Dataset Information

Differential expression of lung adenocarcinoma transcriptome with signature of tobacco exposure.

ABSTRACT: Smoking accounts for almost 80-90% of lung cancer cases, which is also the most frequent cause of cancer-related deaths in humans. With over 60 carcinogens in tobacco smoke, cells dividing at the time of carcinogen exposure are at particular risk of neoplasia. The present study aimed to investigate global gene expression differences in lung adenocarcinoma (LUAD) tumour samples of current smokers and non-smokers, in an attempt to elucidate biological mechanisms underlying divergent smoking effects. Current and non-smoker tumour samples were analysed using bioinformatics tools, examining differences in molecular drivers of cancer initiation and progression, as well as evaluating the effect of smoking and sex on epithelial mesenchymal transition (EMT). As a result, we identified 1150 differentially expressed genes showing visible differences in the expression profiles between the smoking subgroups. The genes were primarily involved in cell cycle, DNA replication, DNA repair, VEGF, GnRH, ErbB and T cell receptor signalling pathways. Our results show that smoking clearly affected E2F transcriptional activity and DNA repair pathways including mismatch repair, base excision repair and homologous recombination. We observed that sex could modify the effects of PLA2G2A and PRG4 in LUAD tumour samples, whereas sex and smoking status might possibly have a biological effect on the EMT-related genes: HEY2, OLFM1, SFRP1 and STRAP. We also identified potential epigenetic changes smoking solely might have on EMT-related genes, which may serve as potential diagnostic and prognostic biomarkers for LUAD patients.

SUBMITTER: Hammouz RY

PROVIDER: S-EPMC7413900 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Differential expression of lung adenocarcinoma transcriptome with signature of tobacco exposure.

Hammouz Raneem Y RY Kostanek Joanna K JK Dudzisz Aleksandra A Witas Piotr P Orzechowska Magdalena M Bednarek Andrzej K AK

Journal of applied genetics 20200620 3

Smoking accounts for almost 80-90% of lung cancer cases, which is also the most frequent cause of cancer-related deaths in humans. With over 60 carcinogens in tobacco smoke, cells dividing at the time of carcinogen exposure are at particular risk of neoplasia. The present study aimed to investigate global gene expression differences in lung adenocarcinoma (LUAD) tumour samples of current smokers and non-smokers, in an attempt to elucidate biological mechanisms underlying divergent smoking effect ...[more]

PMID: 32564237

Similar Datasets

Project description:BackgroundWhen considering therapies for lung adenocarcinoma (LUAD) patients, the carcinogenic mechanisms of smokers are believed to differ from those who have never smoked. The rising trend in the proportion of nonsmokers in LUAD urgently requires the understanding of such differences at a molecular level for the development of precision medicine.MethodsThree independent LUAD tumor sample sets-TCGA, SPORE and EDRN-were used. Genome patterns of expression (GE), copy number variation (CNV) and methylation (ME) were reviewed to discover the differences between them for both smokers and nonsmokers. Tobacco-related signature genes distinguishing these two groups of LUAD were identified using the GE, ME and CNV values of the whole genome. To do this, a novel iterative multi-step selection method based on the partial least squares (PLS) algorithm was proposed to overcome the high variable dimension and high noise inherent in the data. This method can thoroughly evaluate the importance of genes according to their statistical differences, biological functions and contributions to the tobacco exposure classification model. The kernel partial least squares (KPLS) method was used to further optimize the accuracies of the classification models.ResultsForty-three, forty-eight and seventy-five genes were identified as GE, ME and CNV signatures, respectively, to distinguish smokers from nonsmokers. Using only the gene expression values of these 43 GE signature genes, ME values of the 48 ME signature genes or copy numbers of the 75 CNV signature genes, the accuracies of TCGA training and SPORE/EDRN independent validation datasets all exceed 76%. More importantly, the focal amplicon in Telomerase Reverse Transcriptase in nonsmokers, the broad deletion in ChrY in male nonsmokers and the greater amplification of MDM2 in female nonsmokers may explain why nonsmokers of both genders tend to suffer LUAD. These pattern analysis results may have clear biological interpretation in the molecular mechanism of tumorigenesis. Meanwhile, the identified signature genes may serve as potential drug targets for the precision medicine of LUAD.

Project description:BackgroundThe acquisition of invasive tumor cell behavior is considered to be the cornerstone of the metastasis cascade. Thus, genetic markers associated with invasiveness can be stratified according to patient prognosis. In this study, we aimed to identify an invasive genetic trait and study its biological relevance in lung adenocarcinoma.Methods250 TCGA patients with lung adenocarcinoma were used as the training set, and the remaining 250 TCGA patients, 500 ALL TCGA patients, 226 patients with GSE31210, 83 patients with GSE30219, and 127 patients with GSE50081 were used as the verification data sets. Subtype classification of all TCGA lung adenocarcinoma samples was based on invasion-associated genes using the R package ConsensusClusterPlus. Kaplan-Meier curves, LASSO (least absolute contraction and selection operator) method, and univariate and multivariate Cox analysis were used to develop a molecular model for predicting survival.ResultsAs a consequence, two molecular subtypes for LUAD were first identified from all TCGA all data sets which were significant on survival time. C1 subtype with poor prognosis has higher clinical characteristics of malignancy, higher mutation frequency of KRAS and TP53, and a lower expression of immune regulatory molecules. 2463 differentially expressed invasion genes between C1 and C2 subtypes were obtained, including 580 upregulation genes and 1883 downregulation genes. Functional enrichment analysis found that upregulated genes were associated with the development of tumor pathways, while downregulated genes were more associated with immunity. Furthermore, 5-invasion gene signature was constructed based on 2463 genes, which was validated in four data sets. This signature divided patients into high-risk and low-risk groups, and the LUDA survival rate of the high-risk group is significantly lower than that of the low-risk group. Multivariate Cox analysis revealed that this gene signature was an independent prognostic factor for LUDA. Compared with other existing models, our model has a higher AUC.ConclusionIn this study, two subtypes were identified. In addition, we developed a 5-gene signature prognostic risk model, which has a good AUC in the training set and independent validation set and is a model with independent clinical characteristics. Therefore, we recommend using this classifier as a molecular diagnostic test to assess the prognostic risk of patients with LUDA.

Project description:Tobacco exposure is one of the major risks for the initiation and progress of lung cancer. The exact corresponding mechanisms, however, are mainly unknown. Recently, a growing body of evidence has been collected supporting the involvement of DNA methylation in the regulation of gene expression in cancer cells. The identification of tobacco-related signature methylation probes and the analysis of their regulatory networks at different molecular levels may be of a great help for understanding tobacco-related tumorigenesis. Three independent lung adenocarcinoma (LUAD) datasets were used to train and validate the tobacco exposure pattern classification model. A deep selecting method was proposed and used to identify methylation signature probes from hundreds of thousands of the whole epigenome probes. Then, BIMC (biweight midcorrelation coefficient) algorithm, SRC (Spearman's rank correlation) analysis, and shortest path tracing method were explored to identify associated genes at gene regulation level and protein-protein interaction level, respectively. Afterwards, the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis and GO (Gene Ontology) enrichment analysis were used to analyze their molecular functions and associated pathways. 105 probes were identified as tobacco-related DNA methylation signatures. They belong to 95 genes which are involved in hsa04512, hsa04151, and other important pathways. At gene regulation level, 33 genes are uncovered to be highly related to signature probes by both BIMC and SRC methods. Among them, FARSB and other eight genes were uncovered as Hub genes in the gene regulatory network. Meanwhile, the PPI network about these 33 genes showed that MAGOH, FYN, and other five genes were the most connected core genes among them. These analysis results may provide clues for a clear biological interpretation in the molecular mechanism of tumorigenesis. Moreover, the identified signature probes may serve as potential drug targets for the precision medicine of LUAD.

Dataset Information

Differential expression of lung adenocarcinoma transcriptome with signature of tobacco exposure.

Publications

Differential expression of lung adenocarcinoma transcriptome with signature of tobacco exposure.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets