- Email: [email protected]
NAT2 slow acetylation haplotypes are associated with the increased risk of betel quid–related oral and pharyngeal squamous cell carcinoma
NAT2 slow acetylation haplotypes are associated with the increased risk of betel quid–related oral and pharyngeal squamous cell carcinoma Yu-Yi Hou, MD,a,b Hui-Ling Ou, MS,c,d Sau-Tung Chu, MD,e Pi-Chuang Wu, MS,f,g Pei-Jung Lu, PhD,d,h Chao-Chuan Chi, MS, MD,i Kam-Wing Leung, MD,j,k Chien-Yiing Lee, MD,l Pi-Hsiung Wu, MD,m Michael Hsiao, PhD,d,n and Luo-Ping Ger, MPH,o,p Kaohsiung, Tainan, and Taipei, Taiwan KAOHSIUNG VETERANS GENERAL HOSPITAL, YUH-ING JUNIOR COLLEGE OF HEALTH CARE AND MANAGEMENT, NATIONAL SUN YAT-SEN UNIVERSITY, CHIA-NAN UNIVERSITY OF PHARMACY AND SCIENCE, NATIONAL CHENG-KUNG UNIVERSITY, YUAN’S GENERAL HOSPITAL, ZUOYING ARMED FORCES GENERAL HOSPITAL, AND ACADEMIA SINICA
Background. NAT2, the most important phase II metabolic enzyme for betel quid (BQ), might modify the risk of BQrelated oral and pharyngeal squamous cell carcinoma (OPSCC) in Taiwan. Study design. PCR-RFLP and TaqMan assay were conducted for genotyping of NAT2 in 172 OPSCC cases and 170 healthy controls who habitually chewed BQ. Results. The genotypic and allelic type of T341C and C481T in NAT2 are associated with the risk of OPSCC. There were linear trends between increased risk of OPSCC and slowness of NAT2 acetylation haplotypes (P ⫽ .017), especially for young subjects (P ⬍ .001), light BQ chewers (P ⫽ .005), light smokers (P ⫽ .023), and alcohol drinkers (P ⫽ .001). The interactions on risk of OPSCC were found for NAT2 acetylation haplotypes with status of age, BQ chewing, and alcohol drinking. Conclusions. The NAT2 acetylation haplotypes might be genetic markers for risk of BQ-related OPSCC. (Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2011;112:484-492)
Y.-Y.H and H.-L.O. contributed equally to this study. Portions of this work were supported by grant numbers NSC93-2320B-075B-005, VGHKS95-039, VGHKS96-091, VGHKS97-109, and VGHKS97-073. a Director, Division of Otology, Department of Otorhinolaryngology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan. b Department of Nursing, Yuh-Ing Junior College of Health Care and Management, Kaohsiung, Taiwan. c University graduate, Department of Biological Sciences, National Sun Yat-sen University, Kaohsiung, Taiwan. d Department of Medical Education and Research, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan. e Director, Department of Otorhinolaryncology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan. f Dietitian, Department of Nutrition, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan. g Department of Cosmetic Science, Chia-Nan University of Pharmacy and Science, Tainan, Kaohsiung, Taiwan. h Professor, Institute of Clinical Medicine, National Cheng-Kung University, Tainan, Taiwan. i Director, Division of Laryngology, Department of Otorhinolaryncology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan. j Director, Department of Dentistry, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan. k Department of Dentistry, YUAN’s General Hospital, Kaohsiung, Taiwan. l Director, Department of Dentistry, Zuoying Armed Forces General Hospital, Kaohsiung, Taiwan. m Attending doctor, Department of Otorhinolaryncology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.
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Oral cancer is currently listed as the sixth most common malignancy in the world. In Taiwan, oral cancer (including oral cavity, oropharynx, and hypopharynx) is the fourth leading cancer in men and the most common cancer in young adult males (25-44 years old).1 It is also the sixth leading cause of cancer deaths among males. Since the 1980s, the incidence rate and mortality rate of oral cancer have been increasing in Taiwan. Over the past 10 years, the increase (⬎20%) in the incidence and mortality rates of oral cancer is the greatest among all cancers in Taiwan. Squamous cell carcinoma (SCC) accounts for more than 90% of oral and pharyngeal cancers in Taiwan.2 Betel quid (BQ) chewing is a major contributory factor for oral and pharyngeal SCC in South and Southeast Asia, including India and Taiwan.2,3 It is estimated that 600 million people chew betel quid regularly throughout their lives.4 A recent survey n
Associate investigator, Genomics Research Center, Academia Sinica, Taipei, Taiwan. o Professor, Department of Medical Education and Research, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan. p Institute of Biomedical Sciences, National Sun Yat-sen University, Kaohsiung, Taiwan. Received for publication Oct 17, 2010; returned for revision Mar 17, 2011; accepted for publication Mar 24, 2011. 1079-2104/$ - see front matter © 2011 Mosby, Inc. All rights reserved. doi:10.1016/j.tripleo.2011.03.036
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in Taiwan showed that 8.5% of those 18 years or older were current BQ chewers.5 Taiwanese BQ usually consists of areca nut (AN), Piper betle inflorescence, and lime paste, with or without the leaves of Piper betle.2,5,6 AN contains areca alkaloids, which predominantly consist of arecoline, arecaidine, guvacoline, guvacine, and AN-specific nitrosamines. These AN components along with their auto-oxidation derivatives, such as reactive oxygen species, can attack normal oral mucosal epithelial cells during exposure to ANs.7 Because the AN components require metabolism to exert their carcinogenic effects, the variation of xenobiotic metabolizing activity of important enzymes, which results from genetic polymorphism, may modify cancer risk.8 For the predominant components (arecoline, arecaidine, and guvacine) of AN, N-acetyltransferase (NAT) is the most important enzyme in phase II metabolism (http://ctd.mdibl.org). NATs catalyze the N-acetylation and O-acetylation of many aromatic and heterocyclic amines activated by phase I enzymes.9 In addition, NATs also catalyze the intramolecular N, O-acetyltransferation of N-hydroxy-N-aromatic amines.10 These metabolic reactions can be detoxified or activated, depending on which pathway the NATs take.11,12 Two distinct NATs (NAT1 and NAT2) have been identified in human populations and more than 25 alleles of human NAT1 and NAT2 have been found.8,11 In contrast to the unclear relationship between NAT1 genotype and phenotype, NAT2 encodes the classical acetylation polymorphism yielding rapid, intermediate, and slow acetylator phenotypes.11 Furthermore, several single nucleotide polymorphisms (SNPs) of NAT2 along with their variant NAT2 allozymes have been recombinantly expressed in yeast and resulted in significant differences in NAT catalytic activity, levels of expressed protein, and intrinsic protein stability.8,13 In addition, many experimental studies have revealed that the catalytic activity of NAT2 increases gradually by the following sequence of various haplotypes, such as NAT2ⴱ5A, NAT2ⴱ5B, NAT2ⴱ6B, NAT2ⴱ6A, NAT2ⴱ7A, NAT2ⴱ7B, and NAT2ⴱ4.8,10,14-16 To date, there are no haplotype-phenotype interpretations that are applied in molecular epidemiologic studies to evaluate the relation of NAT2 haplotype and oral cancer. Furthermore, the previous association studies of NAT2 genotypes and oral cancer risk demonstrate controversial results.12,17-20 For a more precise evaluation of the NAT2 genetic effect on BQ-related oral and pharyngeal squamous cell carcinoma (OPSCC) risk, our study used the concept of haplotype, which defined a particular combination of alleles at different loci on NAT2, instead of only the genotype, which is the combination of variants at a locus that are present in
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an individual. It is worthwhile to evaluate the association of NAT2 acetylation haplotypes and susceptibility to BQ-related OPSCC in Taiwan. Therefore, the objectives of present study were to evaluate the association of NAT2 common polymorphisms (T341C, C481T, G590A, A803G, and G857A) in BQ chewers with the risk of OPSCC and to evaluate how these associations were influenced by demographic factors and substance use. MATERIAL AND METHODS Study subjects In this study, 342 male subjects with the betel quid– chewing habit were recruited: 170 healthy controls and 172 OPSCC case patients (including 141 oral cavity, 20 oropharynx, and 11 hypopharynx cases). The case patients with newly diagnosed, previously untreated, and pathologically confirmed primaries of OPSCC were recruited between January 2004 and October 2006 from the Department of ENT and Dentistry at Kaohsiung Veterans General Hospital (KSVGH). The 170 healthy controls were recruited from the oral health screening clinic at the Department of Otolaryngology, KSVGH, and from the oral health screening campaigns for vehicle drivers, cleaners, and hardware workers held by the Kaohsiung city government or the Department of Otolaryngology, KSVGH, between 2004 and 2006. The selection criteria for controls included no individual history of cancer or oral precancerous lesions (i.e., oral submucous fibrosis, leukoplakia, erythroplakia, and lichen planus), which was confirmed by the screening physicians. All controls were frequency-matched to case patients on age (⫾5 years), ethnicity, and years of BQ chewing. In addition, the exclusion criteria for both case and control subjects were patients with renal dysfunction, psychosis, chemotherapy, or blood transfusion in the previous 3 months. After informed consent was obtained at subject recruitment, each subject was interviewed by use of a structured questionnaire that elicited detailed information on the history of betel quid-chewing, cigarettesmoking, and alcohol-drinking habits; occupation; family disease; and general demographic data (i.e., age, sex, education, and ethnicity). At the end of the interview, each subject donated 7 to 15 mL of blood in ethylene diamine tetra-acetic acid (EDTA) tube under the protocol approved by the Institutional Review Board of KSVGH. A BQ chewer was defined as a person who had chewed at least 1 betel quid per day for at least 1 year. Pack-years of BQ consumption were calculated using the following: pack-years ⫽ (mean number of betel quids chewed per day/20 betel quids) ⫻ number of
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years of chewing. Light and heavy BQ chewers were categorized by using median values (20 pack-years) of BQ consumption in controls as the cutoff point. A smoker was defined as a person who had smoked at least 1 cigarette per day for at least 1 year. Pack-years of cigarette consumption were calculated using the following: pack-years ⫽ (mean number of cigarettes smoked per day/20 cigarettes) ⫻ number of years of smoking. Light and heavy smokers were categorized by using median value (31 pack-years) of cigarette consumption in controls as the cutoff point. A drinker was defined as a person who had consumed alcohol at least once a week for more than 1 year. Gram-years of alcohol consumption were calculated using the following: gram-years ⫽ mean grams of alcohol consumed per day ⫻ number of years consuming alcohol. Light and heavy drinkers were also categorized by using median value (1150 gram-years) of alcohol consumption in controls as the cutoff point. Blood sample and DNA extraction The whole blood was separated into plasma, buffy coat cells, and red blood cells by centrifugation within 24 hours of obtaining the blood. The separated buffy coat cells were stored at – 80°C before DNA extraction. Genomic DNA for genotyping was extracted with QIAamp DNA Midi kit through commercial protocols (Qiagen, Inc., Valencia, CA). DNA concentration was then determined using a spectrophotometer at 260 nm. Genotype analysis A total of 5 SNPs of the NAT2 gene were assayed in this study: T341C, C481T, G590A, A803G, and G857A. Approximately 60% of the samples were genotyped using the polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP) reported by Chen et al.19 and Malik et al.21 The remaining 40% of samples were detected using the previously reported TaqMan real-time PCR method.22 Quality control procedures were implemented to ensure high genotyping accuracy in our laboratory. A sample of each genotype was randomly selected for DNA sequencing to verify the allele sequence and the results were 100% concordant with the initial analysis. About 5% of samples were randomly selected for repeated assays. The Applied Biosystems 7500 real-time PCR system and TaqMan probes (Applied Biosystems, Foster City, CA, USA) were used to run in duplicate to ensure the accuracy of the genotyping from PCR-RFLP and vice versa. In addition, a senior researcher independently reviewed all of the agarose gels in PCRRFLP assays and absolute quantification curves for fluorescence data in TaqMan assays, data entering, and statistical analyses independently.
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HAPLOTYPE ANALYSIS The computational haplotyping method was used in this study. The haplotypes and their frequencies were estimated with SAS/Genetics (Version 9.1.3; SAS Institute, Cary, NC), based on the expectation–maximization algorithm.23 For the functional NAT2 allele nomenclature, we referred to the list demonstrated by an international gene nomenclature committee (http://www.louisville. edu/medschool/pharmacology/NAT.html). The simultaneous nucleotide substitution at the NAT2 allele with nucleotides 341, 481, and 803 represents the haplotype NAT2ⴱ5B; both nucleotide substitutions at 341 and 481 represent the haplotype NAT2ⴱ5A; a single nucleotide substitution at the NAT2 allele nucleotide 590 represents the haplotype NAT2ⴱ6B; a single nucleotide substitution at the NAT2 allele nucleotide 857 represents the haplotype NAT2ⴱ7A; a single substitution at nucleotide 803 represents the haplotype NAT2ⴱ12A; and the wild type with no nucleotide substitution is referred to NAT2ⴱ4. In addition, the NAT2 acetylation haplotypes were classified by the slow degree of acetylation10 in the following sequence: NAT2ⴱ7A, NAT2ⴱ6B, NAT2ⴱ5B, NAT2ⴱ5A, and NAT2ⴱ4. To clearly evaluate the relation of the NAT2 genotype to OPSCC risk, we classified all genotypes into 3 inferred acetylation phenotypes: rapid (NAT2ⴱ4/ NAT2ⴱ4), intermediate (NAT2ⴱ4/others), and slow acetylators (homozygotes other than NAT2ⴱ4) according to a previous study.24 Statistical analysis The discrepancies from the Hardy-Weinberg equilibrium of each NAT2 SNP in the control group were assessed by comparing the observed and expected genotype frequencies using the goodness-of-fit test. The mean differences of age between case and control groups were evaluated by t test. Logistic regression was performed to evaluate the association of BQ-related OPSCC with demographic, substance use, genotype, and haplotype data. The crude odds ratios (CORs) and their 95% confidence intervals (CIs) were used to estimate the distribution of demographic data and substance use between BQ-related OPSCC and BQ-chewing controls. The multivariate logistic regression was used to evaluate the adjusted odds ratios (AORs) and 95% CIs for each genotype or haplotype relative to the reference 1 by adjusting age and BQ-chewing, cigarette-smoking, and alcohol-drinking status. SPSS (version 12.0; SPSS, Chicago, IL, USA) and SAS/GENETICS (version 9.1.3; SAS Institute Inc., Cary, NC, USA) were used to perform all statistical analyses. A P value less than .05 was considered statistically significant.
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Table I. Distribution and odds ratios for BQ-associated OPSCC cases and healthy controls by demographic and substance use features Factor/Category
Cases (n ⫽ 172) n (%)
Controls (n ⫽ 170) n (%)
COR (95% CI)
Age, mean ⫾ SD, y ⬍40 40-49 50-59 ⱖ60 Ethnicity Fukienese Hakka Mainlander Aborigines Years of education ⱕ6 7-12 ⬎12 BQ chewing (pack†-years) ⱕ20 ⬎20 Smoking (pack‡ -years) Never-smoker 1-31 ⬎31 Drinking (gram-years) Never-drinker 1-1150 ⬎1150
49.6 ⫾ 8.5 20 (11.6) 62 (36.0) 72 (41.9) 18 (10.5)
49.6 ⫾ 8.4 19 (11.2) 61 (35.9) 73 (42.9) 17 (10.0)
1.00 0.97 (0.47-1.99) 0.94 (0.46-1.90) 1.01 (0.40-2.51)
138 (80.2) 13 (7.6) 18 (10.5) 3 (1.7)
133 (78.2) 13 (7.6) 21 (12.4) 3 (1.8)
1.00 0.96 (0.43-2.16) 0.83 (0.42-1.62) 0.96 (0.19-4.86)
61 (35.5) 94 (54.7) 17 (9.9)
56 (32.9) 98 (57.6) 16 (9.4)
1.00 0.88 (0.56-1.40) 0.98 (0.45-2.11)
.588 .950
80 (46.5) 92 (53.5)
85 (50.0) 85 (50.0)
1.00 1.15 (0.75-1.76)
.519
17 (9.9) 92 (53.5) 63 (36.6)
12 (7.1) 80 (47.1) 78 (45.9)
1.00 0.81 (0.37-1.80) 0.57 (0.25-1.28)
.608 .174
38 (22.1) 68 (39.5) 66 (38.4)
66 (38.8) 52 (30.6) 52 (30.6)
1.00 2.27 (1.33-3.89) 2.20 (1.29-3.78)
.003 .004
P value .960* .924 .857 .990 .928 .578 .964
P value is estimated by logistic regression. BQ, betel quid; CI, confidence interval; COR, crude odds ratios; OPSCC, oral and pharyngeal squamous cell carcinoma. *P value is estimated by t test. †Twenty betel quids per pack. ‡Twenty cigarettes per pack.
RESULTS The distribution and COR ratios for 172 OPSCC cases and 170 healthy controls with demographic and substance use features are summarized in Table I. No statistical differences were found between case and control groups in terms of mean age (49.6 ⫾ 8.5 and 49.6 ⫾ 8.4 years, respectively). There were no significant differences between cases and controls by ethnicity, years of education, pack-years of BQ chewing, and pack-years of smoking. More than 90% of BQ chewers are smokers and 70% of BQ chewers are alcohol drinkers in Taiwan. Although an effort was made to achieve a frequency match on years of BQ chewing between cases and controls, alcohol drinking, either light or heavy drinking was more prevalent in the cases than the controls. Therefore, alcohol drinking was further adjusted in later multivariate analysis. The genotype frequencies for the 5 single nucleotide substitution of NAT2 in controls did not differ from expected distributions based on Hardy-Weinberg equilibrium. The respective P values were .663, .663, .957, .604, and .989 for loci T341C, C481T, G590A, A803G, and G857A. The associations between the 5 SNPs of
NAT2 gene and BQ-related OPSCC risk were without any significant differences, except T341C and C481T (Table II). The combined genotypes of T/C⫹C/C at T341C and C/T⫹T/T at C481T were associated with a 2.39-fold increased risk of BQ-related OPSCC as compared with the wild homozygous genotypes of T/T at T341C (P ⫽ .31) and C/C at C481T (P ⫽ .31), respectively. In addition, the allelic type C of T341C and T of C481T were correlated with a 2.26-fold increased risk of OPSCC as compared with the wild allelic type T of T341C (P ⫽ .037) and C of C481T (P ⫽ .037), respectively. According to other studies, we identified the subjects as carriers of NAT2 rapid acetylator genotypes, NAT2 intermediate acetylator genotypes, and NAT2 slow acetylator genotypes. No associations were found between various acetylation genotypes and risk of BQ-related OPSCC, except in the slow acetylator genotype there was a borderline significant difference. Using the carriers with rapid acetylator as the reference group, those with slow acetylator genotype had a 1.69-fold increased risk of developing BQ-related OPSCC (P ⫽ .093). A strong linkage disequilibrium (LD) was observed between NAT2 5 SNPs; D= values were in the range of
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Table II. Association of NAT2 genotypes and acetylation genotypes with the risk of OPSCC NAT2 Genotype T341C T/T T/C C/C T allele C allele C481T C/C C/T T/T C allele T allele G590A G/G G/A A/A G allele A allele A803G A/A A/G G/G A allele G allele G857A G/G G/A A/A G allele A allele Acetylation Genotype Rapid Intermediate Slow
Crude
Cases (n ⫽ 172) n (%)
Controls (n ⫽ 170) n (%)
150 (87.2) 22 (12.8) 0 (0) 322 (93.6) 22 (6.4)
159 (93.5) 11 (6.5) 0 (0) 329 (96.8) 11 (3.2)
150 (87.2) 22 (12.8) 0 (0) 322 (93.6) 22 (6.4)
159 (93.5) 11 (6.5) 0 (0) 329 (96.8) 11 (3.2)
88 (51.2) 68 (39.5) 16 (9.3) 244 (70.9) 100 (29.1)
97 (57.1) 63 (37.1) 10 (5.9) 257 (75.6) 83 (24.4)
1.00 1.19 (0.76-1.86) 1.76 (0.76-4.09) 1.00 1.27 (0.90-1.78)
152 (88.4) 20 (11.6) 0 (0) 324 (94.2) 20 (5.8)
157 (92.4) 13 (7.6) 0 (0) 327 (96.2) 13 (3.8)
1.00
126 (73.3) 42 (24.4) 4 (2.3) 294 (85.5) 50 (14.5)
122 (71.8) 44 (25.9) 4 (2.4) 288 (84.7) 52 (15.3)
46 (26.7) 80 (46.5) 46 (26.7)
55 (32.4) 82 (48.2) 33 (19.4)
OR (95% CI)
Adjusted P value
1.00
OR* (95% CI)
P value
1.00
2.12 (0.99-4.52)
.052
2.39 (1.08-5.27)
.031
1.00 2.04 (0.98-4.28)
.058
1.00 2.26 (1.05-4.87)
.037
1.00
1.00
2.12 (0.99-4.52)
.052
2.39 (1.08-5.27)
.031
1.00 2.04 (0.98-4.28)
.058
1.00 2.26 (1.05-4.87)
.037
.447 .186 .169
1.00 1.22 (0.77-1.94) 1.64 (0.69-3.90) 1.00 1.26 (0.89-1.78)
.397 .261 .199
1.00
1.59 (0.76-3.31)
.216
1.80 (0.84-3.87)
.133
1.00 1.55 (0.76-3.17)
.228
1.00 1.74 (0.83-3.64)
.145
1.00 0.92 (0.57-1.51) 0.97 (0.24-3.96) 1.00 0.94 (0.62-1.44)
.753 .964 .780
1.00 1.17 (0.71-1.92) .545 1.67 (0.92-3.02) .092 P for linear trend ⫽ .099
1.00 0.97 (0.58-1.63) 0.77 (0.18-3.26) 1.00 0.94 (0.61-1.45)
.910 .724 .780
1.00 1.24 (0.74-2.08) .409 1.69 (0.92-3.11) .093 P for linear trend ⫽ .095
P value is estimated by logistic regression. CI, confidence interval; OPSCC, oral and pharyngeal squamous cell carcinoma; OR, odds ratios. *Adjusted for age, pack-years of betel quid chewing, and cigarette smoking as well as the gram-years of alcohol drinking by multiple logistic regression.
0.936 and 1.00, except T341C and C481T were with complete LD (D= ⫽ 1.00 and R2 ⫽ 1.00). The association between NAT2 acetylation haplotypes and BQrelated OPSCC is displayed in Table III. The individuals with NAT2ⴱ5A or NAT2ⴱ5B haplotypes, which represent the slowest acetylators, had higher risk of BQ-related OPSCC (AOR ⫽ 2.48, 95% CI ⫽ 1.145.40, P ⫽ .022) as compared with those with the wild type NAT2ⴱ4. On the contrary, the haplotypes that represent the intermediate acetylators had a minor influence on the BQ-related OPSCC occurrence compared with the NAT2ⴱ4 (AOR ⫽ 1.05 and 1.35 for NAT2ⴱ7A ⫹ NAT2 ⫻ 12 A and NAT2ⴱ6B, respectively), although no statistical significance was found.
However, a linear trend (P ⫽ .017) was shown between the risk of BQ-related OPSCC and slowness of acetylation haplotypes, which might suggest that subjects with the slower acetylation haplotype were inclined to have a greater risk of BQ-related OPSCC. To evaluate the gene–substance use interactions, age and various substance use were stratified to examine the impact of the NAT2 acetylation haplotype on the risk of BQ-related OPSCC (shown in Table IV). Risks associated with NAT2 acetylation haplotypes are significantly different by status of age group, BQ chewing, and alcohol drinking but not for status of cigarette smoking. The NAT2 slower acetylation haplotypes (NAT2ⴱ6B; NAT2ⴱ5A ⫹ 5B) were correlated with the
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Table III. NAT2 acetylation haplotypes associated with the risk of OPSCC Haplotype
Cases (n ⫽ 344) Number (%)
Controls (n ⫽ 340) Number (%)
341,481,590,803,857 (nomenclature) T C G A G (NAT2* 4) T C G A A (NAT2* 7A ⫹ 12 A) T C A A G (NAT2*6B) C T G G G (NAT2* 5B) C T G A G (NAT2* 5 A)
172 (50.0) 50 (14.5) 100 (29.1) 20 (5.8) 2 (0.6)
193 (56.8) 53 (15.6) 83 (24.4) 11 (3.2) 0 (0.0)
Crude
Adjusted
OR (95% CI)
P value
OR* (95% CI)
P value
1.00 1.06 (0.68-1.64) 1.35 (0.95-1.93) 2.24 (1.06-4.76)
.799 .097 .035
1.00 1.05 (0.67-1.65) 1.35 (0.93-1.94) 2.48 (1.14-5.40)
.837 .113 .022
P for linear trend ⫽ .019
P for linear trend ⫽ .017
P value is estimated by logistic regression. CI, confidence interval; OPSCC, oral and pharyngeal squamous cell carcinoma; OR, odds ratios. *Adjusted for age, pack-years of betel quid chewing, and cigarette smoking as well as the gram-years of alcohol drinking by multiple logistic regression.
greater risk of BQ-related OPSCC in the stratum of young subjects (Plineartrend ⬍ .001), but not in the stratum of old subjects (Plineartrend ⬍ .908). Therefore, young subjects with slower acetylation genotypes had a significantly increased risk of BQ-related OPSCC and this risk was clearly greater than that of older subjects (Pinteraction ⫽ .009). When it came to status of BQ chewing, the slower NAT2 acetylation haplotypes were correlated with the greater risk of BQ-related OPSCC in the stratum of light chewers (ⱕ20 pack-years; Plineartrend ⫽ .005). However, in the stratum of heavy chewers, no significant associations or linear trends were found between various NAT2 haplotypes and BQ-related OPSCC risk (Plineartrend ⫽ .587). In addition, a borderline significant interaction was found between NAT2 haplotype and risk of OPSCC (Pinteraction ⫽ .046). When we divided subjects into never-drinkers and alcohol drinkers, slower NAT2 acetylation haplotypes (NAT2ⴱ6B; NAT2ⴱ5A ⫹ 5B) had a significantly increased risk of OPSCC and a linear trend was also found in the stratum of drinkers (Plineartrend ⫽ .001). However, slower acetylation haplotypes were not correlated with the risk of BQ-related OPSCC in the stratum of never-drinkers. Therefore, the influence of NAT2 slower haplotypes on the risk of OPSCC appeared to be greater in alcohol drinkers than in never-drinkers (Pinteraction ⫽ .010). These results might suggest that the role of NAT2 haplotypes in BQ-related OPSCC risk would correlate with environmental exposures. DISCUSSION In this study, a linear trend was shown between the increased risk of BQ-related OPSCC and the NAT2 slow acetylation haplotype (Table III), especially for those who were young subjects, light BQ chewers, light smokers (including a few never-smokers), and alcohol drinkers (Table IV). Additionally, we found that the impact of NAT2 slower haplotypes on risk of BQ-
related OPSCC were significantly higher in young subjects than in old subjects, in light BQ chewers than in heavy BQ chewers, and in alcohol drinkers than in never-drinkers (Table IV). To our knowledge, this is the first study to use the concept of haplotype to represent different degrees of acetylation efficiency (phenotype) of NAT2 enzymes on risk of BQ-related OPSCC and gene-substance use interaction. We found a linear trend between NAT2 slow acetylation haplotypes and increased risk of BQ-related OPSCC, which was supported by in vitro studies.8,10,14-16 Therefore, the carriers with haplotypes of the slower acetylator (NAT2ⴱ5B and NAT2ⴱ5A) would have inefficient elimination of carcinogens and would be more inclined to develop BQ-related OPSCC than those with the haplotypes of the rapid acetylator (NAT2ⴱ4). However, some epidemiologic studies on the association of oral cancer and NAT2 polymorphisms were not consistent with ours. Two studies demonstrated that rapid or intermediate acetylation genotypes increased the risk of oral cancer12,19 and 2 other studies showed that acetylator status does not have any impact on risk of oral cancer.17,18 Nevertheless, several supportive studies were also reported. Gara et al.25 demonstrated that NAT2 T341C substitution, which might reduce enzyme activity, is associated with an increased risk of head and neck cancer. More recently, the results revealed by Malik et al.,21 who used the concept of haplotypes, were also in accordance with ours. These very inconsistent results might arise from the following 4 reasons. First, the number of target SNPs and sites of SNPs in previous studies of oral cancer12,17-20 were quite different from ours, except for Jourenkova-Mironova et al.’s survey.20 A recent functional characterization study of 10 SNPs and 4 common haplotypes of NAT2 showed that some coding region SNPs or haplotypes have different slow acetylator phenotypes by various molecular mechanisms in mammalian cells.16 Therefore, the sur-
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Table IV. NAT2 acetylation haplotypes and risk of BQ-related OPSCC by various age and substance use features Crude Factor category Age, y Young group (⬍50) (82 cases/80 control)
Old group (ⱖ50) (90 cases/ 90 control)
BQ chewing (pack -years) Light chewer (ⱕ20) (80 cases/85 control)
Heavy chewer (⬎20) (92 cases/85 control)
Smoking (pack-years) Never-or light smoker (ⱕ31) (109 cases/92 control)
Heavy smoker ⬎31 (63 cases/78 control)
Alcohol drinking (gram-years) Never-drinker (38 cases/66 control)
Drinker (134 cases/104 control)
Haplotype
CS/CN
NAT2* 4 NAT2*7A ⫹ 12 A NAT2* 6B NAT2*5A ⫹ 5B
72/96 24/25 57/34 11/5
NAT2* 4 NAT2*7A ⫹ 12 A NAT2* 6B NAT2*5A ⫹ 5B
100/97 26/28 43/49 11/6
NAT2* 4 NAT2*7A ⫹ 12 A NAT2* 6B NAT2*5A ⫹ 5B
73/101 29/27 50/38 8/4
NAT2* 4 NAT2*7A ⫹ 12 A NAT2* 6B NAT2*5A ⫹ 5B
99/92 21/26 50/45 14/7
NAT2* 4 NAT2*7A ⫹ 12 A NAT2* 6B NAT2*5A ⫹ 5B
106/104 35/34 65/41 12/5
NAT2* 4 NAT2*7A ⫹ 12 A NAT2* 6B NAT2*5A ⫹ 5B
66/89 15/19 35/42 10/6
NAT2* 4 NAT2*7A ⫹ 12 A NAT2* 6B NAT2*5A ⫹ 5B
44/68 11/23 17/33 4/8
NAT2* 4 NAT2*7A ⫹ 12 A NAT2* 6B NAT2*5A ⫹ 5B
128/125 39/30 83/50 18/3
OR (95% CI)
Adjusted P value
OR* (95% CI)
P value
1.00 1.28 (0.68-2.42) .448 2.24 (1.33-3.77) .003 2.93 (0.98-8.82) .055 P for linear trend ⫽ .001 1.00 0.90 (0.49-1.65) .734 0.85 (0.52-1.40) .524 1.78 (0.63-4.50) .275 P for linear trend ⫽ .957
1.00 1.33 (0.68-2.62) .403 2.41 (1.39-4.20) .002 3.56 (1.12-11.31) .031 P for linear trend ⬍.001 1.00 0.88 (0.47-1.63) .682 0.80 (0.48-1.33) .390 1.83 (0.63-5.31) .265 P for linear trend ⫽ .908
1.00 1.49 (0.81-2.72) .199 1.82 (1.08-3.06) .023 2.77 (0.80-9.54) .107 P for linear trend ⫽ .008 1.00 0.75 (0.40-1.43) .381 1.03 (0.63-1.69) .899 1.86 (0.72-4.81) .201 P for linear trend ⫽ .457
1.00 1.38 (0.72-2.62) .331 1.92 (1.11-3.32) .019 3.59 (0.97-13.35) .056 P for linear trend ⫽ .005 1.00 0.75 (0.39-1.45) .396 1.00 (0.60-1.66) .994 1.72 (0.65-4.56) .278 P for linear trend ⫽ .587
1.00 1.01 (0.59-1.74) .971 1.56 (0.97-2.50) .069 2.36 (0.80-6.92) .119 P for linear trend ⫽ .030 1.00 1.07 (0.50-2.25) .870 1.12 (0.65-1.95) .678 2.25 (0.78-6.49) .135 P for linear trend ⫽ .259
1.00 1.02 (0.58-1.79) .943 1.61 (0.98-2.63) .060 2.63 (0.85-8.09) .093 P for linear trend ⫽ .023 1.00 1.04 (0.47-2.27) .928 1.19 (0.66-2.12) .565 2.40 (0.79-7.25) .121 P for linear trend ⫽ .908
1.00 0.74 (0.33-1.67) .466 0.80 (0.40-1.60) .522 0.77 (0.22-2.72) .688 P for linear trend ⫽ .456 1.00 1.27 (0.74-2.17) .383 1.62 (1.06-2.49) .027 5.86 (1.68-20.39) .005 P for linear trend ⫽ .001
1.00 0.80 (0.35-1.85) .607 0.77 (0.37-1.57) .465 0.69 (0.19-2.55) .578 P for linear trend ⫽ .380 1.00 1.19 (0.69-2.05) .541 1.65 (1.07-2.54) .024 6.18 (1.76-21.72) .005 P for linear trend ⫽ .001
P for interaction
.009
.046
.487
.010
P value is estimated by logistic regression. BQ, betel quid; CI, confidence interval; COR, crude odds ratios; OPSCC, oral and pharyngeal squamous cell carcinoma. *All odds ratios were adjusted for age, pack-years of betel quid chewing, and cigarette smoking as well as the gram-years of alcohol drinking, where it was appropriate.
veyed target SNPs were different, resulting in different findings. Second, the disparity of carcinogen exposures might affect which reactions the NAT2 catalyzed, either detoxified N-acetylation or activated O-acetylation.12,17-20 The carcinogens from BQ chewing are different from smoking and alcohol drinking. In our study, all subjects
were exposed to carcinogens from BQ chewing, which might affect the ability of NAT2 to catalyze by use of detoxified N-acetylation but not activated O-acetylation. In a review, Hein26 pointed out that NAT2 acetylator status modifies risks for developing head and neck squamous cell carcinoma when aromatic and heterocyclic amine carcinogen exposures were added. This is in
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accordance with our findings that the role of NAT2 haplotypes in OPSCC risk might depend on slight exposures to environmental risk factors. Third, the ethnicity in our study is quite different from other studies. Various susceptibilities to OPSCC might exist between different ethnicities either from genetic effect or from various substance use exposures. Among the control subjects in various studies, the genotype frequency of rapid acetylator (NAT2ⴱ4/ NAT2ⴱ4) in our study (32.4%) were similar to those reported for Brazilians (31%),12 but smaller than those (50.0%) for Japanese,18 and greater than those (5.4%, 6.4%, and 8.3%) reported for German,17 American,19 and French,20 respectively. Although all other studies have not used the haplotype to evaluate the effect of NAT2 on oral cancer risk, we believe that the lower frequency of specific NAT2 haplotypes (such as NAT2ⴱ4) in certain populations (such as Caucasians) would decrease the statistical power for their studies. Fourth, we used the specific haplotype (NAT2 acetylation haplotype), which defined the NAT2 allele with several SNPs instead of only the genotype with a single SNP. Recently, the study from Malik et al.,21 which coincides with ours, also demonstrated the impact of NAT2 haplotypes instead of NAT2 single SNP on cancer occurrence. However, most of the studies with opposite results came from genotype analyses of single SNP or several SNPs17,18,27 without considering the role of haplotype in determining NAT2 enzyme activity. The detection of haplotype involvement possibly resulted in the different outcomes of studies because of the allele effect with various acetylation (slow or rapid)8,10,14-16 and the increased statistical power.14,22 Genetic susceptibility to head and neck cancer is evidenced by an early age at onset.28 We found that young subjects with NAT2 slower acetylation haplotypes had an increased risk of BQ-related OPSCC and this risk was significantly greater than those of older subjects (Table IV). After analysis of our questionnaire data on BQ-chewing history, we found that the types of BQ preparations used in subjects’ young age were comparable between the young (⬍50 years) and old (ⱖ50 years) subjects (data not shown). In addition, the market survey showed that there have been no changes in the types of BQ preparation used in Taiwan in the past 50 years. Therefore, these results still support that age could modify the NAT2 genetic contribution to risk of OPSCC. An in vivo study on age effect found that old rats are insensitive to magnetic fields because of their decreased pineal NAT activity.29 We suggested that NAT2 enzyme activity for older subjects might decrease to a lower level and the influence of genetic variation on risk of OPSCC would be minor. However,
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the exact cause is still unknown and needs to be evaluated in the near future. The results for association based on the interaction of NAT2 acetylation haplotypes and substance use exposure showed in the status of light BQ chewers (compared with heavy BQ chewers) and drinkers (compared with never-drinkers), but not in smoking status (Table IV). This result coincides with our hypothesis that NAT2 plays a vital role in clearance of BQ-related carcinogens. However, in the group of heavy BQ chewers, the level of BQ-related carcinogens might be too high to be effectively metabolized by NAT2. Therefore, BQ chewing could modify the contribution of NAT2 haplotypes to cancer risk by different degree of BQ exposures. The exact cause needs be evaluated in the near future. In the aspect of alcohol drinking, Li et al. demonstrated that a certain NAT1 genotype decreased risk of diffuse large B-cell lymphoma among alcohol drinkers, but not among nondrinking subjects.30 Our findings showed similar results that the genotypes of NAT2 would modify the risk of OPSCC in alcohol drinkers, but not in never-drinkers. However, this result was not shown in smoking status (Table IV), which was similar to the interaction findings of smoking and NAT2 in esophageal and gastric cancers from Malik et al.21 We found that both never (or light smokers) or heavy smokers carrying NAT2 slow acetylation haplotypes had higher risk for BQ-related OPSCC (Table IV). However, no statistical significance was found, which may be because of the small sample size of the study. Therefore, the interaction of smoking and NAT2 genotypes on risk of OPSCC is suggested to be validated in a larger cohort. Limitations to this study need to be addressed. This is a single-center investigation with a limited number of patients. Stratification analysis according to the subsites (oral cavity, oropharynx, and hypopharynx) of OPSCC was not carried out because of very small sample size in subsites, especially for the oropharynx and hypopharynx. The strength of association is fairly small; it means that NAT2 slow acetylation haplotypes contribute slightly to OPSCC risk. More studies on NAT2 and other susceptible genes, and other lifestyle-related risk factors (such as human papilloma virus) in independent and large cohorts are suggested. In conclusion, this study showed for the first time a linear trend between increased risk of BQ-related OPSCC and NAT2 slow acetylation haplotypes, especially for those in the young group, light BQ chewers, light smokers, and alcohol drinkers. Additionally, gene-substance use interactions were also found on risk of BQ-related OPSCC. More replication studies with large cohorts in other ethnic groups are needed to confirm our findings.
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We appreciate the Department of Health, Kaohsiung city government, for letting us take part in their oral health screening campaigns for vehicle drivers, cleaners, and hardware workers. We also express our appreciation to Chia-Yu Lee for assistance in English editing. REFERENCES 1. Department of Health (DOH). Executive Yuan, Taiwan. Health Statistics in Taiwan. Stat Causes Death 2006;part II;2008:2-34. 2. Ko YC, Huang YL, Lee CH, Chen MJ, Lin LM, Tsai CC. Betel quid chewing, cigarette smoking and alcohol consumption related to oral cancer in Taiwan. J Oral Pathol Med 1995;24:450-3. 3. Walker DM, Boey G, McDonald LA. The pathology of oral cancer. Pathology 2003;35:376-83. 4. Reichart PA, Philipsen HP. Betel and Miang: Vanishing Thai Habits. Bangkok: Bangkok White Lotus, Ltd; 2005. 5. Yang YH, Chen CH, Chang JS, Lin CC, Cheng TC, Shieh TY. Incidence rates of oral cancer and oral pre-cancerous lesions in a 6-year follow-up study of a Taiwanese aboriginal community. J Oral Pathol Med 2005;34:596-601. 6. Chen CL, Chi CW, Chang KW, Liu TY. Safrole-like DNA adducts in oral tissue from oral cancer patients with a betel quid chewing history. Carcinogenesis 1999;20:2331-4. 7. Jeng JH, Chang MC, Hahn LJ. Role of areca nut in betel quid-associated chemical carcinogenesis: current awareness and future perspectives. Oral Oncol 2001;37:477-92. 8. Hein DW. Molecular genetics and function of NAT1 and NAT2: role in aromatic amine metabolism and carcinogenesis. Mutat Res 2002;506 –507:65-77. 9. Minchin RF, Reeves PT, Teitel CH, McManus ME, Mojarrabi B, Ilett KF, et al. N- and O-acetylation of aromatic and heterocyclic amine carcinogens by human monomorphic and polymorphic acetyltransferases expressed in COS-1 cells. Biochem Biophys Res Commun 1992;185:839-44. 10. Hein DW, Doll MA, Rustan TD, Ferguson RJ. Metabolic activation of N-hydroxyarylamines and N-hydroxyarylamides by 16 recombinant human NAT2 allozymes: effects of 7 specific NAT2 nucleic acid substitutions. Cancer Res 1995;55:3531-6. 11. Grant DM, Hughes NC, Janezic SA, Goodfellow GH, Chen HJ, Gaedigk A, et al. Human acetyltransferase polymorphisms. Mutat Res 1997;376:61-70. 12. Marques CF, Koifman S, Koifman RJ, Boffetta P, Brennan P, Hatagima A. Influence of CYP1A1, CYP2E1, GSTM3 and NAT2 genetic polymorphisms in oral cancer susceptibility: results from a case-control study in Rio de Janeiro. Oral Oncol 2006;42:632-7. 13. Chen B, Zhang WX, Cai WM. The influence of various genotypes on the metabolic activity of NAT2 in a Chinese population. Eur J Clin Pharmacol 2006;62:355-9. 14. Hein DW, Doll MA, Fretland AJ, Leff MA, Webb SJ, Xiao GH, et al. Molecular genetics and epidemiology of the NAT1 and NAT2 acetylation polymorphisms. Cancer Epidemiol Biomarkers Prev 2000;9:29-42. 15. Hein DW. N-acetyltransferase 2 genetic polymorphism: effects of carcinogen and haplotype on urinary bladder cancer risk. Oncogene 2006;25:1649-58. 16. Zang Y, Doll MA, Zhao S, States JC, Hein DW. Functional characterization of single-nucleotide polymorphisms and haplotypes of human N-acetyltransferase 2. Carcinogenesis 2007;28:1665-71.
17. Hahn M, Hagedorn G, Kuhlisch E, Schackert HK, Eckelt U. Genetic polymorphisms of drug-metabolizing enzymes and susceptibility to oral cavity cancer. Oral Oncol 2002;38:486-90. 18. Katoh T, Kaneko S, Boissy R, Watson M, Ikemura K, Bell DA. A pilot study testing the association between N-acetyltransferases 1 and 2 and risk of oral squamous cell carcinoma in Japanese people. Carcinogenesis 1998;19:1803-7. 19. Chen C, Ricks S, Doody DR, Fitzgibbons ED, Porter PL, Schwartz SM. N-acetyltransferase 2 polymorphisms, cigarette smoking and alcohol consumption, and oral squamous cell cancer risk. Carcinogenesis 2001;22:1993-9. 20. Jourenkova-Mironova N, Wikman H, Bouchardy C, Mitrunen K, Dayer P, Benhamou S, et al. Role of arylamine N-acetyltransferase 1 and 2 (NAT1 and NAT2) genotypes in susceptibility to oral/pharyngeal and laryngeal cancers. Pharmacogenetics 1999; 9:533-7. 21. Malik MA, Upadhyay R, Modi DR, Zargar SA, Mittal B. Association of NAT2 gene polymorphisms with susceptibility to esophageal and gastric cancers in the Kashmir Valley. Arch Med Res 2009;40:416-23. 22. Lee KM, Park SK, Kim SU, Doll MA, Yoo KY, Ahn SH, et al. N-acetyltransferase (NAT1, NAT2) and glutathione S-transferase (GSTM1, GSTT1) polymorphisms in breast cancer. Cancer Lett 2003;196:179-86. 23. Zhao JH, Curtis D, Sham PC. Model-free analysis and permutation tests for allelic associations. Hum Hered 2000;50:133-9. 24. Sabbagh A, Darlu P. SNP selection at the NAT2 locus for an accurate prediction of the acetylation phenotype. Genet Med 2006;8:76-85. 25. Gara S, Abdennebi M, Chatti S, Touati S, Ladgham A, Guemira F. Association of NAT2 gene substitution mutation T341C with increased risk for head and neck cancer in Tunisia. Acta Oncol 2007;46:834-7. 26. Hein DW. N-acetyltransferase genetics and their role in predisposition to aromatic and heterocyclic amine-induced carcinogenesis. Toxicol Lett 2000;112–113:349-56. 27. Harth V, Schafer M, Abel J, Maintz L, Neuhaus T, Besuden M, et al. Head and neck squamous-cell cancer and its association with polymorphic enzymes of xenobiotic metabolism and repair. J Toxicol Environ Health A 2008;71:887-97. 28. Sturgis EM, Castillo EJ, Li L, Zheng R, Eicher SA, Clayman GL, et al. Polymorphisms of DNA repair gene XRCC1 in squamous cell carcinoma of the head and neck. Carcinogenesis 1999;20:2125-9. 29. Selmaoui B, Touitou Y. Age-related differences in serum melatonin and pineal NAT activity and in the response of rat pineal to a 50-Hz magnetic field. Life Sci 1999;64:2291-7. 30. Li Y, Zheng T, Kilfoy BA, Lan Q, Holford T, Han X, et al. Genetic polymorphisms in cytochrome P450s, GSTs, NATs, alcohol consumption and risk of non-Hodgkin lymphoma. Am J Hematol 2010;85:213-5.
Reprint requests: Luo-Ping Ger, MPH Department of Medical Education and Research Kaohsiung Veterans General Hospital 386 Ta-Chung 1st Road Kaohsiung, Taiwan [email protected]
Background. NAT2, the most important phase II metabolic enzyme for betel quid (BQ), might modify the risk of BQrelated oral and pharyngeal squamous cell carcinoma (OPSCC) in Taiwan. Study design. PCR-RFLP and TaqMan assay were conducted for genotyping of NAT2 in 172 OPSCC cases and 170 healthy controls who habitually chewed BQ. Results. The genotypic and allelic type of T341C and C481T in NAT2 are associated with the risk of OPSCC. There were linear trends between increased risk of OPSCC and slowness of NAT2 acetylation haplotypes (P ⫽ .017), especially for young subjects (P ⬍ .001), light BQ chewers (P ⫽ .005), light smokers (P ⫽ .023), and alcohol drinkers (P ⫽ .001). The interactions on risk of OPSCC were found for NAT2 acetylation haplotypes with status of age, BQ chewing, and alcohol drinking. Conclusions. The NAT2 acetylation haplotypes might be genetic markers for risk of BQ-related OPSCC. (Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2011;112:484-492)
Y.-Y.H and H.-L.O. contributed equally to this study. Portions of this work were supported by grant numbers NSC93-2320B-075B-005, VGHKS95-039, VGHKS96-091, VGHKS97-109, and VGHKS97-073. a Director, Division of Otology, Department of Otorhinolaryngology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan. b Department of Nursing, Yuh-Ing Junior College of Health Care and Management, Kaohsiung, Taiwan. c University graduate, Department of Biological Sciences, National Sun Yat-sen University, Kaohsiung, Taiwan. d Department of Medical Education and Research, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan. e Director, Department of Otorhinolaryncology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan. f Dietitian, Department of Nutrition, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan. g Department of Cosmetic Science, Chia-Nan University of Pharmacy and Science, Tainan, Kaohsiung, Taiwan. h Professor, Institute of Clinical Medicine, National Cheng-Kung University, Tainan, Taiwan. i Director, Division of Laryngology, Department of Otorhinolaryncology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan. j Director, Department of Dentistry, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan. k Department of Dentistry, YUAN’s General Hospital, Kaohsiung, Taiwan. l Director, Department of Dentistry, Zuoying Armed Forces General Hospital, Kaohsiung, Taiwan. m Attending doctor, Department of Otorhinolaryncology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.
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Oral cancer is currently listed as the sixth most common malignancy in the world. In Taiwan, oral cancer (including oral cavity, oropharynx, and hypopharynx) is the fourth leading cancer in men and the most common cancer in young adult males (25-44 years old).1 It is also the sixth leading cause of cancer deaths among males. Since the 1980s, the incidence rate and mortality rate of oral cancer have been increasing in Taiwan. Over the past 10 years, the increase (⬎20%) in the incidence and mortality rates of oral cancer is the greatest among all cancers in Taiwan. Squamous cell carcinoma (SCC) accounts for more than 90% of oral and pharyngeal cancers in Taiwan.2 Betel quid (BQ) chewing is a major contributory factor for oral and pharyngeal SCC in South and Southeast Asia, including India and Taiwan.2,3 It is estimated that 600 million people chew betel quid regularly throughout their lives.4 A recent survey n
Associate investigator, Genomics Research Center, Academia Sinica, Taipei, Taiwan. o Professor, Department of Medical Education and Research, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan. p Institute of Biomedical Sciences, National Sun Yat-sen University, Kaohsiung, Taiwan. Received for publication Oct 17, 2010; returned for revision Mar 17, 2011; accepted for publication Mar 24, 2011. 1079-2104/$ - see front matter © 2011 Mosby, Inc. All rights reserved. doi:10.1016/j.tripleo.2011.03.036
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in Taiwan showed that 8.5% of those 18 years or older were current BQ chewers.5 Taiwanese BQ usually consists of areca nut (AN), Piper betle inflorescence, and lime paste, with or without the leaves of Piper betle.2,5,6 AN contains areca alkaloids, which predominantly consist of arecoline, arecaidine, guvacoline, guvacine, and AN-specific nitrosamines. These AN components along with their auto-oxidation derivatives, such as reactive oxygen species, can attack normal oral mucosal epithelial cells during exposure to ANs.7 Because the AN components require metabolism to exert their carcinogenic effects, the variation of xenobiotic metabolizing activity of important enzymes, which results from genetic polymorphism, may modify cancer risk.8 For the predominant components (arecoline, arecaidine, and guvacine) of AN, N-acetyltransferase (NAT) is the most important enzyme in phase II metabolism (http://ctd.mdibl.org). NATs catalyze the N-acetylation and O-acetylation of many aromatic and heterocyclic amines activated by phase I enzymes.9 In addition, NATs also catalyze the intramolecular N, O-acetyltransferation of N-hydroxy-N-aromatic amines.10 These metabolic reactions can be detoxified or activated, depending on which pathway the NATs take.11,12 Two distinct NATs (NAT1 and NAT2) have been identified in human populations and more than 25 alleles of human NAT1 and NAT2 have been found.8,11 In contrast to the unclear relationship between NAT1 genotype and phenotype, NAT2 encodes the classical acetylation polymorphism yielding rapid, intermediate, and slow acetylator phenotypes.11 Furthermore, several single nucleotide polymorphisms (SNPs) of NAT2 along with their variant NAT2 allozymes have been recombinantly expressed in yeast and resulted in significant differences in NAT catalytic activity, levels of expressed protein, and intrinsic protein stability.8,13 In addition, many experimental studies have revealed that the catalytic activity of NAT2 increases gradually by the following sequence of various haplotypes, such as NAT2ⴱ5A, NAT2ⴱ5B, NAT2ⴱ6B, NAT2ⴱ6A, NAT2ⴱ7A, NAT2ⴱ7B, and NAT2ⴱ4.8,10,14-16 To date, there are no haplotype-phenotype interpretations that are applied in molecular epidemiologic studies to evaluate the relation of NAT2 haplotype and oral cancer. Furthermore, the previous association studies of NAT2 genotypes and oral cancer risk demonstrate controversial results.12,17-20 For a more precise evaluation of the NAT2 genetic effect on BQ-related oral and pharyngeal squamous cell carcinoma (OPSCC) risk, our study used the concept of haplotype, which defined a particular combination of alleles at different loci on NAT2, instead of only the genotype, which is the combination of variants at a locus that are present in
Hou et al. 485
an individual. It is worthwhile to evaluate the association of NAT2 acetylation haplotypes and susceptibility to BQ-related OPSCC in Taiwan. Therefore, the objectives of present study were to evaluate the association of NAT2 common polymorphisms (T341C, C481T, G590A, A803G, and G857A) in BQ chewers with the risk of OPSCC and to evaluate how these associations were influenced by demographic factors and substance use. MATERIAL AND METHODS Study subjects In this study, 342 male subjects with the betel quid– chewing habit were recruited: 170 healthy controls and 172 OPSCC case patients (including 141 oral cavity, 20 oropharynx, and 11 hypopharynx cases). The case patients with newly diagnosed, previously untreated, and pathologically confirmed primaries of OPSCC were recruited between January 2004 and October 2006 from the Department of ENT and Dentistry at Kaohsiung Veterans General Hospital (KSVGH). The 170 healthy controls were recruited from the oral health screening clinic at the Department of Otolaryngology, KSVGH, and from the oral health screening campaigns for vehicle drivers, cleaners, and hardware workers held by the Kaohsiung city government or the Department of Otolaryngology, KSVGH, between 2004 and 2006. The selection criteria for controls included no individual history of cancer or oral precancerous lesions (i.e., oral submucous fibrosis, leukoplakia, erythroplakia, and lichen planus), which was confirmed by the screening physicians. All controls were frequency-matched to case patients on age (⫾5 years), ethnicity, and years of BQ chewing. In addition, the exclusion criteria for both case and control subjects were patients with renal dysfunction, psychosis, chemotherapy, or blood transfusion in the previous 3 months. After informed consent was obtained at subject recruitment, each subject was interviewed by use of a structured questionnaire that elicited detailed information on the history of betel quid-chewing, cigarettesmoking, and alcohol-drinking habits; occupation; family disease; and general demographic data (i.e., age, sex, education, and ethnicity). At the end of the interview, each subject donated 7 to 15 mL of blood in ethylene diamine tetra-acetic acid (EDTA) tube under the protocol approved by the Institutional Review Board of KSVGH. A BQ chewer was defined as a person who had chewed at least 1 betel quid per day for at least 1 year. Pack-years of BQ consumption were calculated using the following: pack-years ⫽ (mean number of betel quids chewed per day/20 betel quids) ⫻ number of
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years of chewing. Light and heavy BQ chewers were categorized by using median values (20 pack-years) of BQ consumption in controls as the cutoff point. A smoker was defined as a person who had smoked at least 1 cigarette per day for at least 1 year. Pack-years of cigarette consumption were calculated using the following: pack-years ⫽ (mean number of cigarettes smoked per day/20 cigarettes) ⫻ number of years of smoking. Light and heavy smokers were categorized by using median value (31 pack-years) of cigarette consumption in controls as the cutoff point. A drinker was defined as a person who had consumed alcohol at least once a week for more than 1 year. Gram-years of alcohol consumption were calculated using the following: gram-years ⫽ mean grams of alcohol consumed per day ⫻ number of years consuming alcohol. Light and heavy drinkers were also categorized by using median value (1150 gram-years) of alcohol consumption in controls as the cutoff point. Blood sample and DNA extraction The whole blood was separated into plasma, buffy coat cells, and red blood cells by centrifugation within 24 hours of obtaining the blood. The separated buffy coat cells were stored at – 80°C before DNA extraction. Genomic DNA for genotyping was extracted with QIAamp DNA Midi kit through commercial protocols (Qiagen, Inc., Valencia, CA). DNA concentration was then determined using a spectrophotometer at 260 nm. Genotype analysis A total of 5 SNPs of the NAT2 gene were assayed in this study: T341C, C481T, G590A, A803G, and G857A. Approximately 60% of the samples were genotyped using the polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP) reported by Chen et al.19 and Malik et al.21 The remaining 40% of samples were detected using the previously reported TaqMan real-time PCR method.22 Quality control procedures were implemented to ensure high genotyping accuracy in our laboratory. A sample of each genotype was randomly selected for DNA sequencing to verify the allele sequence and the results were 100% concordant with the initial analysis. About 5% of samples were randomly selected for repeated assays. The Applied Biosystems 7500 real-time PCR system and TaqMan probes (Applied Biosystems, Foster City, CA, USA) were used to run in duplicate to ensure the accuracy of the genotyping from PCR-RFLP and vice versa. In addition, a senior researcher independently reviewed all of the agarose gels in PCRRFLP assays and absolute quantification curves for fluorescence data in TaqMan assays, data entering, and statistical analyses independently.
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HAPLOTYPE ANALYSIS The computational haplotyping method was used in this study. The haplotypes and their frequencies were estimated with SAS/Genetics (Version 9.1.3; SAS Institute, Cary, NC), based on the expectation–maximization algorithm.23 For the functional NAT2 allele nomenclature, we referred to the list demonstrated by an international gene nomenclature committee (http://www.louisville. edu/medschool/pharmacology/NAT.html). The simultaneous nucleotide substitution at the NAT2 allele with nucleotides 341, 481, and 803 represents the haplotype NAT2ⴱ5B; both nucleotide substitutions at 341 and 481 represent the haplotype NAT2ⴱ5A; a single nucleotide substitution at the NAT2 allele nucleotide 590 represents the haplotype NAT2ⴱ6B; a single nucleotide substitution at the NAT2 allele nucleotide 857 represents the haplotype NAT2ⴱ7A; a single substitution at nucleotide 803 represents the haplotype NAT2ⴱ12A; and the wild type with no nucleotide substitution is referred to NAT2ⴱ4. In addition, the NAT2 acetylation haplotypes were classified by the slow degree of acetylation10 in the following sequence: NAT2ⴱ7A, NAT2ⴱ6B, NAT2ⴱ5B, NAT2ⴱ5A, and NAT2ⴱ4. To clearly evaluate the relation of the NAT2 genotype to OPSCC risk, we classified all genotypes into 3 inferred acetylation phenotypes: rapid (NAT2ⴱ4/ NAT2ⴱ4), intermediate (NAT2ⴱ4/others), and slow acetylators (homozygotes other than NAT2ⴱ4) according to a previous study.24 Statistical analysis The discrepancies from the Hardy-Weinberg equilibrium of each NAT2 SNP in the control group were assessed by comparing the observed and expected genotype frequencies using the goodness-of-fit test. The mean differences of age between case and control groups were evaluated by t test. Logistic regression was performed to evaluate the association of BQ-related OPSCC with demographic, substance use, genotype, and haplotype data. The crude odds ratios (CORs) and their 95% confidence intervals (CIs) were used to estimate the distribution of demographic data and substance use between BQ-related OPSCC and BQ-chewing controls. The multivariate logistic regression was used to evaluate the adjusted odds ratios (AORs) and 95% CIs for each genotype or haplotype relative to the reference 1 by adjusting age and BQ-chewing, cigarette-smoking, and alcohol-drinking status. SPSS (version 12.0; SPSS, Chicago, IL, USA) and SAS/GENETICS (version 9.1.3; SAS Institute Inc., Cary, NC, USA) were used to perform all statistical analyses. A P value less than .05 was considered statistically significant.
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Table I. Distribution and odds ratios for BQ-associated OPSCC cases and healthy controls by demographic and substance use features Factor/Category
Cases (n ⫽ 172) n (%)
Controls (n ⫽ 170) n (%)
COR (95% CI)
Age, mean ⫾ SD, y ⬍40 40-49 50-59 ⱖ60 Ethnicity Fukienese Hakka Mainlander Aborigines Years of education ⱕ6 7-12 ⬎12 BQ chewing (pack†-years) ⱕ20 ⬎20 Smoking (pack‡ -years) Never-smoker 1-31 ⬎31 Drinking (gram-years) Never-drinker 1-1150 ⬎1150
49.6 ⫾ 8.5 20 (11.6) 62 (36.0) 72 (41.9) 18 (10.5)
49.6 ⫾ 8.4 19 (11.2) 61 (35.9) 73 (42.9) 17 (10.0)
1.00 0.97 (0.47-1.99) 0.94 (0.46-1.90) 1.01 (0.40-2.51)
138 (80.2) 13 (7.6) 18 (10.5) 3 (1.7)
133 (78.2) 13 (7.6) 21 (12.4) 3 (1.8)
1.00 0.96 (0.43-2.16) 0.83 (0.42-1.62) 0.96 (0.19-4.86)
61 (35.5) 94 (54.7) 17 (9.9)
56 (32.9) 98 (57.6) 16 (9.4)
1.00 0.88 (0.56-1.40) 0.98 (0.45-2.11)
.588 .950
80 (46.5) 92 (53.5)
85 (50.0) 85 (50.0)
1.00 1.15 (0.75-1.76)
.519
17 (9.9) 92 (53.5) 63 (36.6)
12 (7.1) 80 (47.1) 78 (45.9)
1.00 0.81 (0.37-1.80) 0.57 (0.25-1.28)
.608 .174
38 (22.1) 68 (39.5) 66 (38.4)
66 (38.8) 52 (30.6) 52 (30.6)
1.00 2.27 (1.33-3.89) 2.20 (1.29-3.78)
.003 .004
P value .960* .924 .857 .990 .928 .578 .964
P value is estimated by logistic regression. BQ, betel quid; CI, confidence interval; COR, crude odds ratios; OPSCC, oral and pharyngeal squamous cell carcinoma. *P value is estimated by t test. †Twenty betel quids per pack. ‡Twenty cigarettes per pack.
RESULTS The distribution and COR ratios for 172 OPSCC cases and 170 healthy controls with demographic and substance use features are summarized in Table I. No statistical differences were found between case and control groups in terms of mean age (49.6 ⫾ 8.5 and 49.6 ⫾ 8.4 years, respectively). There were no significant differences between cases and controls by ethnicity, years of education, pack-years of BQ chewing, and pack-years of smoking. More than 90% of BQ chewers are smokers and 70% of BQ chewers are alcohol drinkers in Taiwan. Although an effort was made to achieve a frequency match on years of BQ chewing between cases and controls, alcohol drinking, either light or heavy drinking was more prevalent in the cases than the controls. Therefore, alcohol drinking was further adjusted in later multivariate analysis. The genotype frequencies for the 5 single nucleotide substitution of NAT2 in controls did not differ from expected distributions based on Hardy-Weinberg equilibrium. The respective P values were .663, .663, .957, .604, and .989 for loci T341C, C481T, G590A, A803G, and G857A. The associations between the 5 SNPs of
NAT2 gene and BQ-related OPSCC risk were without any significant differences, except T341C and C481T (Table II). The combined genotypes of T/C⫹C/C at T341C and C/T⫹T/T at C481T were associated with a 2.39-fold increased risk of BQ-related OPSCC as compared with the wild homozygous genotypes of T/T at T341C (P ⫽ .31) and C/C at C481T (P ⫽ .31), respectively. In addition, the allelic type C of T341C and T of C481T were correlated with a 2.26-fold increased risk of OPSCC as compared with the wild allelic type T of T341C (P ⫽ .037) and C of C481T (P ⫽ .037), respectively. According to other studies, we identified the subjects as carriers of NAT2 rapid acetylator genotypes, NAT2 intermediate acetylator genotypes, and NAT2 slow acetylator genotypes. No associations were found between various acetylation genotypes and risk of BQ-related OPSCC, except in the slow acetylator genotype there was a borderline significant difference. Using the carriers with rapid acetylator as the reference group, those with slow acetylator genotype had a 1.69-fold increased risk of developing BQ-related OPSCC (P ⫽ .093). A strong linkage disequilibrium (LD) was observed between NAT2 5 SNPs; D= values were in the range of
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Table II. Association of NAT2 genotypes and acetylation genotypes with the risk of OPSCC NAT2 Genotype T341C T/T T/C C/C T allele C allele C481T C/C C/T T/T C allele T allele G590A G/G G/A A/A G allele A allele A803G A/A A/G G/G A allele G allele G857A G/G G/A A/A G allele A allele Acetylation Genotype Rapid Intermediate Slow
Crude
Cases (n ⫽ 172) n (%)
Controls (n ⫽ 170) n (%)
150 (87.2) 22 (12.8) 0 (0) 322 (93.6) 22 (6.4)
159 (93.5) 11 (6.5) 0 (0) 329 (96.8) 11 (3.2)
150 (87.2) 22 (12.8) 0 (0) 322 (93.6) 22 (6.4)
159 (93.5) 11 (6.5) 0 (0) 329 (96.8) 11 (3.2)
88 (51.2) 68 (39.5) 16 (9.3) 244 (70.9) 100 (29.1)
97 (57.1) 63 (37.1) 10 (5.9) 257 (75.6) 83 (24.4)
1.00 1.19 (0.76-1.86) 1.76 (0.76-4.09) 1.00 1.27 (0.90-1.78)
152 (88.4) 20 (11.6) 0 (0) 324 (94.2) 20 (5.8)
157 (92.4) 13 (7.6) 0 (0) 327 (96.2) 13 (3.8)
1.00
126 (73.3) 42 (24.4) 4 (2.3) 294 (85.5) 50 (14.5)
122 (71.8) 44 (25.9) 4 (2.4) 288 (84.7) 52 (15.3)
46 (26.7) 80 (46.5) 46 (26.7)
55 (32.4) 82 (48.2) 33 (19.4)
OR (95% CI)
Adjusted P value
1.00
OR* (95% CI)
P value
1.00
2.12 (0.99-4.52)
.052
2.39 (1.08-5.27)
.031
1.00 2.04 (0.98-4.28)
.058
1.00 2.26 (1.05-4.87)
.037
1.00
1.00
2.12 (0.99-4.52)
.052
2.39 (1.08-5.27)
.031
1.00 2.04 (0.98-4.28)
.058
1.00 2.26 (1.05-4.87)
.037
.447 .186 .169
1.00 1.22 (0.77-1.94) 1.64 (0.69-3.90) 1.00 1.26 (0.89-1.78)
.397 .261 .199
1.00
1.59 (0.76-3.31)
.216
1.80 (0.84-3.87)
.133
1.00 1.55 (0.76-3.17)
.228
1.00 1.74 (0.83-3.64)
.145
1.00 0.92 (0.57-1.51) 0.97 (0.24-3.96) 1.00 0.94 (0.62-1.44)
.753 .964 .780
1.00 1.17 (0.71-1.92) .545 1.67 (0.92-3.02) .092 P for linear trend ⫽ .099
1.00 0.97 (0.58-1.63) 0.77 (0.18-3.26) 1.00 0.94 (0.61-1.45)
.910 .724 .780
1.00 1.24 (0.74-2.08) .409 1.69 (0.92-3.11) .093 P for linear trend ⫽ .095
P value is estimated by logistic regression. CI, confidence interval; OPSCC, oral and pharyngeal squamous cell carcinoma; OR, odds ratios. *Adjusted for age, pack-years of betel quid chewing, and cigarette smoking as well as the gram-years of alcohol drinking by multiple logistic regression.
0.936 and 1.00, except T341C and C481T were with complete LD (D= ⫽ 1.00 and R2 ⫽ 1.00). The association between NAT2 acetylation haplotypes and BQrelated OPSCC is displayed in Table III. The individuals with NAT2ⴱ5A or NAT2ⴱ5B haplotypes, which represent the slowest acetylators, had higher risk of BQ-related OPSCC (AOR ⫽ 2.48, 95% CI ⫽ 1.145.40, P ⫽ .022) as compared with those with the wild type NAT2ⴱ4. On the contrary, the haplotypes that represent the intermediate acetylators had a minor influence on the BQ-related OPSCC occurrence compared with the NAT2ⴱ4 (AOR ⫽ 1.05 and 1.35 for NAT2ⴱ7A ⫹ NAT2 ⫻ 12 A and NAT2ⴱ6B, respectively), although no statistical significance was found.
However, a linear trend (P ⫽ .017) was shown between the risk of BQ-related OPSCC and slowness of acetylation haplotypes, which might suggest that subjects with the slower acetylation haplotype were inclined to have a greater risk of BQ-related OPSCC. To evaluate the gene–substance use interactions, age and various substance use were stratified to examine the impact of the NAT2 acetylation haplotype on the risk of BQ-related OPSCC (shown in Table IV). Risks associated with NAT2 acetylation haplotypes are significantly different by status of age group, BQ chewing, and alcohol drinking but not for status of cigarette smoking. The NAT2 slower acetylation haplotypes (NAT2ⴱ6B; NAT2ⴱ5A ⫹ 5B) were correlated with the
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Table III. NAT2 acetylation haplotypes associated with the risk of OPSCC Haplotype
Cases (n ⫽ 344) Number (%)
Controls (n ⫽ 340) Number (%)
341,481,590,803,857 (nomenclature) T C G A G (NAT2* 4) T C G A A (NAT2* 7A ⫹ 12 A) T C A A G (NAT2*6B) C T G G G (NAT2* 5B) C T G A G (NAT2* 5 A)
172 (50.0) 50 (14.5) 100 (29.1) 20 (5.8) 2 (0.6)
193 (56.8) 53 (15.6) 83 (24.4) 11 (3.2) 0 (0.0)
Crude
Adjusted
OR (95% CI)
P value
OR* (95% CI)
P value
1.00 1.06 (0.68-1.64) 1.35 (0.95-1.93) 2.24 (1.06-4.76)
.799 .097 .035
1.00 1.05 (0.67-1.65) 1.35 (0.93-1.94) 2.48 (1.14-5.40)
.837 .113 .022
P for linear trend ⫽ .019
P for linear trend ⫽ .017
P value is estimated by logistic regression. CI, confidence interval; OPSCC, oral and pharyngeal squamous cell carcinoma; OR, odds ratios. *Adjusted for age, pack-years of betel quid chewing, and cigarette smoking as well as the gram-years of alcohol drinking by multiple logistic regression.
greater risk of BQ-related OPSCC in the stratum of young subjects (Plineartrend ⬍ .001), but not in the stratum of old subjects (Plineartrend ⬍ .908). Therefore, young subjects with slower acetylation genotypes had a significantly increased risk of BQ-related OPSCC and this risk was clearly greater than that of older subjects (Pinteraction ⫽ .009). When it came to status of BQ chewing, the slower NAT2 acetylation haplotypes were correlated with the greater risk of BQ-related OPSCC in the stratum of light chewers (ⱕ20 pack-years; Plineartrend ⫽ .005). However, in the stratum of heavy chewers, no significant associations or linear trends were found between various NAT2 haplotypes and BQ-related OPSCC risk (Plineartrend ⫽ .587). In addition, a borderline significant interaction was found between NAT2 haplotype and risk of OPSCC (Pinteraction ⫽ .046). When we divided subjects into never-drinkers and alcohol drinkers, slower NAT2 acetylation haplotypes (NAT2ⴱ6B; NAT2ⴱ5A ⫹ 5B) had a significantly increased risk of OPSCC and a linear trend was also found in the stratum of drinkers (Plineartrend ⫽ .001). However, slower acetylation haplotypes were not correlated with the risk of BQ-related OPSCC in the stratum of never-drinkers. Therefore, the influence of NAT2 slower haplotypes on the risk of OPSCC appeared to be greater in alcohol drinkers than in never-drinkers (Pinteraction ⫽ .010). These results might suggest that the role of NAT2 haplotypes in BQ-related OPSCC risk would correlate with environmental exposures. DISCUSSION In this study, a linear trend was shown between the increased risk of BQ-related OPSCC and the NAT2 slow acetylation haplotype (Table III), especially for those who were young subjects, light BQ chewers, light smokers (including a few never-smokers), and alcohol drinkers (Table IV). Additionally, we found that the impact of NAT2 slower haplotypes on risk of BQ-
related OPSCC were significantly higher in young subjects than in old subjects, in light BQ chewers than in heavy BQ chewers, and in alcohol drinkers than in never-drinkers (Table IV). To our knowledge, this is the first study to use the concept of haplotype to represent different degrees of acetylation efficiency (phenotype) of NAT2 enzymes on risk of BQ-related OPSCC and gene-substance use interaction. We found a linear trend between NAT2 slow acetylation haplotypes and increased risk of BQ-related OPSCC, which was supported by in vitro studies.8,10,14-16 Therefore, the carriers with haplotypes of the slower acetylator (NAT2ⴱ5B and NAT2ⴱ5A) would have inefficient elimination of carcinogens and would be more inclined to develop BQ-related OPSCC than those with the haplotypes of the rapid acetylator (NAT2ⴱ4). However, some epidemiologic studies on the association of oral cancer and NAT2 polymorphisms were not consistent with ours. Two studies demonstrated that rapid or intermediate acetylation genotypes increased the risk of oral cancer12,19 and 2 other studies showed that acetylator status does not have any impact on risk of oral cancer.17,18 Nevertheless, several supportive studies were also reported. Gara et al.25 demonstrated that NAT2 T341C substitution, which might reduce enzyme activity, is associated with an increased risk of head and neck cancer. More recently, the results revealed by Malik et al.,21 who used the concept of haplotypes, were also in accordance with ours. These very inconsistent results might arise from the following 4 reasons. First, the number of target SNPs and sites of SNPs in previous studies of oral cancer12,17-20 were quite different from ours, except for Jourenkova-Mironova et al.’s survey.20 A recent functional characterization study of 10 SNPs and 4 common haplotypes of NAT2 showed that some coding region SNPs or haplotypes have different slow acetylator phenotypes by various molecular mechanisms in mammalian cells.16 Therefore, the sur-
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Table IV. NAT2 acetylation haplotypes and risk of BQ-related OPSCC by various age and substance use features Crude Factor category Age, y Young group (⬍50) (82 cases/80 control)
Old group (ⱖ50) (90 cases/ 90 control)
BQ chewing (pack -years) Light chewer (ⱕ20) (80 cases/85 control)
Heavy chewer (⬎20) (92 cases/85 control)
Smoking (pack-years) Never-or light smoker (ⱕ31) (109 cases/92 control)
Heavy smoker ⬎31 (63 cases/78 control)
Alcohol drinking (gram-years) Never-drinker (38 cases/66 control)
Drinker (134 cases/104 control)
Haplotype
CS/CN
NAT2* 4 NAT2*7A ⫹ 12 A NAT2* 6B NAT2*5A ⫹ 5B
72/96 24/25 57/34 11/5
NAT2* 4 NAT2*7A ⫹ 12 A NAT2* 6B NAT2*5A ⫹ 5B
100/97 26/28 43/49 11/6
NAT2* 4 NAT2*7A ⫹ 12 A NAT2* 6B NAT2*5A ⫹ 5B
73/101 29/27 50/38 8/4
NAT2* 4 NAT2*7A ⫹ 12 A NAT2* 6B NAT2*5A ⫹ 5B
99/92 21/26 50/45 14/7
NAT2* 4 NAT2*7A ⫹ 12 A NAT2* 6B NAT2*5A ⫹ 5B
106/104 35/34 65/41 12/5
NAT2* 4 NAT2*7A ⫹ 12 A NAT2* 6B NAT2*5A ⫹ 5B
66/89 15/19 35/42 10/6
NAT2* 4 NAT2*7A ⫹ 12 A NAT2* 6B NAT2*5A ⫹ 5B
44/68 11/23 17/33 4/8
NAT2* 4 NAT2*7A ⫹ 12 A NAT2* 6B NAT2*5A ⫹ 5B
128/125 39/30 83/50 18/3
OR (95% CI)
Adjusted P value
OR* (95% CI)
P value
1.00 1.28 (0.68-2.42) .448 2.24 (1.33-3.77) .003 2.93 (0.98-8.82) .055 P for linear trend ⫽ .001 1.00 0.90 (0.49-1.65) .734 0.85 (0.52-1.40) .524 1.78 (0.63-4.50) .275 P for linear trend ⫽ .957
1.00 1.33 (0.68-2.62) .403 2.41 (1.39-4.20) .002 3.56 (1.12-11.31) .031 P for linear trend ⬍.001 1.00 0.88 (0.47-1.63) .682 0.80 (0.48-1.33) .390 1.83 (0.63-5.31) .265 P for linear trend ⫽ .908
1.00 1.49 (0.81-2.72) .199 1.82 (1.08-3.06) .023 2.77 (0.80-9.54) .107 P for linear trend ⫽ .008 1.00 0.75 (0.40-1.43) .381 1.03 (0.63-1.69) .899 1.86 (0.72-4.81) .201 P for linear trend ⫽ .457
1.00 1.38 (0.72-2.62) .331 1.92 (1.11-3.32) .019 3.59 (0.97-13.35) .056 P for linear trend ⫽ .005 1.00 0.75 (0.39-1.45) .396 1.00 (0.60-1.66) .994 1.72 (0.65-4.56) .278 P for linear trend ⫽ .587
1.00 1.01 (0.59-1.74) .971 1.56 (0.97-2.50) .069 2.36 (0.80-6.92) .119 P for linear trend ⫽ .030 1.00 1.07 (0.50-2.25) .870 1.12 (0.65-1.95) .678 2.25 (0.78-6.49) .135 P for linear trend ⫽ .259
1.00 1.02 (0.58-1.79) .943 1.61 (0.98-2.63) .060 2.63 (0.85-8.09) .093 P for linear trend ⫽ .023 1.00 1.04 (0.47-2.27) .928 1.19 (0.66-2.12) .565 2.40 (0.79-7.25) .121 P for linear trend ⫽ .908
1.00 0.74 (0.33-1.67) .466 0.80 (0.40-1.60) .522 0.77 (0.22-2.72) .688 P for linear trend ⫽ .456 1.00 1.27 (0.74-2.17) .383 1.62 (1.06-2.49) .027 5.86 (1.68-20.39) .005 P for linear trend ⫽ .001
1.00 0.80 (0.35-1.85) .607 0.77 (0.37-1.57) .465 0.69 (0.19-2.55) .578 P for linear trend ⫽ .380 1.00 1.19 (0.69-2.05) .541 1.65 (1.07-2.54) .024 6.18 (1.76-21.72) .005 P for linear trend ⫽ .001
P for interaction
.009
.046
.487
.010
P value is estimated by logistic regression. BQ, betel quid; CI, confidence interval; COR, crude odds ratios; OPSCC, oral and pharyngeal squamous cell carcinoma. *All odds ratios were adjusted for age, pack-years of betel quid chewing, and cigarette smoking as well as the gram-years of alcohol drinking, where it was appropriate.
veyed target SNPs were different, resulting in different findings. Second, the disparity of carcinogen exposures might affect which reactions the NAT2 catalyzed, either detoxified N-acetylation or activated O-acetylation.12,17-20 The carcinogens from BQ chewing are different from smoking and alcohol drinking. In our study, all subjects
were exposed to carcinogens from BQ chewing, which might affect the ability of NAT2 to catalyze by use of detoxified N-acetylation but not activated O-acetylation. In a review, Hein26 pointed out that NAT2 acetylator status modifies risks for developing head and neck squamous cell carcinoma when aromatic and heterocyclic amine carcinogen exposures were added. This is in
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accordance with our findings that the role of NAT2 haplotypes in OPSCC risk might depend on slight exposures to environmental risk factors. Third, the ethnicity in our study is quite different from other studies. Various susceptibilities to OPSCC might exist between different ethnicities either from genetic effect or from various substance use exposures. Among the control subjects in various studies, the genotype frequency of rapid acetylator (NAT2ⴱ4/ NAT2ⴱ4) in our study (32.4%) were similar to those reported for Brazilians (31%),12 but smaller than those (50.0%) for Japanese,18 and greater than those (5.4%, 6.4%, and 8.3%) reported for German,17 American,19 and French,20 respectively. Although all other studies have not used the haplotype to evaluate the effect of NAT2 on oral cancer risk, we believe that the lower frequency of specific NAT2 haplotypes (such as NAT2ⴱ4) in certain populations (such as Caucasians) would decrease the statistical power for their studies. Fourth, we used the specific haplotype (NAT2 acetylation haplotype), which defined the NAT2 allele with several SNPs instead of only the genotype with a single SNP. Recently, the study from Malik et al.,21 which coincides with ours, also demonstrated the impact of NAT2 haplotypes instead of NAT2 single SNP on cancer occurrence. However, most of the studies with opposite results came from genotype analyses of single SNP or several SNPs17,18,27 without considering the role of haplotype in determining NAT2 enzyme activity. The detection of haplotype involvement possibly resulted in the different outcomes of studies because of the allele effect with various acetylation (slow or rapid)8,10,14-16 and the increased statistical power.14,22 Genetic susceptibility to head and neck cancer is evidenced by an early age at onset.28 We found that young subjects with NAT2 slower acetylation haplotypes had an increased risk of BQ-related OPSCC and this risk was significantly greater than those of older subjects (Table IV). After analysis of our questionnaire data on BQ-chewing history, we found that the types of BQ preparations used in subjects’ young age were comparable between the young (⬍50 years) and old (ⱖ50 years) subjects (data not shown). In addition, the market survey showed that there have been no changes in the types of BQ preparation used in Taiwan in the past 50 years. Therefore, these results still support that age could modify the NAT2 genetic contribution to risk of OPSCC. An in vivo study on age effect found that old rats are insensitive to magnetic fields because of their decreased pineal NAT activity.29 We suggested that NAT2 enzyme activity for older subjects might decrease to a lower level and the influence of genetic variation on risk of OPSCC would be minor. However,
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the exact cause is still unknown and needs to be evaluated in the near future. The results for association based on the interaction of NAT2 acetylation haplotypes and substance use exposure showed in the status of light BQ chewers (compared with heavy BQ chewers) and drinkers (compared with never-drinkers), but not in smoking status (Table IV). This result coincides with our hypothesis that NAT2 plays a vital role in clearance of BQ-related carcinogens. However, in the group of heavy BQ chewers, the level of BQ-related carcinogens might be too high to be effectively metabolized by NAT2. Therefore, BQ chewing could modify the contribution of NAT2 haplotypes to cancer risk by different degree of BQ exposures. The exact cause needs be evaluated in the near future. In the aspect of alcohol drinking, Li et al. demonstrated that a certain NAT1 genotype decreased risk of diffuse large B-cell lymphoma among alcohol drinkers, but not among nondrinking subjects.30 Our findings showed similar results that the genotypes of NAT2 would modify the risk of OPSCC in alcohol drinkers, but not in never-drinkers. However, this result was not shown in smoking status (Table IV), which was similar to the interaction findings of smoking and NAT2 in esophageal and gastric cancers from Malik et al.21 We found that both never (or light smokers) or heavy smokers carrying NAT2 slow acetylation haplotypes had higher risk for BQ-related OPSCC (Table IV). However, no statistical significance was found, which may be because of the small sample size of the study. Therefore, the interaction of smoking and NAT2 genotypes on risk of OPSCC is suggested to be validated in a larger cohort. Limitations to this study need to be addressed. This is a single-center investigation with a limited number of patients. Stratification analysis according to the subsites (oral cavity, oropharynx, and hypopharynx) of OPSCC was not carried out because of very small sample size in subsites, especially for the oropharynx and hypopharynx. The strength of association is fairly small; it means that NAT2 slow acetylation haplotypes contribute slightly to OPSCC risk. More studies on NAT2 and other susceptible genes, and other lifestyle-related risk factors (such as human papilloma virus) in independent and large cohorts are suggested. In conclusion, this study showed for the first time a linear trend between increased risk of BQ-related OPSCC and NAT2 slow acetylation haplotypes, especially for those in the young group, light BQ chewers, light smokers, and alcohol drinkers. Additionally, gene-substance use interactions were also found on risk of BQ-related OPSCC. More replication studies with large cohorts in other ethnic groups are needed to confirm our findings.
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Reprint requests: Luo-Ping Ger, MPH Department of Medical Education and Research Kaohsiung Veterans General Hospital 386 Ta-Chung 1st Road Kaohsiung, Taiwan [email protected]