Chemical carcinogenesis induced by lifestyle factors like cigarette smoking is a major research area in molecular epidemiology. Gene expression analysis of large numbers of genes simultaneously using microarrays holds the opportunity to study the effects of such an exposure at the genome level yielding more mechanism-based information. Therefore, the aim of our study was to investigate multiple gene expressions in blood, indicative for the effects caused by cigarette smoke. Smoking-discordant monozygotic twin pairs (n=9) were studied to diminish influences of genetic background. Using a dedicated microarray containing 600 toxicologically relevant genes, we investigated which genes are differentially expressed in smokers compared to non-smokers. We also looked for genes of which the expression changes correlated with DNA adducts, a biomarker of effective dose for exposure to cigarette smoke carcinogens. The mean DNA adduct level in smokers differed significantly from that in non-smokers (mean +/- standard error 1.96 +/- 0.24 versus 1.17 +/- 0.16 adducts per 10(8) nucleotides, respectively; P=0.04). The genes of which the expression differed most significantly between smokers and non-smokers are ATF4, MAPK14, SOD2, CYP1B1 and SERPINB2. CYP1B1 and SOD2 can directly be linked to cigarette smoke exposure, whereas the other genes are associated with stress or environmentally induced response. Main functions of the genes influenced by cigarette smoking comprise carcinogen metabolism, oxidative stress response and anti-apoptosis.

Reactive oxygen species (ROS), possibly produced during the metabolic conversion of benzo(a)pyrene (B[a]P), could be involved in B[a]P-induced genotoxicity and, eventually, carcinogenicity. Therefore, ROS formation by rat lung and liver microsomes was studied in vitro by electron spin resonance (ESR/EPR) spectrometry. B[a]P-mediated generation of ROS was detected in incubations with rat lung, but not with liver microsomes. Inhibition of cytochrome P450 (CYP450) by the non isoform-specific inhibitor SKF-525A resulted in a complete inhibition of B[a]P-dependent ROS formation, whereas ROS formation was not affected by inhibition of prostaglandin H synthase by indomethacin. Subsequently, bulky DNA adduct formation and 8-oxo-dG levels after a single oral dose of B[a]P were examined in vivo in rat lung and liver, in combination with urinary excretion of 8-oxodG. B[a]P exposure resulted in increased urinary 8-oxo-dG levels. On the contrary, 8-oxo-dG levels decreased in liver and lung after B[a]P exposure. Bulky DNA adducts reached higher levels and were more persistent in rat lung than in liver. These results indicate that ROS are generated during the CYP450 dependent metabolism of B[a]P, particularly in the rat lung, but this does not necessarily result in increased levels of oxidative DNA damage in vivo, possibly by induction of DNA repair mechanisms.

Two general mechanisms are implicated in chemical carcinogenesis. The first involves direct damage to DNA, referred to as genotoxic (GTX), to which the cell responds by repair of the damages, arrest of the cell cycle or induction of apoptosis. The second is non-DNA damaging, non-genotoxic (NGTX), in which a wide variety of cellular processes may be involved. Therefore, it can be hypothesized that modulation of the underlying gene expression patterns is profoundly distinct between GTX and NGTX carcinogens, and thus that expression profiling is applicable for classification of chemical carcinogens as GTX or NGTX. We investigated this hypothesis by analysing modulation of gene expression profiles induced by 20 chemical carcinogens in HepG2 cells with application of cDNA microarrays that contain 597 toxicologically relevant genes. In total, 22 treatments were included, divided in two sets. The training set consisted of 16 treatments (nine genotoxins and seven non-genotoxins) and the validation set of six treatments (three and three). Class discrimination models based on Pearson correlation analyses for the 20 most discriminating genes were developed with data from the training set, where after the models were tested with all data. Using all data, the correctness for classification of the carcinogens from the training set was clearly better than that for the validation set, namely 81 and 33%, respectively. Exclusion of the treatments that had only marginal effects on the expression profiles, improved the discrimination for the training and validation sets to 92 and 100% correctness, respectively. Exclusion of the gene expression signals that were hardly altered also improved classification, namely to 94 and 80%. Therefore, our study proves the principle that gene expression profiling can discriminate carcinogens with major differences in their mode of actions, namely genotoxins versus non-genotoxins.

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