Pathogenic mechanisms of lung adenocarcinoma in smokers and non-smokers determined by gene expression interrogation

A nested case-control study of untargeted plasma metabolomics and lung cancer among never-smoking women within the prospective Shanghai Women's Health Study

The etiology of lung cancer in never-smokers remains elusive, despite 15% of lung cancer cases in men and 53% in women worldwide being unrelated to smoking. Here, we aimed to enhance our understanding of lung cancer pathogenesis among never-smokers using untargeted metabolomics. This nested case-control study included 395 never-smoking women who developed lung cancer and 395 matched never-smoking cancer-free women from the prospective Shanghai Women's Health Study with 15,353 metabolic features quantified in pre-diagnostic plasma using liquid chromatography high-resolution mass spectrometry. Recognizing that metabolites often correlate and seldom act independently in biological processes, we utilized a weighted correlation network analysis to agnostically construct 28 network modules of correlated metabolites. Using conditional logistic regression models, we assessed the associations for both metabolic network modules and individual metabolic features with lung cancer, accounting for multiple testing using a false discovery rate (FDR) < 0.20. We identified a network module of 121 features inversely associated with all lung cancer (p = .001, FDR = 0.028) and lung adenocarcinoma (p = .002, FDR = 0.056), where lyso-glycerophospholipids played a key role driving these associations. Another module of 440 features was inversely associated with lung adenocarcinoma (p = .014, FDR = 0.196). Individual metabolites within these network modules were enriched in biological pathways linked to oxidative stress, and energy metabolism. These pathways have been implicated in previous metabolomics studies involving populations exposed to known lung cancer risk factors such as traffic-related air pollution and polycyclic aromatic hydrocarbons. Our results suggest that untargeted plasma metabolomics could provide novel insights into the etiology and risk factors of lung cancer among never-smokers.

Uncovering molecular features driving lung adenocarcinoma heterogeneity in patients who formerly smoked

BACKGROUND

An increasing proportion of lung adenocarcinoma (LUAD) occurs in patients even after they have stopped smoking. Here, we aimed to determine whether tobacco smoking induced changes across LUADs from patients who formerly smoked correspond to different biological and clinical factors.

METHODS

Random forest models (RFs) were trained utilizing a smoking associated signature developed from differentially expressed genes between LUAD patients who had never smoked (NS) or currently smoked (CS) from TCGA (n = 193) and BCCA (n = 69) cohorts. The RFs were subsequently applied to 299 and 131 formerly smoking patients from TCGA and MSKCC cohorts, respectively. FS were RF-classified as either CS-like or NS-like and associations with patient characteristics, biological features, and clinical outcomes were determined.

RESULTS

We elucidated a 123 gene signature that robustly classified NS and CS in both RNA-seq (AUC = 0.85) and microarray (AUC = 0.92) validation test sets. The RF classified 213 patients who had formerly smoked as CS-like and 86 as NS-like from the TCGA cohort. CS-like and NS-like status in formerly smoking patients correlated poorly with patient characteristics but had substantially different biological features including tumor mutational burden, number of mutations, mutagenic signatures and immune cell populations. NS-like formerly smoking patients had 17.5 months and 18.6 months longer overall survival than CS-like patients from the TCGA and MSKCC cohorts, respectively.

CONCLUSIONS

Patients who had formerly smoked with LUAD harbor heterogeneous tumor biology. These patients can be divided by smoking induced gene expression to inform prognosis and underlying biological characteristics for treatment selection.

Identifying lncRNAs and mRNAs related to survival of NSCLC based on bioinformatic analysis and machine learning

Non-small cell lung cancer (NSCLC) is the most common histopathological type, and it is purposeful for screening potential prognostic biomarkers for NSCLC. This study aims to identify the lncRNAs and mRNAs related to survival of non-small cell lung cancer (NSCLC). The expression profile data of lung adenocarcinoma and lung squamous cell carcinoma were downloaded in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset. A total of eight survival related long non-coding RNAs (lncRNAs) and 262 survival related mRNAs were filtered. By gene set enrichment analysis, 17 significantly correlated Gene Ontology signal pathways and 14 Kyoto Encyclopedia of Genes and Genomes signal pathways were screened. Based on the clinical survival and prognosis information of the samples, we screened eight lncRNAs and 193 mRNAs by single factor Cox regression analysis. Further single and multifactor Cox regression analysis were performed, 30 independent prognostication-related mRNAs were obtained. The PPI network was further constructed. We then performed the machine learning algorithms (Least absolute shrinkage and selection operator, Recursive feature elimination, and Random forest) to screen the optimized DEGs combination, and a total of 17 overlapping mRNAs were obtained. Based on the 17 characteristic mRNAs obtained, we firstly built a Nomogram prediction model, and the ROC values of training set and testing set were 0.835 and 0.767, respectively. By overlapping the 17 characteristic mRNAs and PPI network hub genes, three genes were obtained: CDC6, CEP55, TYMS, which were considered as key factors associated with survival of NSCLC. The in vitro experiments were performed to examine the effect of CDC6, CEP55, and TYMS on NSCLC cells. Finally, the lncRNAs-mRNAs networks were constructed.

Combined model integrating deep learning, radiomics, and clinical data to classify lung nodules at chest CT

OBJECTIVES

The study aimed to develop a combined model that integrates deep learning (DL), radiomics, and clinical data to classify lung nodules into benign or malignant categories, and to further classify lung nodules into different pathological subtypes and Lung Imaging Reporting and Data System (Lung-RADS) scores.

MATERIALS AND METHODS

The proposed model was trained, validated, and tested using three datasets: one public dataset, the Lung Nodule Analysis 2016 (LUNA16) Grand challenge dataset (n = 1004), and two private datasets, the Lung Nodule Received Operation (LNOP) dataset (n = 1027) and the Lung Nodule in Health Examination (LNHE) dataset (n = 1525). The proposed model used a stacked ensemble model by employing a machine learning (ML) approach with an AutoGluon-Tabular classifier. The input variables were modified 3D convolutional neural network (CNN) features, radiomics features, and clinical features. Three classification tasks were performed: Task 1: Classification of lung nodules into benign or malignant in the LUNA16 dataset; Task 2: Classification of lung nodules into different pathological subtypes; and Task 3: Classification of Lung-RADS score. Classification performance was determined based on accuracy, recall, precision, and F1-score. Ten-fold cross-validation was applied to each task.

RESULTS

The proposed model achieved high accuracy in classifying lung nodules into benign or malignant categories in LUNA 16 with an accuracy of 92.8%, as well as in classifying lung nodules into different pathological subtypes with an F1-score of 75.5% and Lung-RADS scores with an F1-score of 80.4%.

CONCLUSION

Our proposed model provides an accurate classification of lung nodules based on the benign/malignant, different pathological subtypes, and Lung-RADS system.

Integrative, multi-omics, analysis of blood samples improves model predictions: applications to cancer

Background

Cancer genomic studies often include data collected from several omics platforms. Each omics data source contributes to the understanding of the underlying biological process via source specific (“individual”) patterns of variability. At the same time, statistical associations and potential interactions among the different data sources can reveal signals from common biological processes that might not be identified by single source analyses. These common patterns of variability are referred to as “shared” or “joint”. In this work, we show how the use of joint and individual components can lead to better predictive models, and to a deeper understanding of the biological process at hand. We identify joint and individual contributions of DNA methylation, miRNA and mRNA expression collected from blood samples in a lung cancer case–control study nested within the Norwegian Women and Cancer (NOWAC) cohort study, and we use such components to build prediction models for case–control and metastatic status. To assess the quality of predictions, we compare models based on simultaneous, integrative analysis of multi-source omics data to a standard non-integrative analysis of each single omics dataset, and to penalized regression models. Additionally, we apply the proposed approach to a breast cancer dataset from The Cancer Genome Atlas.

Results

Our results show how an integrative analysis that preserves both components of variation is more appropriate than standard multi-omics analyses that are not based on such a distinction. Both joint and individual components are shown to contribute to a better quality of model predictions, and facilitate the interpretation of the underlying biological processes in lung cancer development.

Conclusions

In the presence of multiple omics data sources, we recommend the use of data integration techniques that preserve the joint and individual components across the omics sources. We show how the inclusion of such components increases the quality of model predictions of clinical outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04296-0.

Deep learning classification of lung cancer histology using CT images

Tumor histology is an important predictor of therapeutic response and outcomes in lung cancer. Tissue sampling for pathologist review is the most reliable method for histology classification, however, recent advances in deep learning for medical image analysis allude to the utility of radiologic data in further describing disease characteristics and for risk stratification. In this study, we propose a radiomics approach to predicting non-small cell lung cancer (NSCLC) tumor histology from non-invasive standard-of-care computed tomography (CT) data. We trained and validated convolutional neural networks (CNNs) on a dataset comprising 311 early-stage NSCLC patients receiving surgical treatment at Massachusetts General Hospital (MGH), with a focus on the two most common histological types: adenocarcinoma (ADC) and Squamous Cell Carcinoma (SCC). The CNNs were able to predict tumor histology with an AUC of 0.71(p = 0.018). We also found that using machine learning classifiers such as k-nearest neighbors (kNN) and support vector machine (SVM) on CNN-derived quantitative radiomics features yielded comparable discriminative performance, with AUC of up to 0.71 (p = 0.017). Our best performing CNN functioned as a robust probabilistic classifier in heterogeneous test sets, with qualitatively interpretable visual explanations to its predictions. Deep learning based radiomics can identify histological phenotypes in lung cancer. It has the potential to augment existing approaches and serve as a corrective aid for diagnosticians.

The role of viruses in adenocarcinoma development

Cancer is a leading public health issue that accounts for million deaths around the world every year. Human cancers contain over 100 types, which are categorized into different groups. Adenocarcinoma is one of those categories of cancer that begins from the glans and involves various tissues such as lung, esophagus, pancreas, prostate and colorectal. A range of risk factors has been identified for the development and progression of adenocarcinomas. One of these risk factors are viruses that serves special mechanisms to affect important host cell factors and tumorigenic pathways, contributing in development and promotion of adenocarcinomas. Here, we summarized the main viruses and their mechanisms implicated in the course of various adenocarcinomas development.

Analysis of genomic and transcriptomic variations as prognostic signature for lung adenocarcinoma

Background

Lung cancer is the leading cause of the largest number of deaths worldwide and lung adenocarcinoma is the most common form of lung cancer. In order to understand the molecular basis of lung adenocarcinoma, integrative analysis have been performed by using genomics, transcriptomics, epigenomics, proteomics and clinical data. Besides, molecular prognostic signatures have been generated for lung adenocarcinoma by using gene expression levels in tumor samples. However, we need signatures including different types of molecular data, even cohort or patient-based biomarkers which are the candidates of molecular targeting.

Results

We built an R pipeline to carry out an integrated meta-analysis of the genomic alterations including single-nucleotide variations and the copy number variations, transcriptomics variations through RNA-seq and clinical data of patients with lung adenocarcinoma in The Cancer Genome Atlas project. We integrated significant genes including single-nucleotide variations or the copy number variations, differentially expressed genes and those in active subnetworks to construct a prognosis signature. Cox proportional hazards model with Lasso penalty and LOOCV was used to identify best gene signature among different gene categories.

We determined a 12-gene signature (BCHE, CCNA1, CYP24A1, DEPTOR, MASP2, MGLL, MYO1A, PODXL2, RAPGEF3, SGK2, TNNI2, ZBTB16) for prognostic risk prediction based on overall survival time of the patients with lung adenocarcinoma. The patients in both training and test data were clustered into high-risk and low-risk groups by using risk scores of the patients calculated based on selected gene signature. The overall survival probability of these risk groups was highly significantly different for both training and test datasets.

Conclusions

This 12-gene signature could predict the prognostic risk of the patients with lung adenocarcinoma in TCGA and they are potential predictors for the survival-based risk clustering of the patients with lung adenocarcinoma. These genes can be used to cluster patients based on molecular nature and the best candidates of drugs for the patient clusters can be proposed. These genes also have a high potential for targeted cancer therapy of patients with lung adenocarcinoma.

Identification of key gene modules and hub genes of human mantle cell lymphoma by coexpression network analysis

Purpose

Mantle cell lymphoma (MCL) is a rare and aggressive subtype of non-Hodgkin lymphoma that is incurable with standard therapies. The use of gene expression analysis has been of interest, recently, to detect biomarkers for cancer. There is a great need for systemic coexpression network analysis of MCL and this study aims to establish a gene coexpression network to forecast key genes related to the pathogenesis and prognosis of MCL.

Methods

The microarray dataset GSE93291 was downloaded from the Gene Expression Omnibus database. We systematically identified coexpression modules using the weighted gene coexpression network analysis method (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis were performed on the modules deemed important. The protein-protein interaction networks were constructed and visualized using Cytoscape software on the basis of the STRING website; the hub genes in the top weighted network were identified. Survival data were analyzed using the Kaplan-Meier method and were compared using the log-rank test.

Results

Seven coexpression modules consisting of different genes were applied to 5,000 genes in the 121 human MCL samples using WGCNA software. GO and KEGG enrichment analysis identified the blue module as one of the most important modules; the most critical pathways identified were the ribosome, oxidative phosphorylation and proteasome pathways. The hub genes in the top weighted network were regarded as real hub genes (IL2RB, CD3D, RPL26L1, POLR2K, KIF11, CDC20, CCNB1, CCNA2, PUF60, SNRNP70, AKT1 and PRPF40A). Survival analysis revealed that seven genes (KIF11, CDC20, CCNB1, CCNA2, PRPF40A, CD3D and PUF60) were associated with overall survival time (p < 0.05).

Conclusions

The blue module may play a vital role in the pathogenesis of MCL. Five real hub genes (KIF11, CDC20, CCNB1, CCNA2 and PUF60) were identified as potential prognostic biomarkers as well as therapeutic targets with clinical utility for MCL.

Water-soluble variant of human Lynx1 induces cell cycle arrest and apoptosis in lung cancer cells via modulation of α7 nicotinic acetylcholine receptors

Lynx1 is the first three-finger prototoxin found in the mammalian central nervous system. It is a GPI-anchored protein modulating nicotinic acetylcholine receptors (nAChRs) in the brain. Besides the brain, the Lynx1 protein was found in the lung and kidney. Endogenous Lynx1 controls the nicotine-induced up-regulation of the expression of α7 type nAChRs in lung adenocarcinoma A549 cells as well as the cell growth. Here, we analyzed the Lynx1 expression in the set of human epithelial cells. The Lynx1 expression both at the mRNA and protein level was detected in normal oral keratinocytes, and lung, colon, epidermal, and breast cancer cells, but not in embryonic kidney cells. Co-localization of Lynx1 with α7-nAChRs was revealed in a cell membrane for lung adenocarcinoma A549 and colon carcinoma HT-29 cells, but not for breast adenocarcinoma MCF-7 and epidermoid carcinoma A431 cells. The recombinant water-soluble variant of Lynx1 without a GPI-anchor (ws-Lynx1) inhibited the growth of A549 cells causing cell cycle arrest via modulation of α7-nAChRs and activation of different intracellular signaling cascades, including PKC/IP₃, MAP/ERK, p38, and JNK pathways. A549 cells treatment with ws-Lynx1 resulted in phosphorylation of the proapoptotic tumor suppressor protein p53 and different kinases participated in the regulation of gene transcription, cell growth, adhesion, and differentiation. Externalization of phosphatidylserine, an early apoptosis marker, observed by flow cytometry, confirmed the induction of apoptosis in A549 cells upon the ws-Lynx1 treatment. Our data revealed the ability of ws-Lynx1 to regulate homeostasis of epithelial cancer cells.

Genomic analyses based on pulmonary adenocarcinoma in situ reveal early lung cancer signature

BACKGROUND

Non-small cell lung cancer (NSCLC) represents more than about 80% of the lung cancer. The early stages of NSCLC can be treated with complete resection with a good prognosis. However, most cases are detected at late stage of the disease. The average survival rate of the patients with invasive lung cancer is only about 4%. Adenocarcinoma in situ (AIS) is an intermediate subtype of lung adenocarcinoma that exhibits early stage growth patterns but can develop into invasion.

METHODS

In this study, we used RNA-seq data from normal, AIS, and invasive lung cancer tissues to identify a gene module that represents the distinguishing characteristics of AIS as AIS-specific genes. Two differential expression analysis algorithms were employed to identify the AIS-specific genes. Then, the subset of the best performed AIS-specific genes for the early lung cancer prediction were selected by random forest. Finally, the performances of the early lung cancer prediction were assessed using random forest, support vector machine (SVM) and artificial neural networks (ANNs) on four independent early lung cancer datasets including one tumor-educated blood platelets (TEPs) dataset.

RESULTS

Based on the differential expression analysis, 107 AIS-specific genes that consisted of 93 protein-coding genes and 14 long non-coding RNAs (lncRNAs) were identified. The significant functions associated with these genes include angiogenesis and ECM-receptor interaction, which are highly related to cancer development and contribute to the smoking-free lung cancers. Moreover, 12 of the AIS-specific lncRNAs are involved in lung cancer progression by potentially regulating the ECM-receptor interaction pathway. The feature selection by random forest identified 20 of the AIS-specific genes as early stage lung cancer signatures using the dataset obtained from The Cancer Genome Atlas (TCGA) lung adenocarcinoma samples. Of the 20 signatures, two were lncRNAs, BLACAT1 and CTD-2527I21.15 which have been reported to be associated with bladder cancer, colorectal cancer and breast cancer. In blind classification for three independent tissue sample datasets, these signature genes consistently yielded about 98% accuracy for distinguishing early stage lung cancer from normal cases. However, the prediction accuracy for the blood platelets samples was only 64.35% (sensitivity 78.1%, specificity 50.59%, and AUROC 0.747).

CONCLUSIONS

The comparison of AIS with normal and invasive tumor revealed diseases-specific genes and offered new insights into the mechanism underlying AIS progression into an invasive tumor. These genes can also serve as the signatures for early diagnosis of lung cancer with high accuracy. The expression profile of gene signatures identified from tissue cancer samples yielded remarkable early cancer prediction for tissues samples, however, relatively lower accuracy for boold platelets samples.

DNA methylation and associated gene expression in blood prior to lung cancer diagnosis in the Norwegian Women and Cancer cohort

The majority of lung cancer is caused by tobacco smoking, and lung cancer-relevant epigenetic markers have been identified in relation to smoking exposure. Still, smoking-related markers appear to mediate little of the effect of smoking on lung cancer. Thus in order to identify disease-relevant markers and enhance our understanding of pathways, a wide search is warranted. Through an epigenome-wide search within a case-control study (131 cases, 129 controls) nested in a Norwegian prospective cohort of women, we found 25 CpG sites associated with lung cancer. Twenty-three were classified as associated with smoking (LC-AwS), and two were classified as unassociated with smoking (LC-non-AwS), as they remained associated with lung cancer after stringent adjustment for smoking exposure using the comprehensive smoking index (CSI): cg10151248 (PC, CSI-adjusted odds ratio (OR) = 0.34 [0.23-0.52] per standard deviation change in methylation) and cg13482620 (B3GNTL1, CSI-adjusted OR = 0.33 [0.22-0.50]). Analysis among never smokers and a cohort of smoking-discordant twins confirmed the classification of the two LC-non-AwS CpG sites. Gene expression profiles demonstrated that the LC-AwS CpG sites had different enriched pathways than LC-non-AwS sites. In conclusion, using blood-derived DNA methylation and gene expression profiles from a prospective lung cancer case-control study in women, we identified 25 CpG lung cancer markers prior to diagnosis, two of which were LC-non-AwS markers and related to distinct pathways.

Chronic exposure to cigarette smoke leads to activation of p21 (RAC1)-activated kinase 6 (PAK6) in non-small cell lung cancer cells

Epidemiological data clearly establishes cigarette smoking as one of the major cause for lung cancer worldwide. Recently, targeted therapy has become one of the most preferred modes of treatment for cancer. Though certain targeted therapies such as anti-EGFR are in clinical practice, they have shown limited success in lung cancer patients who are smokers. This demands discovery of alternative drug targets through systematic investigation of cigarette smoke-induced signaling mechanisms. To study the signaling events activated in response to cigarette smoke, we carried out SILAC-based phosphoproteomic analysis of H358 lung cancer cells chronically exposed to cigarette smoke. We identified 1,812 phosphosites, of which 278 phosphosites were hyperphosphorylated (≥ 3-fold) in H358 cells chronically exposed to cigarette smoke. Our data revealed hyperphosphorylation of S560 within the conserved kinase domain of PAK6. Activation of PAK6 is associated with various processes in cancer including metastasis. Mechanistic studies revealed that inhibition of PAK6 led to reduction in cell proliferation, migration and invasion of the cigarette smoke treated cells. Further, siRNA mediated silencing of PAK6 resulted in decreased invasive abilities in a panel of non-small cell lung cancer (NSCLC) cells. Consistently, mice bearing tumor xenograft showed reduced tumor growth upon treatment with PF-3758309 (group II PAK inhibitor). Immunohistochemical analysis revealed overexpression of PAK6 in 66.6% (52/78) of NSCLC cases in tissue microarrays. Taken together, our study indicates that PAK6 is a promising novel therapeutic target for NSCLC, especially in smokers.

Elevated mRNA Levels of AURKA, CDC20 and TPX2 are associated with poor prognosis of smoking related lung adenocarcinoma using bioinformatics analysis

Background and aim: Adenocarcinoma is a very common pathological subtype for lung cancer. We aimed to identify the gene signature associated with the prognosis of smoking related lung adenocarcinoma using bioinformatics analysis. Methods: A total of five gene expression profiles (GSE31210, GSE32863, GSE40791, GSE43458 and GSE75037) have been identified from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were analyzed using GEO2R software and functional and pathway enrichment analysis. Furthermore, the overall survival (OS) and recurrence-free survival (RFS) have been validated using an independent cohort from the Cancer Genome Atlas (TCGA) database. Results: We identified a total of 58 DEGs which mainly enriched in ECM-receptor interaction, platelet activation and PPAR signaling pathway. Then according to the enrichment analysis results, we selected three genes (AURKA, CDC20 and TPX2) for their roles in regulating tumor cell cycle and cell division. The results showed that the hazard ratio (HR) of the mRNA expression of AURKA for OS was 1.588 with (1.127-2.237) 95% confidence interval (CI) (P=0.009). The mRNA levels of CDC20 (HR 1.530, 95% CI 1.086-2.115, P=0.016) and TPX2 (HR 1.777, 95%CI 1.262-2.503, P=0.001) were also significantly associated with the OS. Expression of these three genes were not associated with RFS, suggesting that there might be many factors affect RFS. Conclusion: The mRNA signature of AURKA, CDC20 and TPX2 were potential biomarkers for predicting poor prognosis of smoking related lung adenocarcinoma.

Divergent trends in lung cancer incidence by gender, age and histological type in Estonia: a nationwide population-based study

Background

Lung cancer (LC) is the leading cause of cancer deaths in men and the second most frequent cause of cancer deaths in women in Estonia. The study aimed to analyze time trends in LC incidence and mortality in Estonia over the 30-year period, which included major social, economic and health care transition. The results are discussed in the context of changes in tobacco control and smoking prevalence. Long-term predictions of incidence and mortality are provided.

Methods

Data for calculating the incidence and mortality rates in 1985–2014 were obtained from the nationwide population-based Estonian Cancer Registry and the Causes of Death Registry. Joinpoint regression was used to analyze trends and estimate annual percentage change (APC) with 95% confidence interval (CI). Nordpred model was used to project future incidence and mortality trends for 2015–2034.

Results

Incidence peaked among men in 1991 and decreased thereafter (APC: -1.5, 95% CI: -1.8; −1.3). A decline was seen for all age groups, except age ≥ 75 years, and for all histological types, except adenocarcinoma and large cell carcinoma. Incidence among women increased overall (APC: 1.6, 95% CI: 1.1; 2.0) and in all age groups and histological types, except small cell carcinoma. Age-standardized incidence rate (world) per 100,000 was 54.2 in men and 12.9 in women in 2014. Changes in mortality closely followed those in incidence. According to our predictions, the age-standardized incidence and mortality rates will continue to decrease in men and reach a plateau in women.

Conclusions

The study revealed divergent LC trends by gender, age and histological type, which were generally consistent with main international findings. Growing public awareness and stricter tobacco control have stimulated overall favorable changes in men, but not yet in women. Large increase in incidence was observed for adenocarcinoma, which in men showed a trend opposite to the overall decline. LC will remain a serious public health issue in Estonia due to a high number of cases during the next decades, related to aging population, and previous and current smoking patterns. National tobacco control policy in Estonia should prioritize preventing smoking initiation and promoting smoking cessation, particularly among women.

Does smoking change expression patterns of the tumor suppressor and DNA repair genes in the prostate gland?

OBJECTIVES

Somatic mutations can be present in clonally expanded cell populations in nonmalignant tissues, which are detectable at tissue-level resolution. Some of the mutational changes may arise due to smoking. We aimed to find out changes in carcinogenic gene expressions related to smoking in nonmalignant prostate gland epithelia.

MATERIALS AND METHODS

The patients who came to the Department of Urology at Abant Izzet Baysal University Medical Faculty from December 2006 to December 2009 for prostate biopsy were questioned for cigarette smoking. The patients were divided into 2 groups, namely, smokers and nonsmokers. Paraffin sections were stained immunohistochemically with p53, PTEN, p16INK4a, MSH2, CHK2, RB, and E-cadherin.

RESULTS

Smoking was the main independent factor that had an effect on the immunohistochemical expressions for p53, p16, and PTEN (P = 0.007, P = 0.036, P = 0.015, respectively). Age and inflammation had no statistically significant effects on gene expressions. No difference was found between smokers and nonsmokers for immunohistochemical expressions of E-cadherin, MSH2, RB, and CHK2.

CONCLUSIONS

Smoking-related carcinogens can alter the expressions of some suppressor genes in a prostate tissue, and these alterations can be determined immunohistochemically. Alterations in these genes in prostate gland epithelia could possibly increase the risk for prostate carcinoma.

Uncovering Driver DNA Methylation Events in Nonsmoking Early Stage Lung Adenocarcinoma

As smoking rates decrease, proportionally more cases with lung adenocarcinoma occur in never-smokers, while aberrant DNA methylation has been suggested to contribute to the tumorigenesis of lung adenocarcinoma. It is extremely difficult to distinguish which genes play key roles in tumorigenic processes via DNA methylation-mediated gene silencing from a large number of differentially methylated genes. By integrating gene expression and DNA methylation data, a pipeline combined with the differential network analysis is designed to uncover driver methylation genes and responsive modules, which demonstrate distinctive expressions and network topology in tumors with aberrant DNA methylation. Totally, 135 genes are recognized as candidate driver genes in early stage lung adenocarcinoma and top ranked 30 genes are recognized as driver methylation genes. Functional annotation and the differential network analysis indicate the roles of identified driver genes in tumorigenesis, while literature study reveals significant correlations of the top 30 genes with early stage lung adenocarcinoma in never-smokers. The analysis pipeline can also be employed in identification of driver epigenetic events for other cancers characterized by matched gene expression data and DNA methylation data.