C-type lectin CLEC4A was reduced in CD8+ cells and CLEC10A was increased in CD14+ cells from smokers; moreover, CLEC5A (CD8+), CLEC7A (CD8+) and CLEC9A (CD19+) were related to smoking in supplementary analyses. CD14+ cells from smokers exhibited overexpression of LDLR and the formyl peptide receptor FPR3. Smoking specifically alters vital immune regulation genes in lymphocyte subtypes, especially CD4+, CD8+ and CD14+ cells.

Genome-wide methylation and expression analyses reveal the epigenetic landscape of immune-related diseases for tobacco smoking

BackgroundSmoking is a major causal risk factor for lung cancer, chronic obstructive pulmonary disease (COPD), cardiovascular disease (CVD), and is the main preventable cause of deaths in the world. The components of cigarette smoke are involved in immune and inflammatory processes, which may increase the prevalence of cigarette smoke-related diseases. However, the underlying molecular mechanisms linking smoking and diseases have not been well explored. This study was aimed to depict a global map of DNA methylation and gene expression changes induced by tobacco smoking and to explore the molecular mechanisms between smoking and human diseases through whole-genome bisulfite sequencing (WGBS) and RNA-sequencing (RNA-seq).ResultsWe performed WGBS on 72 samples (36 smokers and 36 nonsmokers) and RNA-seq on 75 samples (38 smokers and 37 nonsmokers), and cytokine immunoassay on plasma from 22 males (9 smokers and 13 nonsmokers) who were recruited from the city of Jincheng in China. By comparing the data of the two groups, we discovered a genome-wide methylation landscape of differentially methylated regions (DMRs) associated with smoking. Functional enrichment analyses revealed that both smoking-related hyper-DMR genes (DMGs) and hypo-DMGs were related to synapse-related pathways, whereas the hypo-DMGs were specifically related to cancer and addiction. The differentially expressed genes (DEGs) revealed by RNA-seq analysis were significantly enriched in the “immunosuppression” pathway. Correlation analysis of DMRs with their corresponding gene expression showed that genes affected by tobacco smoking were mostly related to immune system diseases. Finally, by comparing cytokine concentrations between smokers and nonsmokers, we found that vascular endothelial growth factor (VEGF) was significantly upregulated in smokers.ConclusionsIn sum, we found that smoking-induced DMRs have different distribution patterns in hypermethylated and hypomethylated areas between smokers and nonsmokers. We further identified and verified smoking-related DMGs and DEGs through multi-omics integration analysis of DNA methylome and transcriptome data. These findings provide us a comprehensive genomic map of the molecular changes induced by smoking which would enhance our understanding of the harms of smoking and its relationship with diseases.

The role of blood DNA methylation in smoking-related cancers

BACKGROUND AND AIM: Smoking is a well-known carcinogen consistently associated with differential DNA methylation in diverse populations. Dysregulations of DNA methylation have been associated with lung cancer. Smoking and lung cancer are common among many indigenous communities of the United States, leading to health disparities. This study investigated the potential mediating role of DNA methylation changes in the association of smoking with incident smoking-related cancers. METHODS: DNA methylation was measured in blood collected at baseline (1989-1991) using the EPIC 850K platform in 2351 American Indian participants of the Strong Heart Study. Cancer incidence was available through 2017 (97 cases for lung cancer, 222 cases for a combined smoking-related cancer endpoint including lung, esophageal, stomach, colorectal, liver, pancreatic and kidney cancers). Iterative Sure Independence Screening paired with adaptive elastic-net was used to select differentially methylated CpGs associated with cancer. Mediation analysis using Aalen additive hazard models was then applied to the selected CpGs. RESULTS:At total of 71 CpGs were associated with lung cancer and smoking-related cancer. For lung cancer, we found significant mediated effects for seven CpGs for current versus former or never smoking, and for eight CpGs for cumulative smoking. For smoking-related cancers, we found significant mediated associations for eight CpGs for current versus former or never smoking, and for one CpG for cumulative smoking. CpGs annotated to the AHRR and the PRSS23 genes (well-known smoking-related genes in DNA methylation studies), showed up to 70 % and up to 48 % of relative mediated effects, respectively, without accounting for multiple mediation. CONCLUSIONS:Our study provides strong evidence that a large extent of the association of smoking with smoking-related cancers, especially in lung cancer, can be explained by differential DNA methylation changes in well-known smoking related genes. Replication is ongoing in the Framingham Heart Study and the Rotterdam Study. KEYWORDS: Smoking, DNA methylation, cancer, American Indians

Genetic Variants in Smoking-Related Genes in Two Smoking Cessation Programs: A Cross-Sectional Study.

Previous studies have identified variants in genes encoding proteins associated with the degree of addiction, smoking onset, and cessation. We aimed to describe thirty-one single nucleotide polymorphisms (SNPs) in seven candidate genomic regions spanning six genes associated with tobacco-smoking in a cross-sectional study from two different interventions for quitting smoking: (1) thirty-eight smokers were recruited via multimedia to participate in e-Decídete! program (e-Dec) and (2) ninety-four attended an institutional smoking cessation program on-site. SNPs genotyping was done by real-time PCR using TaqMan probes. The analysis of alleles and genotypes was carried out using the EpiInfo v7. on-site subjects had more years smoking and tobacco index than e-Dec smokers (p < 0.05, both); in CYP2A6 we found differences in the rs28399433 (p < 0.01), the e-Dec group had a higher frequency of TT genotype (0.78 vs. 0.35), and TG genotype frequency was higher in the on-site group (0.63 vs. 0.18), same as GG genotype (0.03 vs. 0.02). Moreover, three SNPs in NRXN1, two in CHRNA3, and two in CHRNA5 had differences in genotype frequencies (p < 0.01). Cigarettes per day were different (p < 0.05) in the metabolizer classification by CYP2A6 alleles. In conclusion, subjects attending a mobile smoking cessation intervention smoked fewer cigarettes per day, by fewer years, and by fewer cumulative pack-years. There were differences in the genotype frequencies of SNPs in genes related to nicotine metabolism and nicotine dependence. Slow metabolizers smoked more cigarettes per day than intermediate and normal metabolizers.

Genetic Susceptibility to Pneumonia: A GWAS Meta-Analysis Between the UK Biobank and FinnGen.

Pneumonia is a respiratory condition with complex etiology. Host genetic variation is thought to contribute to individual differences in susceptibility and symptom manifestation. Here, we analyze pneumonia data from the UK Biobank (14,780 cases and 439,096 controls) and FinnGen (9980 cases and 86,519 controls) and perform a genomewide association study meta-analysis. We use gene-based tests, colocalization, genetic correlation, latent causal variable (LCV) and polygenic prediction in an independent Australian sample (N = 5595) to draw insights into the etiology of pneumonia risk. We identify two independent loci on chromosome 15 (lead single-nucleotide polymorphisms rs2009746 and rs76474922) to be associated with pneumonia (p < 5e-8). Gene-based tests revealed 18 genes in chromosomes 15, 16 and 9, including IL127, PBX3, ApoB receptor (APOBR) and smoking related genes CHRNA3/5, statistically associated with pneumonia. We observed genetic correlations between pneumonia and cardiorespiratory, psychiatric and inflammatory related traits. LCV analysis suggests a strong genetic causal relationship with cardiovascular health phenotypes. Polygenic risk scores for pneumonia significantly predicted self-reported pneumonia in an independent sample, albeit with a small effect size (OR = 1.1195% CI [1.04, 1.19], p<.05). Sensitivity analyses suggested the associations in chromosome 15 are mediated by smoking history, but the associations in chromosomes 16 and 9, and polygenic prediction were robust to adjustment for smoking. Altogether, our results highlight common genetic variants, genes and potential pathways that contribute to individual differences in susceptibility to pneumonia, and advance our understanding of the genetic factors underlying heterogeneity in respiratory medical outcomes.

Identification and validation of smoking-related genes in lung adenocarcinoma using an in vitro carcinogenesis model and bioinformatics analysis

BackgroundLung cancer is one of the most common carcinomas in the world, and lung adenocarcinoma (LUAD) is the most lethal and most common subtype of lung cancer. Cigarette smoking is the most leading risk factor of lung cancer, but it is still unclear how normal lung cells become cancerous in cigarette smokers. This study aims to identify potential smoking-related biomarkers associated with the progression and prognosis of LUAD, as well as their regulation mechanism using an in vitro carcinogenesis model and bioinformatics analysis.ResultsBased on the integration analysis of four Gene Expression Omnibus (GEO) datasets and our mRNA sequencing analysis, 2 up-regulated and 11 down-regulated genes were identified in both S30 cells and LUAD. By analyzing the LUAD dataset in The Cancer Gene Analysis (TCGA) database, 3 of the 13 genes, viz., glycophorin C (GYPC), NME/NM23 nucleoside diphosphate kinase 1 (NME1) and slit guidance ligand 2 (SLIT2), were found to be significantly correlated with LUAD patients’ smoking history. The expression levels of GYPC, NME1 and SLIT2 in S30 cells and lung cancer cell lines were validated by quantitative PCR, immunofluorescence, and western blot assays. Besides, these three genes are associated with tumor invasion depth, and elevated expression of NME1 was correlated with lymph node metastasis. The enrichment analysis suggested that these genes were highly correlated to tumorigenesis and metastasis-related biological processes and pathways. Moreover, the increased expression levels of GYPC and SLIT2, as well as decreased expression of NME1 were associated with a favorable prognosis in LUAD patients. Furthermore, based on the multi-omics data in the TCGA database, these genes were found to be regulated by DNA methylation.ConclusionIn conclusion, our observations indicated that the differential expression of GYPC, NME1 and SLIT2 may be regulated by DNA methylation, and they are associated with cigarette smoke-induced LUAD, as well as serve as prognostic factors in LUAD patients.

An Integrated Genomic Strategy to Identify CHRNB4 as a Diagnostic/Prognostic Biomarker for Targeted Therapy in Head and Neck Cancer.

Although many studies have shown the association between smoking and the increased incidence and adverse prognosis of head and neck squamous cell carcinoma (HNSCC), the mechanisms and pharmaceutical targets involved remain unclear. Here, we integrated gene expression signatures, genetic alterations, and survival analyses to identify prognostic indicators and therapeutic targets for smoking HNSCC patients, and we discovered that the FDA-approved drug varenicline inhibits the target for cancer cell migration/invasion. We first identified 18 smoking-related and prognostic genes for HNSCC by using RNA-Seq and clinical follow-up data. One of these genes, CHRNB4 (neuronal acetylcholine receptor subunit beta-4), increased the risk of death by approximately threefold in CHRNB4-high expression smokers compared to CHRNB4-low expression smokers (log rank, p = 0.00042; hazard ratio, 2.82; 95% CI, 1.55–5.14), former smokers, and non-smokers. Furthermore, we examined the functional enrichment of co-regulated genes of CHRNB4 and its 246 frequently occurring copy number alterations (CNAs). We found that these genes were involved in promoting angiogenesis, resisting cell death, and sustaining proliferation, and contributed to much worse outcomes for CHRNB4-high patients. Finally, we performed CHRNB4 gene editing and drug inhibition assays, and the results validate these observations. In summary, our study suggests that CHRNB4 is a prognostic indicator for smoking HNSCC patients and provides a potential new therapeutic drug to prevent recurrence or distant metastasis.

Gene network approach reveals co-expression patterns in nasal and bronchial epithelium

Nasal gene expression profiling is a new approach to investigate the airway epithelium as a biomarker to study the activity and treatment responses of obstructive pulmonary diseases. We investigated to what extent gene expression profiling of nasal brushings is similar to that of bronchial brushings. We performed genome wide gene expression profiling on matched nasal and bronchial epithelial brushes from 77 respiratory healthy individuals. To investigate differences and similarities among regulatory modules, network analysis was performed on correlated, differentially expressed and smoking-related genes using Gaussian Graphical Models. Between nasal and bronchial brushes, 619 genes were correlated and 1692 genes were differentially expressed (false discovery rate <0.05, |Fold-change|>2). Network analysis of correlated genes showed pro-inflammatory pathways to be similar between the two locations. Focusing on smoking-related genes, cytochrome-P450 pathway related genes were found to be similar, supporting the concept of a detoxifying response to tobacco exposure throughout the airways. In contrast, cilia-related pathways were decreased in nasal compared to bronchial brushes when focusing on differentially expressed genes. Collectively, while there are substantial differences in gene expression between nasal and bronchial brushes, we also found similarities, especially in the response to the external factors such as smoking.

Abstract 139: Nicotinic Acetylcholine Receptor Genes Contribute to Leptin Resistance

Background: The mechanisms of leptin resistance among overweight/obese individuals in not fully understood. Smokers have lower body mass index (BMI) compared to non-smokers, and quit smoking results in weight gain. Therefore, we aimed to evaluate whether smoking related genes contribute to leptin resistance. Methods: A total of 541 SNPs in seven genes of the nicotinic acetylcholine receptors family ( CHRNB2-B4, CHRNA3-A6 ) were evaluated for interactions with leptin on BMI among 748 European American participants of the Multi-Ethnic Study of Atherosclerosis (MESA). Significant SNPs were evaluated for replication among 3,844 participants of the Framingham Heart Study (FHS) 3 rd generation cohort. Multivariate linear regressions were used in single SNP-based analyses controlling for age and sex. Gene-based analyses were performed using the GATES method. MESA and FHS results were combined through meta-analysis methods. Results: After Bonferroni correction, a total of 34 SNPs within genes CHRNA3 , CHRNA5 , CHRNA6 , CHRNB2, and CHRNB3 significantly ( P &lt;8.20х10 -4 , correcting for 61 linkage disequilibrium blocks) interacted with leptin on BMI, of which, 23 SNPs reached genome-wide significance ( P &lt;5.00х10 -8 ), and 34 SNPs were successfully replicated in the FHS. CHRNB2 rs2072661 (minor allele frequency=0.24) was most significant with a major G allele negatively interacting with leptin on BMI (MESA: β=-0.05, p=8.20х10 -4 ; FHS: β=-0.05, p=1.31х10 -11 ; Meta: β=-0.05, p=4.19х10 -16 ), indicating that individuals with the major alleles were less resistant to leptin. Meanwhile, gene-based analysis revealed that the seven genes were all significantly interacting with leptin on BMI with meta-analysis p values ranging from 2.62х10 -13 for CHRNB2 to 4.25х10 -5 for CHRNA4 . Conclusion: Nicotine acetylcholine receptor genes modify the sensitivity of leptin on BMI.

RNA-sequencing across three matched tissues reveals shared and tissue-specific gene expression and pathway signatures of COPD

BackgroundMultiple gene expression studies have been performed separately in peripheral blood, lung, and airway tissues to study COPD. We performed RNA-sequencing gene expression profiling of large-airway epithelium, alveolar macrophage and peripheral blood samples from the same subset of COPD cases and controls from the COPDGene study who underwent bronchoscopy at a single center. Using statistical and gene set enrichment approaches, we sought to improve the understanding of COPD by studying gene sets and pathways across these tissues, beyond the individual genomic determinants.MethodsWe performed differential expression analysis using RNA-seq data obtained from 63 samples from 21 COPD cases and controls (includes four non-smokers) via the R package DESeq2. We tested associations between gene expression and variables related to lung function, smoking history, and CT scan measures of emphysema and airway disease. We examined the correlation of differential gene expression across the tissues and phenotypes, hypothesizing that this would reveal preserved and private gene expression signatures. We performed gene set enrichment analyses using curated databases and findings from prior COPD studies to provide biological and disease relevance.ResultsThe known smoking-related genes CYP1B1 and AHRR were among the top differential expression results for smoking status in the large-airway epithelium data. We observed a significant overlap of genes primarily across large-airway and macrophage results for smoking and airway disease phenotypes. We did not observe specific genes differentially expressed in all three tissues for any of the phenotypes. However, we did observe hemostasis and immune signaling pathways in the overlaps across all three tissues for emphysema, and amyloid and telomere-related pathways for smoking. In peripheral blood, the emphysema results were enriched for B cell related genes previously identified in lung tissue studies.ConclusionsOur integrative analyses across COPD-relevant tissues and prior studies revealed shared and tissue-specific disease biology. These replicated and novel findings in the airway and peripheral blood have highlighted candidate genes and pathways for COPD pathogenesis.

Epigenome-wide association study of serum cotinine in current smokers reveals novel genetically driven loci

BackgroundDNA methylation alteration extensively associates with smoking and is a plausible link between smoking and adverse health. We examined the association between epigenome-wide DNA methylation and serum cotinine levels as a proxy of nicotine exposure and smoking quantity, assessed the role of SNPs in these associations, and evaluated molecular mediation by methylation in a sample of biochemically verified current smokers (N = 310).ResultsDNA methylation at 50 CpG sites was associated (FDR < 0.05) with cotinine levels, 17 of which are novel associations. As cotinine levels are influenced not only by nicotine intake but also by CYP2A6-mediated nicotine metabolism rate, we performed secondary analyses adjusting for genetic risk score of nicotine metabolism rate and identified five additional novel associations. We further assessed the potential role of genetic variants in the detected association between methylation and cotinine levels observing 124 cis and 3898 trans methylation quantitative trait loci (meQTLs). Nineteen of these SNPs were also associated with cotinine levels (FDR < 0.05). Further, at seven CpG sites, we observed a trend (P < 0.05) that altered DNA methylation mediates the effect of SNPs on nicotine exposure rather than a direct consequence of smoking. Finally, we performed replication of our findings in two independent cohorts of biochemically verified smokers (N = 450 and N = 79).ConclusionsUsing cotinine, a biomarker of nicotine exposure, we replicated and extended identification of novel epigenetic associations in smoking-related genes. We also demonstrated that DNA methylation in some of the identified loci is driven by the underlying genotype and may mediate the causal effect of genotype on cotinine levels.