Abstract

IntroductionCancer initiation and progression mechanisms contingent upon tobacco use are not yet comprehensively understood. In lung adenocarcinoma (LUAD) increased overall mutation rate was attributed to smoking, whereas in bladder cancer (BLCA) reduced suppression of oncogenes in smokers, might have been a distinguishing factor between cancers caused in smokers and never smokers (NS).It is well established that cigarette smoking is a risk factor for BLCA. However it is also found that the risk of BLCA as well as LUAD, might be higher in women than in men when both subjects have smoked comparable amounts of cigarettes, whilst confirmed that smoking cessation reduces the risk of BLCA.Smoking is usually associated with lung cancer however, almost 25% of all lung cancer cases worldwide have been found in NS. Environmental tobacco is a relatively weak carcinogen thus it is a controversial thought to assume that LUAD in NS is due to passive exposure.Material and methodsIn our analyses we used mRNASeq expression and clinical data downloaded from The Cancer Genome Atlas (TCGA), and to perform differential gene expression analysis we used Gene Set Enrichment Analysis (GSEA). We focused on signalling pathways to investigate the molecular biology of the association of TGF-β, Wnt and Notch to explain their contribution to the inter-individual variations associated with smoking status. We will investigate how these three pathways crosstalk and respond to signals from the microenvironment to regulate the expression and function of epithelial mesenchymal transition (EMT) inducing transcription factors in the development and physiology of both cancers.Results and discussionsOf interest our analysis in nonsmokers have showed JAG1 to be underexpressed in LUAD and overexpressed in BLCA contrary to the expression of NOTCH2. Whereas in current smokers ADAM17 as well as PSEN2 have showed underexpression in LUAD but overexpression in BLCA with NOTCH1 presenting a reverse effect.ConclusionOur approach to a pathway specific gene association analysis will help detect the accumulative effect of group of functionally related genes aiding in revealing the transcriptional program accounting for the variability in the phenotype, since cancers arise from the aberrations in multiple genes, several of which have moderate or even less than moderate effects, making them difficult to detect by individual gene analysis.This work was supported by the Medical University of Lodz grant number 503/0-078-02/503-01-004. Authors declare.

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