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. 2012 Mar 1;7(3):270–277. doi: 10.4161/epi.7.3.19306

DNA methylation changes associated with risk factors in tumors of the upper aerodigestive tract

Samson Mani 1,#, Katarzyna Szymańska 1,#, Cyrille Cuenin 1, David Zaridze 2, Karen Balassiano 1, Sheila CS Lima 1,9, Elena Matos 3, Alexander Daudt 4, Sergio Koifman 5, Victor Wunsch Filho 6, Ana MB Menezes 7, Maria Paula Curado 8, Gilles Ferro 1, Thomas Vaissière 1, Bakary S Sylla 1, Massimo Tommasino 1, Luis Felipe Ribeiro Pinto 9,10, Paolo Boffetta 8,†, Pierre Hainaut 1, Paul Brennan 1, Zdenko Herceg 1,✉
PMCID: PMC3335950  PMID: 22430803

Abstract

Cancers of the upper aerodigestive tract (UADT) are common forms of malignancy associated with tobacco and alcohol exposures, although human papillomavirus and nutritional deficiency are also important risk factors. While somatically acquired DNA methylation changes have been associated with UADT cancers, what triggers these events and precise epigenetic targets are poorly understood. In this study, we applied quantitative profiling of DNA methylation states in a panel of cancer-associated genes to a case-control study of UADT cancers. Our analyses revealed a high frequency of aberrant hypermethylation of several genes, including MYOD1, CHRNA3 and MTHFR in UADT tumors, whereas CDKN2A was moderately hypermethylated. Among differentially methylated genes, we identified a new gene (the nicotinic acetycholine receptor gene) as target of aberrant hypermethylation in UADT cancers, suggesting that epigenetic deregulation of nicotinic acetycholine receptors in non-neuronal tissues may promote the development of UADT cancers. Importantly, we found that sex and age is strongly associated with the methylation states, whereas tobacco smoking and alcohol intake may also influence the methylation levels in specific genes. This study identifies aberrant DNA methylation patterns in UADT cancers and suggests a potential mechanism by which environmental factors may deregulate key cellular genes involved in tumor suppression and contribute to UADT cancers.

Key words: DNA methylation, upper aerodigestive tract, cancer, risk factors, biomarkers

Introduction

The cancers of the upper aerodigestive tract (UADT) are a group of malignancies comprising cancers of the oral cavity, pharynx and larynx (often referred to as head and neck cancers) and esophageal cancer. UADT cancer is the sixth most frequent cancer worldwide; it is estimated that head and neck squamous cell carcinoma (HNSCC) alone accounts for over 500,000 cases per year.1 UADT cancers show a striking geographical variation in worldwide incidence rates, and some parts of South America, such as Brazil and Argentina, are considered high-risk areas.1 However, the underlying etiology and reasons for this geographical variation are not fully understood. Despite considerable progress being made in early detection, diagnosis and treatment, advanced UADT cancer cases have poor prognosis with a high percentage of recurrences.2,3 This highlights the need for better understanding of the molecular mechanisms underlying UADT carcinogenesis and identification of biomarkers that may serve as tools for exposure assessment, early diagnosis and predictive prognosis.

While genetic factors such as mutations and genetic polymorphisms in tumor suppressor genes have been implicated in UADT cancer etiology,4 epigenetic events have also been linked to UADT cancers. DNA methylation is considered an important epigenetic mechanism that underlies silencing of key regulatory genes in many human cancers, including UADT cancers.5–11 Abnormal DNA hypermethylation within the promoters of various cancer-associated genes is believed to be a common event in human malignancies.5,12,13 Recently, many studies have revealed the presence of somatically acquired DNA methylation changes in a large number of genes in UADT cancers, most notably head and neck squamous cell carcinoma (HNSCC).14–17 UADT cancers are causally linked to tobacco smoking and alcohol drinking, which are believed to act both independently and synergistically in the development and progression of cancer.18,19 In addition, in developing countries, HPV infection as well as dietary factors such as low folate intake and drinking of hot beverages may also be important risk factors.6,20–22 However, whether these risk factors promote the development and progression of UADT cancer through epigenetic mechanisms remains unknown.

In this study, we aimed to quantitatively identify changes in DNA methylation in UADT cancers and their potential association with primary UADT cancer risk factors. To this end, we have taken advantage of a case-control study of UADT cancers involving seven centers in South America, using detailed lifestyle information and quantitative analysis of DNA methylation in a panel of cancer-associated genes. Our results identified aberrant DNA hypermethylation of specific genes in UADT cancer and its association with major risk factors.

Results

Patient characteristics.

A total of 184 UADT cancer cases were included in the study (Table 1). Among the cases, 31% were women and 68% were men. A majority of the cases (81%) were between 41 and 70 y of age. The average age of patients of both genders was 57 y. The predominant UDAT tumors included in the study were from the oropharnyx (26%), larynx (24%) and oral cavity (20%). We categorized the subjects as never-smokers (10%), those consuming 0–20 pack-years (py) (23%), 20–40 py (31%), 40–60 py (21%) and over 60 py (14%). Alcohol consumption (grams of ethanol per day) groups were categorized into low (0–138 g/day, 24%), medium (139–889 g/day, 25%) and high (>890 g/day, 51%). For comparison, samples of exfoliated mouth epithelial cells samples from healthy subjects included in the analysis as controls were described previously in reference 23.

Table 1.

Patient's information

UADT Tumors (n = 184)
Gender
Male 126
Female 58
Age
≤40 10
41–50 41
51–60 64
61–70 44
71+ 25
Topology
Oral cavity (tongue and floor of mouth) 38
Oropharynx 48
Hypopharynx 21
Larynx 44
Esophagus 26
Other 7
Tobacco (pack years)
Never 20
0–20 42
20–40 58
40–60 38
60+ 26
Alcohol intake (grams per day)
0–138 44
139–189 46
890–3119 46
3120+ 45
ND 3
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Methylation profiles of CDKN2A, MTHFR, TP53, GSTP1, MGMT, CHRNA3, MYOD1 and RASSF1A genes in UADT cancers.

In our selection of relevant genes that are potential targets of aberrant DNA methylation in UADT tumors, we were guided by two criteria: (1) genes that may have an association with UADT carcinogenesis based on their supposed biological function, (2) genes that are newly identified as targets of methylation in cancer and (3) genes that are proposed to be the frequent targets of hypermethylation in human cancers. Results of quantitative analysis of methylation status in UADT tumors and control samples (normal mouth exfoliated epithelial cells) are shown in Figures 1 and S2. Analysis of all the cases for methylation levels showed high levels of methylation in MYOD1, CHRNA3 and MTHFR in tumors. GSTP1, TP53, CDKN2A, MGMT and RASSF1A exhibited relatively low levels of methylation or were unmethylated. Comparison of mean methylation levels of all CpG sites in tumors and exfoliated mouth epithelial cells revealed a highly significant increase in methylation levels in tumors for CHRNA3 (p < 0.0001), MTHFR (p < 0.0001) and MYOD1 (p < 0.0001), as well as a moderate hypermethylation for CDKN2A (p < 0.05), whereas no significant difference in methylation levels for GSTP1, MGMT, RASSF1A and Tp53 between tumor and control samples was observed (Fig. 1).

Figure 1.

Figure 1

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The DNA methylation levels in UADT tumors and control samples. (A) Summary of the analysis of DNA methylation of individual CpG sites in eight genes in UADT tumors and exfoliated epithelial cells (controls). (B) Graphical representation comparing DNA methylation levels in UADT tumors and control samples. Box plots of the summary results obtained by the analysis of the mean levels of all CpG sites analyzed for a given gene and the statistical significance for differential methylation in tumors compared with exfoliated mouth epithelial cells (EEC) samples.

Analysis of DNA methylation frequency (defined as the percentage of tumor samples with methylation levels above 95% quantile levels in control exfoliated epithelial cells samples) showed that three genes (MYOD1, CHRNA3 and MTHFR) were most frequently methylated (82.7%, 69.6% and 64.7%, respectively), three genes (CDKN2A, RASSF1A and MGMT) exhibited intermediate methylation frequency (20.8%, 21.1% and 11.9%, respectively), and the remaining two genes (GSTP1 and TP53) showed low levels of methylation frequency (9.7% and 7.0%, respectively) (Table 2).

Table 2.

Frequency of hypermethylation in UADT tumors compared with DNA methylation level in control exfoliated epithelial cells

Percentage of hyper-methylated samples* Methylation cut-off value**
CDKN2A 20.78 2.57
CHRNA3 69.56 7.93
MTHFR 64.67 50.28
MYOD1 82.75 10.27
GSTP1 9.73 1.57
MGMT 11.89 20.05
RASSF1A 21.08 2.29
p53 7.02 2.3
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*

Samples with methylation levels above the quantile representing the upper 95% of methylation in control exfoliated epithelial cells.

**

Methylation cut-off value was used for dichotomization and is defined as methylation level above which the sample is considered hyper-methylated. For a given gene methylation cut-off value was set at 95% quantile levels in control samples.

Association between methylation levels and risk factor exposure.

We first analyzed associations between the methylation of MTHFR, TP53, CDKN2A, GSTP1, MGMT, CHRNA3, MYOD1 and RASSF1A genes (both mean levels of all CpG sites or individual CpG sites) separately and with available epidemiological and clinical information including sex, age, smoking status and alcohol consumption. These factors were then included in a multivariate analysis. As shown in Table 3, UADT tumors from women exhibited higher methylation levels of CDKN2A (2.0% vs. 10.2%, p < 0.01), MTHFR (49.5% vs. 56.0%, p < 0.01) and MGMT (6.9% vs. 10.8%, p < 0.01) than those from men, whereas methylation levels of RASSF1A, TP53, GSTP1, CHRNA3, MYOD1 were not found to correlate with sex.

Table 3.

DNA methylation levels of genes analyzed in UADT cancers, stratified by sex, age, topology, tobacco consumption and alcohol intake

CDKN2A MTHFR RASSF1A TP53 GSTP1 MGMT CHRNA3 MYOD1
*mean (p value) *mean (p value) *mean (p value) *mean (p value) *mean (p value) *mean (p value) *mean (p value) *mean (p value)
Sex
Men 2.0 (<0.001) 49.51 (<0.001) 2.17 (0.402) 0.31 (0.107) 0.98 (0.669) 6.93 (<0.001) 15.54 (0.002) 24.90 (0.554)
Women 10.22- 56.01- 1.72- 0.62- 1.05- 10.79- 12.25- 25.98-
Age
<40 2.53- 47.70- 2.38- 1.24- 4.01- 5.58- 7.70- 4.84-
40–44 9.4 (0.001) 52.61 (0.138) 0.54 (0.475) 0.04 (0.047) 0.68 (<0.001) 7.19 (0.800) 13.72 (0.122) 29.91 (<0.001)
45–49 4.5 (0.613) 54.65 (0.018) 0.20 (0.338) 0.25 (0.171) 0.31 (<0.001) 8.59 (0.311) 15.07 (0.033) 24.22 (0.005)
50–54 4.7 (0.465) 54.15 (0.016) 2.40 (1.000) 0.79 (0.714) 0.77 (<0.001) 12.04 (0.001) 15.53 (0.010) 28.82 (<0.001)
55–59 1.8 (−0.06) 56.16 (0.001) 3.47 (0.814) 0.33 (0.178) 0.61 (<0.001) 9.67 (0.076) 16.38 (0.003) 29.59 (<0.001)
60–64 11.5 (<0.001) 52.63 (0.110) 5.37 (0.094) 0.39 (0.236) 0.51 (<0.001) 9.90 (0.063) 19.06 (<0.001) 30.87 (<0.001)
65–69 10.1 (<0.001) 52.68 (0.129) 0.32 (0.380) 0.36 (0.245) 0.56 (<0.001) 8.68 (0.285) 14.52 (0.066) 28.37 (0.001)
>70 4.5 (0.983) 51.52 (0.274) 0.92 (0.629) 0.42 (0.262) 0.65 (<0.001) 9.25 (0.137) 9.19 (0.967) 26.88 (0.001)
Alcohol consumption
0–138 1.96- 52.73- 3.03- 0.70- 0.97- 12.59- 23.18- 25.31-
139–889 6.6 (<0.001) 53.32 (0.970) 1.22 (0.037) 0.59 (0.978) 0.85 (0.959) 10.22 (0.042) 13.17 (<0.001) 25.42 (1.000)
890–3119 10.4 (<0.001) 49.23 (0.758) 1.83 (0.264) 0.46 (0.736) 1.23 (0.639) 9.63 (0.006) 9.68 (<0.001) 26.47 (0.919)
3120+ 2.9 (0.991) 55.30 (0.152) 4.53 (0.152) 0.93 (0.832) 1.09 (0.972) 9.80 (0.022) 9.56 (<0.001) 24.56 (0.978)
Tobacco pack/years
Never 1.4- 50.60- 1.85- 0.33- 0.82- 8.94- 12.92- 20.49-
0–20 8.3 (<0.001) 53.09 (0.267) 1.36 (0.930) 0.39 (0.999) 1.31 (0.246) 8.97 (1.000) 16.16 (0.340) 30.47 (0.034)
20–40 9.9 (<0.001) 53.18 (0.178) 2.79 (0.542) 0.65 (0.589) 1.18 (0.430) 8.56 (0.986) 9.72 (0.310) 28.80 (0.068)
40–60 6.1 (<0.001) 54.13 (0.065) −0.66 (0.018) 0.19 (0.967) 0.76 (0.997) 10.07 (0.691) 16.87 (0.201) 25.40 (0.406)
60+ 4.9 (0.003) 52.81 (0.415) 4.40 (0.027) 0.78 (0.450) 1.01 (0.914) 7.77 (0.716) 13.82 (0.961) 22.04 (0.958)
Topography
OP 4.3- 52.91- 3.09- 0.45- 1.16- 11.76- 16.38- 29.62-
HP 5.6 (0.771) 57.30 (0.011) 3.19 (1.000) −0.11 (0.943) 1.56 (0.652) 7.95 (0.003) 11.56 (0.003) 26.52 (0.732)
OC/OP/HP/NOS 22.0 (<0.001) 47.60 (0.287) −2.69 (0.002) 0.57 (1.000) 0.75 (0.983) 8.78 (0.690) 7.77 (0.239) 19.28 (0.316)
Larynx 2.8 (0.584) 52.93 (1.000) 1.27 (0.054) 0.07 (0.612) 0.96 (0.966) 11.15 (0.994) 16.93 (0.998) 23.54 (0.060)
OC/OF/HF/larynx −1.2 (0.212) 58.02 (0.506) −0.49 (0.293) −0.05 (0.987) 0.51 (0.897) 10.25 (0.996) ND ND
Other parts of OC 3.8 (1.000) 55.80 (0.318) 1.11 (0.154) 0.32 (1.000) 1.02 (0.999) 5.53 (<0.001) 9.10 (<0.001) 34.10 (0.666)
Tongue 13.2 (<0.001) 48.09 (0.149) 2.08 (0.980) 1.34 (0.299) 0.99 (1.000) 11.34 (1.000) 25.29 (0.165) 31.57 (1.000)
Floor of mouth 8.8 (0.001) 48.62 (0.067) 1.78 (0.743) 1.12 (0.358) 1.13 (1.000) 8.74 (0.125) 12.16 (0.063) 19.29 (0.002)
Esophagus 6.1 (0.214) 53.68 (0.998) 2.28 (0.923) 0.29 (0.998) 0.80 (0.701) 5.97 (<0.001) ND 26.97 (0.981)
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OC, oral cavity; OP, oropharynx; HP, hypopharynx; *mean of methylation at all CpG sites analyzed at a given gene promoter.

In addition, associations were observed between age and hypermethylation of CDKN2A, MYOD1, MGMT and CHRNA3 (Table 3). MYOD1 exhibited a very high level of methylation in the older age groups (>40 y). Hypomethylation of GSTP1 was significantly associated with all age categories. We also observed increased risk associated with hypermethylation of MTHFR in the 45–59 y age group; however this association was of borderline statistical significance. An increase in DNA methylation level of CDKN2A gene was observed in tumors from smokers, whereas hypermethylation of MYOD1 was observed in both smokers and non-smokers (Table 3).

We observed a significant inverse association between alcohol consumption and methylation of MGMT and CHRNA3, which exhibited lower levels of methylation in all strata. Hypermethylation of CDKN2A in tumors from alcohol drinkers was also observed; however this did not reach statistical significance in any strata.

Discussion

In the present study we quantitatively determined the methylation levels in the promoter regions of eight cancer-related genes in UADT tumors. Our results revealed DNA methylation changes specific for UADT cancers and identified risk factors associated with these changes. In UADT tumors, MYOD1 is the most frequently hypermethylated (82.7%) among the genes, followed by CHRNA3 (69.6%). In addition, there was an increase in hypermethylation of all analyzed CpG sites of MYOD1, CHRNA3 and MTHFR in UADT tumors. These results are consistent with the notion that hypermethylation of tumor suppressor genes and other cancer-associated genes may be selected for and fixed in tumor cells in accordance with the degree of growth advantage caused by their inactivation. Therefore, hypermethylation of MYOD1, CHRNA3 and MTHFR may confer a growth advantage to cancer cells and contribute to the cancer phenotype.

While other studies have reported hypermethylation of many cancer-associated genes, including CDKN2A and MYOD1,14–16,24 our results reveal for the first time aberrant methylation and silencing of CHRNA3 in these cancer types. This suggests that hypermethylation of the CHRNA3 gene, a member of the family of genes encoding subunits of nicotinic acetylcholine receptors (nAChRs) clustered at lung cancer susceptibility locus 15q25,25 that is frequently silenced in lung cancer,26 may also be involved in be epigenetic deregulation of nAChR receptors in UADT tumors. Similarly, our study identifies the frequent hypermethylation of MTHFR, the gene encoding the enzyme known to play a critical role in maintaining an adequate methionine pool,27 in UADT tumors. Because the enzyme MTHFR catalyzes the synthesis of methionine and is required for its metabolite, S-adenosylmethionine (SAM), epigenetic deregulation of the enzyme may further alter DNA methylation states and contribute to the development of UADT cancers. Our observations that the MYOD1 gene, a key player involved in muscle differentiation, is frequently hypermethylated in UADT tumors are consistent with previous studies showing that MYOD1 is commonly inactivated in a broad spectrum of human cancers.28,29 In contrast, we found no marked changes in methylation levels of GSTP1 and Tp53 in UADT tumors, supporting the notion that unscheduled hypermethylation and presumably gene silencing associated with this epigenetic event, does not occur randomly, but rather in a gene-specific manner.5,30

Our data showed that methylation levels of CDKN2A, MTHFR and MGMT in UADT tumors were correlated with sex, with females showing higher levels of methylation than males. There are few studies showing age- and sex-specific differences in DNA methylation level and patterns thereof.31,32 DNA methylation analysis of a small panel of genes suggested that sex is a strong predictor of methylation levels. In contrast, high-resolution methylation profiling of three human chromosomes did not find any significant attributable effect of age and sex on methylation levels.33,34 However, a study of methylation levels in a panel of genes in subjects from a population-based cohort revealed sex differentials in DNA methylation levels of specific genes.32 Our findings are consistent with this notion and argue that gender may strongly influence DNA methylation levels in specific genes. Our results are also consistent with the notion that differences in DNA methylation states between the sexes may constitute the basis for gender-dependent susceptibility to exposures to environmental epimutagens or endogenous toxic agents, and ultimately to differential susceptibility to diseases.35

Our results further revealed that CDKN2A, CHRNA3 and MYOD1 hypermethylation were significantly more frequent in older age groups (>40 y), whereas methylation levels of GSTP1 was decreased. These results indicate that age can influence methylation of distinct loci in a gene-specific manner. Our data revealed higher levels of methylation in CDKN2A among alcohol drinkers, whereas methylation levels of CHRNA3 were significantly lower in tumors of alcohol drinkers. There have been only few reports on associations between DNA methylation changes and alcohol consumption in human cancer, notably cancers of the head and neck, lung, liver and colorectum.31,36–38 Our findings further strengthen the notion that alcohol consumption may interfere with the DNA methylation of specific genes, although the effect of alcohol on methylation levels may be dependent on gene locus-specific context.

Of note, we found a strong association between CDKN2A methylation levels and tobacco smoking status, whereas methylation levels of other genes studied were not significantly associated with smoking status. Methylation levels of CDKN2A were significantly higher in all groups of smokers than among non-smokers. These results suggest that even a short period of smoking is sufficient to induce aberrant methylation of CDKN2A, consistent with a previous study showing that smoking can influence aberrant methylation of specific genes in lung cancer.31 These findings further corroborate previous studies showing that hypermethylation of the CDKN2A promoter is significantly associated with tobacco exposure in head and neck squamous cell carcinoma11,39 and lung cancer,40–42 although the precise underlying mechanism remains to be elucidated. The observation that some genes (such as MYOD1) show low methylation levels in normal exfoliated cells and striking hypermethylation in UADT tumors may reflect the fact that epigenetic inactivation of these genes contributes to tumor development and thus is selected for during oncogenic transformation. However, the lack of association with some of the risk factors under study may be explained by the fact that hypermethylation (and consequent inactivation) of these genes may be a common but relatively late event in tumor development and may be independent of the initial stimuli that trigger oncogenic transformation. Alternatively, the factors (environmental or endogenous) other then those under study may be involved in the deregulation of DNA methylation in these genes. Further studies are required to elucidate the functional impact of differential methylation in specific genes in UADT tumor tissues as well as the causal involvement of specific risk factors in epigenetic deregulation.

In summary, this study provides evidence of gene-specific and sex-specific differences in methylation patterns and their association with various risk factors such as age, smoking and alcohol consumption in UADT tumors. These results also show that promoter methylation of MYOD1, MTHFR and CHRNA3 may serve as a potential biomarker for UADT tumors. Our findings suggest a possible mechanism by which various risk factors may interact with key genes involved in important cellular processes pertinent to tumorigenesis. Although further studies are required to test the functional impact of aberrant methylation of these specific genes, the results of this study could be exploited in biomarker discovery in molecular epidemiology and clinics, and also provide the basis for the development of epigenetics-based strategies for the risk assessment of UADT cancer.

Materials and Methods

Cancer samples and controls.

This study is based on an international multi-center case-control study of UADT cancers conducted in seven centers in South America (São Paulo, Goiania, Rio de Janeiro, Pelotas and Porto Alegre in Brazil; Buenos Aires in Argentina; and La Havana in Cuba).43 Cases were patients with newly diagnosed UADT cancer with no prior treatment. The incident cancer cases and hospital controls were recruited and informed consent was obtained from all study subjects. Each subject answered a detailed lifestyle questionnaire, including basic demographic characteristics and a detailed history of tobacco, alcohol and maté use, which was administered face-to-face in the hospital by a trained interviewer.22,43 Ever-smokers were defined as having smoked on average one cigarette, one cigar or one pipefill a day for at least one year. Individuals who quit smoking more than a year before the interview were considered to be former smokers. Ever-alcohol drinkers were defined as having ever consumed alcoholic drinks. Individuals who quit drinking more than a year before the interview were considered to be former drinkers. Fresh tumor samples were collected from cases whenever possible. Among these, a subset of 184 tumors stratified into several subgroups was selected for DNA methylation analysis (Table 1). The control samples consisting of 45 samples of exfoliated mouth epithelial cells from healthy individuals were also included in the analysis.23 Ethical approvals were obtained from the relevant local ethical committees, and this study was approved by the institutional review board of the International Agency for Research on Cancer (IARC).

DNA extraction.

DNA was extracted from freshly frozen tumors using a standard Qiagen DNA Tissue Kit protocol as described previously in reference 43. A total of 184 UADT tumors clustered in different groups according to associated risk factors were included in DNA methylation analysis (Table 1). In addition, 45 samples of mouth exfoliated epithelial cells were used for extraction of genomic DNA using Buccal AMP DNA extraction kit.

Bisulfite conversion and pyrosequencing analysis of DNA methylation.

DNA methylation levels in the selected panel of genes were examined by pyrosequencing, which allows sensitive and quantitative analysis of DNA methylation at multiple CpG sites as described previously in reference 31. Briefly, genomic DNA (0.5–1 µg) from UADT tumor samples and exfoliated mouth epithelial cells were treated with the EZ DNA Methylation-Gold kit (Zymo Research), according to the manufacturer's protocol. The modified DNA was stored at −20°C until use. In order to investigate the methylation level of the genes under study, targeted sequences localized in bona fide CpG islands were selected for the design of pyrosequencing assays (Fig. S1). For each gene, sets of primers were designed on an in silico modified DNA sequence. DNA amplifications were performed on bisulfitetreated DNA using specific PCR primers (Table S1). Modified DNA (20–25 ng) was amplified in a total volume of 50 µL and 10 µL of the PCR product was analyzed on an agarose gel, whereas the remaining 40 µL was used in a pyrosequencing assay using sequencing primers (Table S1). Pyrosequencing reactions were set up using the PyroGold Reagent kit (Biotage, Sweden) and a pyrosequencing apparatus (PSQ™ 96MA, Biotage, Sweden), according to the manufacturer's instructions. The methylation levels at the target CpGs were evaluated by converting the resulting pyrograms to numerical values for peak heights and expressed either as percentage of methylation of individual CpG sites or as the mean of all CpG analyzed at a given gene promoter.31

Statistical analysis.

All methylation data were generated without knowledge of the exposure status of the subjects or the histological features of the samples analyzed. To compare methylation levels in UADT tumor samples and control samples, we used the Wilcoxon rank-sum test, which allows the comparison of two groups of independent but continuous samples. Multivariate linear regression analysis was performed to test whether any of the risk factors (smoking and alcohol status) or clinical characteristics (sex, age and topology) was associated with DNA methylation. Cumulative tobacco consumption was calculated by multiplying smoking duration (in years) by smoking intensity (in the equivalent of cigarette packs) and expressed as pack-years. Alcohol consumption (grams per day of ethanol) was categorized into four groups (0–138, 139–889, 890–3,119 and <3,120). Analyses were performed using SAS software, version 9.1 (SAS Institute, Inc., Cary, NC). A p value of < 0.01 was considered statistically significant. To assess DNA hypermethylation frequency, we calculated the percentage of tumor samples with methylation levels of >95% quantile levels in exfoliated mouth epithelial cells samples (controls).

Acknowledgments

S. Mani is supported by IARC postdoctoral fellowship. T. Vassière is supported by a Ph.D., fellowship from la Ligue National (Française) Contre le Cancer. The work of the IARC Epigenetics Group is supported by grants from the National Institutes of Health/National Cancer Institute (NIH/NCI), United States; l'Association pour la Recherche sur le Cancer (ARC), France; la Ligue Nationale (Française) Contre le Cancer (Comité Saône-et-Loire), France; and the Swiss Bridge Award. K.S. was supported by an IARC postdoctoral fellowship.

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Supplementary Material

Supplementary Material
epi0703_0270SD1.pdf (2MB, pdf)

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