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Gene expression and copy number profiling suggests the importance of allelic imbalance in 19p in asbestos-associated lung cancer

Abstract

Asbestos is a pulmonary carcinogen known to give rise to DNA and chromosomal damage, but the exact carcinogenic mechanisms are still largely unknown. In this study, gene expression arrays were performed on lung tumor samples from 14 heavily asbestos-exposed and 14 non-exposed patients matched for other characteristics. Using a two-step statistical analysis, 47 genes were revealed that could differentiate the tumors of asbestos-exposed from those of non-exposed patients. To identify asbestos-associated regions with DNA copy number and expressional changes, the gene expression data were combined with comparative genomic hybridization microarray data. As a result, a combinatory profile of DNA copy number aberrations and expressional changes significantly associated with asbestos exposure was obtained. Asbestos-related areas were detected in 2p21–p16.3, 3p21.31, 5q35.2–q35.3, 16p13.3, 19p13.3–p13.1 and 22q12.3–q13.1. The most prominent of these, 19p13, was further characterized by microsatellite analysis in 62 patients for the differences in allelic imbalance (AI) between the two groups of lung tumors. 79% of the exposed and 45% of the non-exposed patients (P=0.008) were found to be carriers of AI in their lung tumors. In the exposed group, AI in 19p was prevalent regardless of the histological tumor type. In adenocarcinomas, AI in 19p appeared to occur independently of the asbestos exposure.

Introduction

Tobacco smoking is undoubtedly the single most important reason for lung cancer. In addition to tobacco, lung cancer is associated with occupational and environmental exposure to other carcinogenic factors such as asbestos. Tobacco smoking together with asbestos exposure has been shown to act synergistically leading to more than an additive effect on the risk of lung cancer (Selikoff et al., 1968). The etiologic fraction of asbestos exposure in lung cancer among men has been estimated to range between 6 and 23% in different populations (reviewed in Karjalainen and Anttila, 1997).

Asbestos has been shown to be a genotoxic and cytotoxic agent that can produce both DNA and chromosomal damage. The mechanisms behind these actions may be multiple. The main mechanisms are thought to be generation of reactive oxygen species (ROS) and reactive nitrogen species (RNS), alteration in the mitochondrial function, physical disturbance of cell cycle progression and activation of several signal transduction pathways (Upadhyay and Kamp, 2003; Shukla et al., 2003a). Asbestos-exposed workers have been reported to have increased levels of sister chromatid exchanges and DNA double-strand breaks in white blood cells (Fatma et al., 1991; Marczynski et al., 1994). Elevated concentrations of 8-hydroxy-2′-deoxyguanosine DNA adducts, a marker for ROS exposure, have also been detected in the blood of asbestos-exposed workers (Marczynski et al., 2000).

Lung cancer is characterized by very complex patterns of both cytogenetic and molecular genetic changes (Balsara and Testa, 2002; Li et al., 2006). This high complexity, including many randomly appearing changes, makes it very challenging to determine the most essential lung carcinogenesis-associated alterations. We have, however, recently shown that asbestos exposure can be associated with a specific genomic aberration profile (Nymark et al., 2006).

The aim of this study was to investigate whether asbestos exposure causes a specific gene expression profile that correlates with the previously detected asbestos-associated genomic aberration profile. By combining the gene expression data with the comparative genomic hybridization (CGH) array data, we were able to detect six distinct chromosomal regions that harbor both gene expression and DNA level changes. One of these, 19p13.3–19p13.1, was further characterized for allelic imbalance (AI) on lung carcinomas from 62 male patients chosen on the basis of their present or absent asbestos exposure determined by the work histories and pulmonary asbestos fiber counts.

Results

Gene expression profiles

ROC analysis was carried out using the gene expression data to detect genes that best separated the lung tumors of 14 heavily asbestos-exposed patients from the tumors of 14 non-exposed patients. 12 865 genes were included in the first ROC analysis (inclusion criterion was the presence of a signal in at least one-third of the patients in either exposure group).

A crude supervised algorithm based on genes with the highest ROC values (<0.4 or >0.6, and with P-value less than 0.4) allowed us to cluster the 28 tumors into two groups on the basis of the exposure of the patients (data not shown). The clear division of the tumors according to the exposure category of the patients suggested that the tumors can be divided into these two types on the basis of about 6000 gene transcripts.

Next, the correlation coefficient of gene expression with the exposure status of the patient (asbestos exposed versus non-exposed) was calculated for each gene. To identify the smallest set of genes that could distinguish the two tumor groups, the genes were rank-ordered according to the absolute value of the correlation coefficient. The identification of exposure-associated genes revealed 47 genes with Pearson's correlation coefficient >0.8 or <−0.8 (Figure 1, Supplementary Table 1). Hierarchical clustering results obtained for these 47 genes are shown in Figure 1. We note that our choice of reference (the median signal of the non-associated tumors for the asbestos-associated tumors and the median signal of the asbestos-associated tumors for the non-associated tumors) gives rise to the relatively high-correlation coefficient, but similar results are obtained when median signals of normal tissue of each group was used as a reference. Thirty-eight of the 47 top genes are identical with both references. Only single genes with similar magnitude of correlation coefficient could be detected after random permutation of the data. The functional annotation for this small set of top genes (47) did not show clear overrepresentation of a specific function or chromosomal localization.

Figure 1
figure1

A supervised hierarchical clustering result of lung carcinomas from 14 heavily asbestos-exposed and 14 non-exposed patients using the 47 significant genes found with the correlation analysis. The cluster on the left is composed of the exposed patients, and the cluster on the right of the non-exposed patients. The brightness of the colors correlates with the difference of expression between the two groups (green, downregulated; red, upregulated).

Combination of gene expression profiles with DNA aberration profiles

The identification of exposure-related areas with expressional changes revealed 35 areas (areas within 5 Mbp were combined) on which the tumors of asbestos-exposed patients differed from the tumors of non-exposed patients (data not shown). The detection of the areas was performed by comparing the gene expression data of the two tumor groups to each other in 0.5–1 Mbp segments similar to as done for the CGH array (Nymark et al., 2006). Areas with exposure-related changes were identified by means of permutation testing. The regions were declared significant if the observed expressional differences were beyond the upper or lower 1% confidence intervals estimated from the permutation distribution. To identify loci that contain exposure-associated changes both at messenger RNA (mRNA; expression data) and DNA level (CGH array), results from these two data analyses were combined. Six areas were common in the two analyses, namely 2p21–p16.3, 3p21.31, 5q35.2–q35.3, 16p13.3, 19p13.3–p13.1 and 22q12.3–q13.1 (Table 1). The data suggests that 2p21–p16.3 could be simultaneously amplified in the exposed patients' and deleted in the non-exposed patients' tumor samples, whereas 3p21.31, 5q35.2–q35.3 and 22q12–3.q13.1 seem to be deleted among the tumors of the exposed group of patients. The largest significant region was detected on chromosome 19p13.3–p13.1, showing a loss and downregulation of genes in exposed patients and gain in the non-exposed patients.

Table 1 Combined results from gene expression and DNA aberration profiling

Allelic imbalance on 19p13.3–p13.1

Microsatellite (LOH, loss of heterozygosity) analysis was carried out to verify the exposure-associated changes on the p-arm of chromosome 19 and to reveal the extent of the aberration in 62 lung carcinomas from male patients that fell into three categories of exposure: heavy exposure, moderate occupational exposure and no exposure to asbestos. Ninteen microsatellite markers spanning 22.3 Mbp region on 19p13.3–p13.1 were used (Figure 2) for majority of the samples. For the 10 paraffin samples, only five of the 19 markers producing fragments less than 200 bp were analysed. About 80% (20/25) of the exposed and 45% (13/29) of the non-exposed patients (P=0.0045) were found to be carriers of AI on the 19p region in their tumor tissue. AI was also detected in 75% (6/8) of the moderately exposed patients (Figure 2). AI detected was in good accordance with the results indicated by the CGH array (Nymark et al., 2006).

Figure 2
figure2

Microsatellite analysis results for LOH at 19p13.3–19p13.1 in lung carcinomas of 25 heavily asbestos-exposed, seven moderately asbestos-exposed and of 29 non-exposed patients. The microsatellite markers used are given without the prefix 19S. In addition to the markers on 19p, results are also presented for the BAT-26 marker. The result for each marker is shown as follows: AI, red; MSI, yellow; non-informative, dark gray; unavailable measurement, light gray; and informative without changes, white. For 11 of the heavily exposed samples, results are only available from five markers. P-values are given for individual markers describing the difference between all asbestos exposed and non-exposed.

Differences in AI frequencies were observed between histological tumor types. In the exposed groups, AI was prevalent regardless of histological type (Table 2). The results are presented for the combined group of heavily and moderately exposed patients because no obvious differences were detected in the AI frequencies between these two exposure groups. On the other hand, the non-exposed group, AI was detected commonly in adenocarcinomas. More thorough comparing between different lung cancer subtypes is, however, not possible because of limited group sizes.

Table 2 Prevalence of allelic imbalance on 19p in lung carcinomas of asbestos exposed and non-exposed patients according to histological tumor type

The AI degree for individual markers ranged between 50 and 90% in exposed, 40 and 100% in moderately exposed and 20 and 50% in non-exposed patients' tumor samples (only informative markers are taken into account). When focusing into the differences in the frequency of AI as determined by individual markers, the frequency of chromosomal alterations was significantly higher with 11 out of 19 markers in the tumor samples from asbestos-exposed patients compared with the non-exposed patients (Figure 2). In most cases, AI seemed to extend throughout the investigated 22 Mbp region, indicating a complete loss of the short arm of chromosome 19.

Additionally, 10% (3/29) of the non-exposed patients were found to have microsatellite instability (MSI) ranging throughout the region studied. When assessing MSI further with the colon MSI marker BAT-26, two of the three cases (54 and 60) showing high instability in the individual markers also showed instability in this marker. As MSI cases were detected among moderately exposed and non-exposed patients, MSI does not seem to be a major player in asbestos-related cancer and further analyses were not conducted.

Discussion

To bring insight to the deregulated genes associated with asbestos-related lung cancer, we performed a combined expression and CGH microarray screening analysis on two groups of primary lung tumors, namely asbestos-associated and non-associated carcinomas, similar for other patient and tumor characteristics. We have demonstrated previously, by using classical and array CGH, that DNA copy number changes in certain chromosomal regions appeared to be more common in lung carcinomas of asbestos-exposed than in the carcinomas of non-exposed patients (Nymark et al., 2006). In the present study, a gene expression profile for asbestos-associated lung cancer was created, and the chromosomal regions with both gene copy number and expression changes were revealed. One of the most discriminative regions, 19p, was further verified by microsatellite analysis on tumor and normal tissue samples from 62 patients with heavy or moderate exposure, or no exposure to asbestos.

We found a set of 47 genes that correctly classified the tumors according to the asbestos-exposure status of the patients by using a two-step data analysis procedure for the gene expression. Hierarchical clustering analysis on these 47 genes showed a clear division between the two exposure groups independently of the histological lung cancer type. Although several of these genes are currently fairly unknown, a few of them have been reported altered in different tumor types. These include WFDC2, TDE1 and SLC6A15, which we detected to be upregulated, and RUNX1, ATM and UVRAG, which were downregulated in our tumors from asbestos-exposed patients. The TDE1 gene has been shown to be up-regulated in lung cancer cell lines (Bossolasco et al., 1999), whereas the HE4 (WFDC2) protein has been reported as a biomarker for ovarian carcinoma (Hellstrom et al., 2003), and the SLC6A15 gene to be upregulated in colorectal cancer (Gupta et al., 2005). ATM is known to be silenced in lung cancer by promoter hypermethylation (Safar et al., 2005), and UVRAG was recently reported to be mutated in colon cancer (Ionov et al., 2004). Translocations, mutations and methylation of RUNX1 have been described in leukemia and lately in gastric cancer (Blyth et al., 2005; Sakakura et al., 2005).

Adducin – a substrate of protein kinase C (PKC) – has been associated before with asbestos exposure (Shukla et al., 2003b). The PKC signal transduction pathway is suggested to be one of the main signaling pathways to be activated after asbestos exposure (Shukla et al., 2003b). Similar to these findings, adducin was found to be upregulated among the lung tumors of asbestos-exposed patients of the present study.

The difficulty to find stable and reliable molecular signatures from microarray data, even when the data sets are large, was recently presented by Michiels et al. (2005). We are, thus, aware that by doing this type of analysis with thousands of genes but few patients, one has a big chance of finding false-positive results. Therefore, for validation and increasing the specificity of the identified differences, the expression profiles were correlated with CGH data. Potential good markers with biological relevance are aberrations that have influence on gene expression.

In this study we were indeed able to find six chromosomal regions that were changed at DNA and RNA levels simultaneously and were more common in either the asbestos-exposed or non-exposed group of tumors. Interestingly, four of these regions seemed to be deletions in the tumors of asbestos-exposed patients. It is unsurprising that not all expressional changes can be explained by DNA level aberrations as there has been shown to be only a 10–40% correlation between chromosomal and expressional changes (Hyman et al., 2002). Changes in methylation or acetylation patterns can also result in areas with RNA-level changes.

Chromosome 3p, 5q, 19p and 22q aberrations, which we found to be significantly associated with asbestos exposure, have all been detected previously in lung carcinogenesis in general. However, recently an association between the loss of 3p and asbestos exposure was described (Marsit et al., 2004). Furthermore, the q-arm of chromosome 22 has been reported to be commonly lost in malignant mesothelioma, a cancer type closely linked to asbestos exposure (De Rienzo and Testa, 2000). Whereas, 2p amplifications have rarely been described in lung tumors, a region homologous to the human 2p21–p25 has previously been reported to be amplified in radon-induced rat lung tumors (Dano et al, 2000). Although 16p13.3 has been reported to be amplified rarely, it contains TSC2 gene, which has been described to be affected by LOH in 29% of lung adenocarcinomas (Takamochi et al., 2004). Also NTHL1 gene on 16p13.3, which is involved in 8oxoG repair, has been shown to have a lower expression in lung cancer compared than normal lung tissue (Radak et al., 2005)

In the present study, one of the possibly asbestos-related chromosomal regions according to gene expression and CGH array results, 19p13, was further verified by microsatellite analysis on a larger number of tumors. LOH of 19p is common in lung cancer (Sanchez-Cespedes et al., 2001), but its relation with asbestos exposure has not been studied previously. We observed, as expected, that the chromosomal changes were not limited to the tumors of exposed patients, but they were significantly associated with asbestos exposure, similarly to as shown previously for 3p (Marsit et al., 2004). AI in the 19p13 region was detected in lung carcinomas in 80% of the heavily exposed, in 75% of the moderately exposed and in 45% the non-exposed patients, indicating that asbestos exposure favor the rise of aberrations in this area. Our results indicate that AI is prevalent in the tumors of exposed patients regardless of histological type. In the non-exposed patients, AI seems to occur frequently in adenocarcinomas but uncommonly in other lung cancer types. The detection of AI in adenocarcinomas of both the exposed and non-exposed patients is in accordance with the study by Sanchez-Cespedes et al. (2001), where it was shown that 58% of lung adenocarcinomas of smoking patients had AI at 19p. The AI profiles of adenocarcinomas did not show obvious differences in comparison with AI profiles of other histological tumor types.

In our study, the markers with best separation between asbestos-exposed and non-exposed patients' tumors were spread throughout the 19p region. Well-differentiating markers resided both in the tip and in the middle of the p-arm, suggesting either the presence of multiple asbestos exposure related hotspots within the area or an association of asbestos exposure with imbalance of the whole-chromosomal arm.

It is noteworthy that two genes STK11 and SMARCA4, reported to be inactivated in lung cancer previously, reside proximal to some of the most significantly distinguishing markers on the 19p13 region. Inactivation through mutations and LOH of the tumor-suppressor gene STK11 (LKB1) has been found in 30% of lung adenocarcinomas (Sanchez-Cespedes et al., 2002), whereas the SMARCA4 (BRG1) gene has been implied to have a role in lung tumorigenesis (Medina et al., 2005). The SMARCA4 protein has been reported to be lost in about 10% of the lung primary tumors (Reisman et al., 2003).

Microsatellite analysis does not, however, differentiate between allelic gain and loss, and thus the changes in markers may only be reported as AI. Our array CGH results do, however, suggest that there are both losses and gains on the 19p13 region in the tumor samples, gains especially among the non-exposed patients' samples. Therefore, the association of this region with asbestos exposure may be underestimated by our current results. Additional studies should be carried out by means of, for example, quantitative PCR to gain better insight into the nature of the changes occurring in this and other regions. Such studies are expected to strengthen the relatedness of the 19p aberration, especially the loss, to asbestos exposure.

In conclusion, by combining different high-throughput methods we show for the first time that lung carcinomas of asbestos-exposed patients have a distinct gene expression profile with certain chromosomal regions such as 19p significantly associated with the exposure.

Materials and methods

Patient material

All patients were of Finnish Caucasian origin with histologically confirmed primary lung cancer and no previous malignancies. The samples for gene expression analysis consisted of lung tumor and corresponding normal lung samples from 14 heavily asbestos-exposed and from 14 non-exposed patients (Table 3). The subsequent microsatellite analyses for AI in 19p were done on the original set of 28 cases and on 34 additional lung cancer cases chosen on the basis of the level of asbestos exposure: 11 heavily asbestos-exposed, eight moderately occupationally asbestos-exposed and 15 non-exposed lung cancer cases (Table 4). The Ethical Review Boards for Research in Occupational Health and Safety and the Coordinating Ethical Review Board, Helsinki and Uusimaa Hospital District, have approved the study protocols (223/E0/2005 and75/E2/2001). The National Agency for Medicolegal Affairs has given the permission to use diagnostic samples for the research purpose (4476/33/300/05), and the Ministry for Social Affairs and Health has permitted the collection of patient information for research (STM/2474/2005).

Table 3 Characteristics of cancer patients and lung tumors studied by expression array and array CGH
Table 4 Characteristics of cancer patients and lung tumors studied by microsatellite analysis

In all cases, the level of asbestos exposure was estimated both by work history and by measurement of the pulmonary asbestos fiber concentration (Karjalainen et al., 1993). Only patients, who had both a definite or probable occupational exposure history to asbestos according to an interview, and more than 5 million fibers per gram of dry lung tissue were included in the heavy exposure group. Patients with a concentration between 1 and 5 million fibers per gram were classified as moderately exposed. A minimum of 1 million fibers per gram of dry lung tissue is usually considered as a sign of occupational exposure to asbestos (Karjalainen et al., 1993). In the non-exposed group, only patients in whom neither the exposure history nor the pulmonary fiber count indicated an exposure to asbestos were included.

Expression microarrays

RNA was isolated with Ultraspec RNA isolation system from tumor and adjacent normal peripheral lung tissue for each patient as described in Wikman et al. (2002). The quality of RNA was assessed with 2100 Bioanalyzer (RNA Nano Labchip, Agilent Technologies, Palo Alto, CA, USA) and quantified by spectrophotometer.

Gene expression profiling was conducted using Affymetrix HG-U133A GeneChips (Affymetrix, Santa Clara, CA, USA) with 6 μg of total RNA. The RNA was converted into cDNA by one-cycle cDNA Synthesis Kit (Invitrogene, Carlsbad, CA, USA), purified and converted into labeled cRNA (Enzo, Farmingdale, NY, USA) according to Affymetrix recommendations. The fragmented cRNA was hybridized for 16 h. Washing, staining and scanning of the slides were performed according to the standard Affymetrix protocols. Hybridizations on Affymetrix chips (HG-U133A) were carried out with tumor and normal lung RNA samples from each of the 28 patients.

Data analysis of the gene expression data

Affymetrix Analysis Suite version 5 (MAS5) was used to scale the arrays for the target value of 100 and to define the absent/present calls. Only samples with a background of 40–70 and house keeping control signal ratios (5′–3′ prime end transcript ratio) close to 1 were included in data analysis. As a result of these criteria, three normal lung samples were excluded from the study.

Chips of matched normal lung samples were used as a reference for the tumor chips. For the three cases with a missing normal lung result, the mean signal of the samples from the same exposure group was used instead as a reference. Genes that were present (Affymetrix P-value <0.04) in at least one-third of the exposed or non-exposed samples were included in the analyses. Next, the data were log2 transformed and Lowess normalized.

A two-step analysis model was used to detect differentially expressed genes and to identify the smallest set of genes that could distinguish the exposed group from the non-exposed group. As the first step, AUROC (ROC) analysis model (Kettunen et al., 2004) was chosen owing to similar size of the two exposure groups. Genes with ROC values less than 0.4, or higher than 0.6, and with P-value less than 0.4 were included in the subsequent analyses.

In the second step, a correlation coefficient for the gene expression and exposure status (asbestos exposed versus non-exposed) was calculated for each gene. As we were primarily interested in the differences between the tumors of asbestos-exposed and non-exposed patients and, minimize the effect of variation in gene expression between individual normal lung tissue samples, the data were rescaled before conducting the correlation analysis. To emphasize the differences between the asbestos-associated and non-associated tumors, the signals of the asbestos-associated tumors were scaled by the median signal of the non-associated tumors and the signals of the non-associated tumors by the median signal of the asbestos-associated tumors.

The genes were rank-ordered according to the absolute value of the correlation coefficient. To optimize the number of genes needed for the correct classification of tumors, the genes were added sequentially according to their rank order, and the number of correctly classified patients was determined. A ‘leave-one-out’ cross-validation method was used to assess the reliability of the classification.

Analysis of combined gene expression and DNA copy number data

Identification of the chromosomal areas with exposure-associated gene expression changes was performed by comparing the gene expression ratios of the exposed to the non-exposed locally. The chromosomes were divided into overlapping segments of 0.5–1 Mbp and each segment was tested for differential expression. The differentially expressed regions were identified by means of hypothesis testing. The number of patients correctly classified by the gene expression ratios of each gene was calculated and, as a test statistic, an average classification capability of the segment was used. The regions found in this analysis were compared with the regions found to have exposure-associated copy number changes. The regions that were detected both in the expression and copy number data sets were considered prominently interesting.

Microsatellite analysis for detection of AI

Microsatellite analysis was used as a validation method for confirming the presence of AI. The samples used in microsatellite analyses included both microdissected and not microdissected DNA specimens. The original 28 tumor samples from heavily asbestos-exposed and non-exposed patients were macrodissected, whereas microdissection was used to obtain DNA from the additional 34 patient samples. Samples for microsatellite analysis were from freshly frozen tissue in 52 cases and from paraffin-embedded tissue in 10 cases.

Microdissection was performed using an Arcturus Veritas instrument on 9-μm tissue sections stained with 1% toluidine blue and 0.2% methylene blue solution. Laser capture microdissection technology was utilized to harvest cancer cells from heterogeneous tumor tissues. DNA was isolated using a PicoPure DNA Extraction Kit (Arcturus Bioscience, Inc., Mountain View, CA, USA) according to the manufacturer's instructions.

Allelic balance of the chromosomal region 19p13.3–13.1 (chr19:550811–22287245 bp; 22.29 Mbp) was assessed using 5–19 microsatellite markers with approximate coverage of 22 Mbp. FAM or HEX end-labeled primer pairs were used to amplify the di- or trinucleotide-repeat fragments of 80–300 bp in length. The primer sequences for the markers were obtained from the databases of the National Center for Biotechnology. The target sequences were amplified by PCR, and the PCR products were then electrophorized with a 310- or 3100-Avant Genetic Analyzer (Applied Biosystems, Inc., Foster City, CA, USA).

GeneMapper Analysis Software version 3.5 (Applied Biosystems) was used to study the lengths of the allele fragments. The alleles were defined as the two highest peaks within the expected size range. The determination of AI was performed for heterozygous markers by calculating the ratio of the peak heights of the tumor and normal alleles. Ratios of 1.5 or higher were scored as AI. The criterion based on which AI carriers were determined was that at least 25% of the informative microsatellite markers had to be AI-positive. The mononucleotide repeat BAT-26 was used to test its correlation with the MSI phenotypes in lung cancer. This marker has previously been used to reveal a high-frequency MSI phenotype of sporadic colorectal and gastric cancers with 99.4–100% accuracy (Hoang et al., 1997).

Abbreviations

CGH array:

comparative genomic hybridization array

LOH:

loss of heterozygosity

AI:

allelic imbalance

MSI:

microsatellite instability

References

  1. Balsara BR, Testa JR . (2002). Chromosomal imbalances in human lung cancer. Oncogene 21: 6877–6883.

    CAS  Article  PubMed  Google Scholar 

  2. Blyth K, Cameron ER, Neil JC . (2005). The RUNX genes: gain or loss of function in cancer. Nat Rev Cancer 5: 376–387.

    CAS  Article  PubMed  Google Scholar 

  3. Bossolasco M, Lebel M, Lemieux N, Mes-Masson AM . (1999). The human TDE gene homologue: localization to 20q13.1–13.3 and variable expression in human tumor cell lines and tissue. Mol Carcinog 26: 189–200.

    CAS  Article  PubMed  Google Scholar 

  4. Dano L, Guilly MN, Muleris M, Morlier JP, Altmeyer S, Vielh P et al. (2000). CGH analysis of radon-induced rat lung tumors indicates similarities with human lung cancers. Genes Chromosomes Cancer 29: 1–8.

    CAS  Article  PubMed  Google Scholar 

  5. De Rienzo A, Testa JR . (2000). Recent advances in the molecular analysis of human malignant mesothelioma. Clin Ther 151: 433–438.

    CAS  Google Scholar 

  6. Fatma N, Jain A, Rahman Q . (1991). Frequency of sister chromatid exchange and chromosomal aberrations in asbestos cement workers. Br J Int Med 48: 103–105.

    CAS  Google Scholar 

  7. Gupta N, Miyauchi S, Martindale RG, Herdman AV, Podolsky R, Miyake K et al. (2005). Upregulation of the amino acid transporter ATB0,+ (SLC6A14) in colorectal cancer and metastasis in humans. Biochim Biophys Acta 1741: 215–223.

    CAS  Article  PubMed  Google Scholar 

  8. Hellstrom I, Raycraft J, Hayden-Ledbetter M, Ledbetter JA, Schummer M, McIntosh M et al. (2003). The HE4 (WFDC2) protein is a biomarker for ovarian carcinoma. Cancer Res 63: 3695–3700.

    PubMed  Google Scholar 

  9. Hoang JM, Cottu PH, Thuille B, Salmon RJ, Thomas G, Hamelin R . (1997). BAT-26, an indicator of the replication error phenotype in colorectal cancers and cell lines. Cancer Res 57: 300–303.

    CAS  PubMed  Google Scholar 

  10. Hyman E, Kauraniemi P, Hautaniemi S, Wolf M, Mousses S, Rozenblum E et al. (2002). Impact of DNA amplification on gene expression patterns in breast cancer. Cancer Res 62: 6240–6245.

    CAS  PubMed  Google Scholar 

  11. Ionov Y, Nowak N, Perucho M, Markowitz S, Cowell JK . (2004). Manipulation of nonsense mediated decay identifies gene mutations in colon cancer cells with microsatellite instability. Oncogene 23: 639–645.

    CAS  Article  PubMed  Google Scholar 

  12. Karjalainen A, Anttila S . (1997). Asbestos exposure and the risk of lung cancer in urban populations. In: Chereminsinoff (ed). Health and Toxicology. Advances in Environmental Control Technology Series: Houston, USA, pp 127–136.

    Google Scholar 

  13. Karjalainen A, Anttila S, Heikkila L, Karhunen P, Vainio H . (1993). Asbestos exposure among Finnish lung cancer patients: occupational history and fiber concentration in lung tissue. Am J Int Med 23: 461–471.

    CAS  Article  Google Scholar 

  14. Kettunen E, Anttila S, Seppanen JK, Karjalainen A, Edgren H, Lindstrom I et al. (2004). Differentially expressed genes in nonsmall cell lung cancer: expression profiling of cancer-related genes in squamous cell lung cancer. Cancer Genet Cytogenet 149: 98–106.

    CAS  Article  PubMed  Google Scholar 

  15. Li R, Wang H, Bekele BN, Yin Z, Caraway NP, Katz RL et al. (2006). Identification of putative oncogenes in lung adenocarcinoma by a comprehensive functional genomic approach. Oncogene 25: 2628–2635.

    CAS  Article  PubMed  Google Scholar 

  16. Marczynski B, Czuppon A, Marek W, Reichel G, Baur X . (1994). Increased incidence of DNA double-strand breaks and anti-ds DNA antibodies in blood of workers occupationally exposed to asbestos. Hum Exp Toxicol 13: 3–9.

    CAS  Article  PubMed  Google Scholar 

  17. Marczynski B, Rozynek P, Kraus T, Schlosser S, Raithel HJ, Baur X . (2000). Levels of 8-hydroxy-2′-deoxyguanosine in DNA of white blood cells from workers highly exposed to asbestos in Germany. Mutat Res 468: 195–202.

    CAS  Article  PubMed  Google Scholar 

  18. Marsit CJ, Hasegawa M, Hirao T, Kim D-H, Aldape K, Hinds PW et al. (2004). Loss of heterozygosity of chromosome 3p21 is associated with mutant TP53 and better patient survival in non-small-cell lung cancer. Cancer Res 64: 8702–8707.

    CAS  Article  PubMed  Google Scholar 

  19. Medina PP, Carretero J, Ballestar E, Angulo B, Lopez-Rios F, Esteller M et al. (2005). Transcriptional targets of the chromatin-remodelling factor SMARCA4/BRG1 in lung cancer cells. Hum Mol Genet 14: 973–982.

    CAS  Article  PubMed  Google Scholar 

  20. Michiels S, Koscielny S, Hill C . (2005). Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet 365: 488–492.

    CAS  Article  PubMed  Google Scholar 

  21. Nymark P, Wikman H, Ruosaari S, Hollmén J, Vanhala E, Karjalainen A et al. (2006). Identification of specific gene copy number changes in asbestos-related lung cancer. Cancer Res 66: 5737–5743.

    CAS  Article  PubMed  Google Scholar 

  22. Radak Z, Goto S, Nakamoto H, Udud K, Papai Z, Horvath I . (2005). Lung cancer in smoking patients inversely alters the activity of hOGG1 and hNTH1. Cancer Lett 219: 191–195.

    CAS  Article  PubMed  Google Scholar 

  23. Reisman DN, Sciarrotta J, Wang W, Funkhouser WK, Weissman BE . (2003). Loss of BRG1/BRM in human lung cancer cell lines and primary lung cancers: correlation with poor prognosis. Cancer Res 63: 560–566.

    CAS  PubMed  Google Scholar 

  24. Safar AM, Spencer III H, Su X, Coffey M, Cooney CA, Ratnasinghe LD et al. (2005). Methylation profiling of archived non-small cell lung cancers: a promising prognostic system. Clin Cancer Res 11: 4400–4405.

    CAS  Article  PubMed  Google Scholar 

  25. Sakakura C, Hagiwara A, Miyagawa K, Nakashima S, Yoshikawa T, Kin S et al. (2005). Frequent downregulation of the runt domain transcription factors RUNX1, RUNX3 and their cofactor CBFB in gastric cancer. Int J Cancer 113: 221–228.

    CAS  Article  PubMed  Google Scholar 

  26. Sanchez-Cespedes M, Ahrendt SA, Piantadosi S, Rosell R, Monzo M, Wu L et al. (2001). Chromosomal alterations in lung adenocarcinomas from smokers and nonsmokers. Cancer Res 61: 1309–1313.

    CAS  PubMed  Google Scholar 

  27. Sanchez-Cespedes M, Parrella P, Esteller M, Nomoto S, Trink B, Engles JM et al. (2002). Inactivation of LKB1/STK11 is a common event in adenocarcinomas of the lung. Cancer Res 62: 3659–3662.

    CAS  PubMed  Google Scholar 

  28. Selikoff IJ, Hammond EC, Churg J . (1968). Asbestos exposure, smoking, and neoplasia. JAMA 204: 106–112.

    CAS  Article  PubMed  Google Scholar 

  29. Shukla A, Gulumian M, Hei TK, Kamp D, Rahman Q, Mossman B . (2003a). Multiple roles of oxidants in the pathogenesis of asbestos-induced diseases. Free Radic Biol Med 34: 1117–1129.

    CAS  Article  PubMed  Google Scholar 

  30. Shukla A, Ramos-Nino M, Mossman B . (2003b). Cell signaling and transcription factor activation by asbestos in lung injury and disease. Int J Biochem Cell Biol 35: 1198–1209.

    CAS  Article  PubMed  Google Scholar 

  31. Takamochi K, Ogura T, Yokose T, Ochiai A, Nagai K, Nishiwaki Y et al. (2004). Molecular analysis of the TSC1 gene in adenocarcinoma of the lung. Lung Cancer 46: 271–281.

    Article  PubMed  Google Scholar 

  32. Upadhyay D, Kamp DW . (2003). Asbestos-induced pulmonary toxicity: role of DNA damage and apoptosis. Exp Biol Med 228: 650–659.

    CAS  Article  Google Scholar 

  33. Wikman H, Kettunen E, Seppänen JK, Karjalainen A, Hollmen J, Anttila S et al. (2002). Identification of differentially expressed genes in pulmonary adenocarcinoma by using cDNA array. Oncogene 21: 5804–5813.

    CAS  Article  PubMed  Google Scholar 

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Acknowledgements

We are grateful to Mrs Päivi Tuominen and Mrs Tuula Stjernvall for excellent technical assistance. We also thank Mrs Helinä Urhonen and Dr Kaisa Salmenkivi for helping with collecting the samples. This work is financially supported by the Academy of Finland Grants 200802 and 207469, Finnish Cancer Foundations, Sigrid Jusélius Foundation, Finnish Work Environment Fund Grant 102125, and Graduate school in Computational Biology, Bioinformatics and Biometry.

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Correspondence to S Anttila.

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Supplementary Information accompanies the paper on the Oncogene website (http://www.nature.com/onc).

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Wikman, H., Ruosaari, S., Nymark, P. et al. Gene expression and copy number profiling suggests the importance of allelic imbalance in 19p in asbestos-associated lung cancer. Oncogene 26, 4730–4737 (2007). https://doi.org/10.1038/sj.onc.1210270

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Keywords

  • lung cancer
  • asbestos
  • microarrays
  • microsatellite analysis

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