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. Author manuscript; available in PMC: 2012 Nov 23.
Published in final edited form as: Cancer Res. 2009 Oct 13;69(20):7994–8000. doi: 10.1158/0008-5472.CAN-09-1149

Candidate Gene Association Study of Esophageal Squamous Cell Carcinoma in a High-Risk Region in Iran

MR Akbari 1,2,3, R Malekzadeh 4, R Shakeri 5, D Nasrollahzadeh 6, M Foumani 7, Y Sun 8, A Pourshams 9, M Sotoudeh 10, P Boffetta 11, SM Dawsey 12, P Ghadirian 13, SA Narod 14
PMCID: PMC3505030  NIHMSID: NIHMS416486  PMID: 19826048

Abstract

A region with a high risk for esophageal squamous cell carcinoma (ESCC) in northeast of Iran was identified more than three decades ago. Previous studies suggest that hereditary factors play a role in the high incidence of cancer in the region. Polymorphisms of several genes have been associated with susceptibility to esophageal cancer in various populations, but these have not been studied in Iran. We selected 22 functional variants (and 130 related tagSNPs) from 15 genes which previously have been suggested to be associated with an increased risk of ESCC. We genotyped a primary set of samples from 451 Turkmen (197 cases and 254 controls). Seven of 152 variants were associated with ESCC at the P = 0.05 level; these SNPs were then studied in a validation set of 1668 cases and controls (Turkmen and non-Turkmen) under dominant and recessive models. In the joint sample set, five variants, from five different genes, showed significant associations with ESCC at the P = 0.05 level. For one variant, in ADH1B, the association was strong and was present in both Turkmen and non-Turkmen. The histidine allele at codon 48 of ADH1B gene was associated with a significantly decreased risk of ESCC under a recessive model (OR = 0.41, 95%, CI = 0.19 to 0.49; P = 4×10−4). For four additional variants, an association was present in the Turkmen subgroup, but the statistical significance of these was less compelling than for ADH1B. Two variants showed deleterious effects and two were protective. The G allele of the c.870A>G variant of CCND1 gene was associated with a 1.5-fold increased risk of ESCC under the recessive model (OR = 1.50, 95% CI = 1.14 to 2.16, P = 0.02) and the A allele of the rs1625895 variant of TP53 gene was associated with a 1.5-fold increased risk of ESCC under a dominant model (OR = 1.54, 95% CI = 1.21 to 4.07, P = 0.005). The C allele of the rs886205 variant of ALDH2 was associated with a decreased risk of ESCC under a recessive model (OR = 0.58, 95% CI = 0.34 to 0.87, P = 0.02) and the A allele of the rs7087131 variant of MGMT was associated with a decreased risk of ESCC under the recessive model (OR = 0.26, 95% CI = 0.05 to 0.49, P=0.01). These results confirm that genetic predisposition to ESCC plays a role in high incidence of this cancer among Turkmens who live in northeast of Iran.

Keywords: Esophageal squamous cell carcinoma, Turkmen population, ADH1B, ALDH2, MGMT, TP53, CCND1

Introduction

Worldwide, 462,000 new cases of esophageal cancer are diagnosed annually [1]. 75% of affected individuals die within a year of diagnosis [2] and the five-year survival rate is only 10–16% [1]. The incidence of esophageal cancer varies greatly between populations and across geographic regions. Gonbad City in northeastern Iran, which is inhabited mainly by Turkmen, is a region with a very high risk of esophageal cancer, but the causative factors remain elusive. The annual incidence rate exceeds 100/100,000 [3, 4], although the rate has declined somewhat over the last three decades [5]. Esophageal cancer has two main histological types; squamous cell carcinoma and adenocarcinoma. Esophageal squamous cell carcinomas (ESCC) constitute more than 90% of esophageal cancers among Turkmen [6]. We have recently shown that, among Turkmen, there is a strong familial component to esophageal cancer [7]. The first gene found to contribute to the burden of esophageal cancer in Iran was BRCA2 [8]. Protein-truncating mutations in BRCA2 are associated with an increased risk of esophageal cancer among Turkmen. However, because only 8% of the cases in the region carry a deleterious BRCA2 mutation [8] it is likely that other genes are involved.

Turkmen are believed to descend from Turkic tribes who migrated from the Altai Mountains on the border of China and Mongolia to northern Iran [9]. A population at high risk for ESCC is also present in China [10] and the two high-risk populations might share common susceptibility genes. Based on this hypothesis, we considered that the genes which have been previously reported to be associated with ESCC in the high-risk Chinese population [2, 11] were candidate genes among Turkmen.

We studied 15 different genes in cases and controls from Turkmen and non-Turkmen in the North of Iran. These genes are involved in DNA repair, cell cycle control and the metabolism of alcohol, folate and carcinogens.

Materials and Methods

Study Subject

This report is a component of a study of upper gastrointestinal (UGI) cancers [6, 12] which is underway in Gonbad, the second largest city of Golestan province, in northeastern Iran. The project is directed by the Digestive Disease Research Center (DDRC) of the Tehran University of Medical Sciences, in collaboration with the National Cancer Institute (NCI), the International Agency for Research on Cancer (IARC) and the University of Toronto. Cases and controls were collected between August 1, 2001 and May 15, 2008. The diagnosis of ESCC was confirmed for all cases by upper gastrointestinal endoscopy and by pathologic evaluation of tumour biopsies. The study protocol was approved by the ethics committee of the Digestive Disease Research Center of Tehran University of Medical Sciences.

Cases

A total of 746 cases was enrolled in this study. There were 281 Turkmen and 211 non-Turkmen ESCC cases from Gonbad city. Non-Turkmen included Persians (n = 114), Turks (n = 59), Sistanies (n = 28), Balouches (n = 9) and Kurds (n = 10). In addition, we enrolled 245 Turkish cases from Ardabil, a city in northwestern Iran with intermediate rates of ESCC [13]. The mean age at diagnosis was 63.6 years (range 25 to 89 years); 380 cases (50.9%) were male and 366 cases (49.1%) were female. Epidemiologic data included smoking status, use of opium and alcohol and a family history of cancer. The primary set of cases consisted of the first 197 Turkmen who were available at the time of testing. The remaining 549 cases were part of the validation set.

Controls

1373 controls were recruited to the study: 244 were Turks from Ardabil city, 811 were Turkmen from Gonbad city and 318 were non-Turkmen from Gonbad city [Persians (n = 133), Turks (n = 78), Sistanies (n = 79), Balouches (n = 19) and Kurds (n = 9)]. 898 controls were patients referred to local hospitals for a reason other than cancer and 475 controls were healthy individuals enrolled from the Golestan cohort study [12]. Controls had no personal history of cancer. The mean age for controls was 55.2 years (range 24 to 90 years); 700 (51.0%) were male and 673 (49.0%) were female. Charactersistics of the 746 cases and 1373 controls are presented in Table 1.

Table 1.

Subject characteristics of the ESCC cases and controls

Variable Cases Controls P-Value
Total Number, n 746 1373 ---
Age, Mean (range) 63.6 (25–89) 55.2 (24–90) 1.3E-43
Gender, n (%) Male 380 (51) 700 (51) 1.0
Female 366 (49) 673 (49)
Ethnicity, n (%) Turkmen 281 (38) 811 (59) 2.3E-7
Turk 304 (41) 322 (23)
Persian 114 (15) 133 (10)
Sistani 28 (4) 79 (6)
Balooch 9 (1) 19 (1.4)
Kurd 10 (1) 9 (0.7)
Ever Smoker, n (%) Yes 200 (28) 339 (25) 0.14
No 516 (72) 1018 (75)
Ever Opium User, n (%) Yes 158 (29) 239 (20) 3.5E-5
No 382 (71) 943 (80)
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Candidate genes

Genes were selected based on literature review. Four genes were involved in DNA repair ERCC2 (XPD), XRCC1, hOGG1 and MGMT; four genes were involved in cell cycle control: CCND1 (Cyclin D1), CDKN2A (p16), TP53 (p53) and TMPRSS11A (ECRG1); four genes coded for carcinogen metabolizing enzymes: CYP1A1, CYP2E1, GSTP1and NAT2; two coded for alcohol metabolizing enzymes: ADH1B and ALDH2; and one coded for a folate metabolizing enzyme: MTHFR. The role of these genes and their variants in esophageal cancer has been reviewed [2, 11].

For each gene, at least one dysfunctional variant has been reported in the literature to be associated with ESCC [2, 11, and 14]. These variants include Asp312Asn (rs1799793) and Lys751Gln (rs13181) from ERCC2 gene, Arg399Gln (rs25487) from XRCC1 gene; Ser326Cys (rs1052133) from hOGG1 gene; Leu74Phe (rs12917) and Lys178Arg (rs2308327) from MGMT gene; c.870A>G (rs9344) from CCND1 gene; Ala148Thr (rs3731249) from CDKN2A gene; Arg72Pro (rs1042522) from TP53 gene; Arg293Gln (rs353163) from TMPRSS11A (ECRG1) gene; Ile462Val (rs1048943) from CYP1A1 gene; c.-1019C>T (rs2031920) from CYP2E1; Ile105Val (rs1695) from GSTP1 gene; Tyr94Tyr (rs1041983), Ile114Thr (rs1801280), Leu161Leu (rs1799929), Arg197Gln (rs1799930) and Gly286Glu (rs1799931) from NAT2 gene; Arg48His (rs1229984) from ADH1B gene; Glu487Lys (rs671) from ALDH2 gene; and Ala222Val (rs1801133) and Glu429Ala (rs1801131) from MTHFR gene.

Three slow metabolizing alleles of M1, M2 and M3 were reported for NAT2 gene. Variants Ile114Thr and Leu161Leu constitute allele M1; variants Arg197Gln and Tyr94Tyr constitute allele M2; and variant Gly286Glu is named allele M3 [15].

tagSNP Selection

Within populations, it is believed that SNPs are distributed along chromosomes in haplotype blocks and therefore genotyping of one or more SNPs which are characteristic of a haplotype block will capture the important information for the additonal SNPs in that block. These SNPS are in linkage disequilibrium with the genotyped SNPs, and are called tagSNPs. Genotype data from the Chinese population of the HapMap project [16] (Rel21/Phase II) was used for selecting tagSNPs to cover the genomic region of each gene, including 5 kbp in each 5’ and 3’end to cover the regulatory regions of the genes. Haploview software [17] was used for selecting tagSNps. Aggressive tagging with using 2- and 3-marker haplotypes, LOD score threshold of 3.0 for multi-marker testing and r2 threshold of 0.8 were employed for tagging procedure. Genomic regions of all 15 candidate genes encompass 705 kbp of the human genome. There are 635 SNPs with MAF of more than 1% in Chinese population of HapMap project. 141 tagSNP were selected to capture 594 SNPs (94% of the total common SNPs) with mean r2 of o.98 in 152 tests [Table 2].

Table 2.

The number of tagSNPs genotyped in this study along with the number of the SNPs captured with these tagSNPs for each gene

No. Gene Chromosome Size (kbp)* Number of
Genotyped
tagSNPs, n		 Number of
Captured SNPs,

n (%)† 		Mean
Max r2
1 ERCC2 19 29 8 18/20 (90) 0.97
2 ADH1B 4 10 3 4/4 (100) 1.00
3 ALDH2 12 53 10 22/23 (95) 0.95
4 CCND1 11 23 2 2/2 (100) 1.00
5 CDKN2A 9 18 6 8/8 (100) 0.99
6 CYP1A1 15 16 1 1/1 (100) 1.00
7 CYP2E1 10 22 6 27/34 (79) 0.97
8 ECRG1 4 63 9 49/54 (90) 0.99
9 GSTP1 11 13 4 12/12 (100) 0.99
10 MGMT 10 310 49 321/332 (96) 0.95
11 MTHFR 1 30 8 35/36 (97) 0.99
12 NAT2 8 20 10 40/46 (86) 0.94
13 OGG1 3 27 5 5/5 (100) 1.00
14 TP53 17 29 6 7/14 (50) 1.00
15 XRCC1 19 42 14 43/44 (97) 0.93
Total 705 141 594/635 (94) 0.98
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*

The size of the gene including 5 kbp at each end for covering gene regulatory regions

†

In this column the number of the SNPs with MAF >1% identified in the Chinese population of the HapMap project and the proportion of these SNPs which is captured by the genotyped tagSNPs in this study are shown for each gene.

SNP Genotyping

We initially tested the 152 SNPs in a primary set of 197 cases and 254 controls. From the 141 tagSNPs selected for study, 11 were among the dysfunctional variants above mentioned and 130 were additional. All SNPs were genotyped in germline DNA of all samples of the primary set of cases and controls. iPLEX chemistry on a MALDI-TOF MassARRAY system (Sequenom Inc., San Diego, CA, USA) was used for genotyping the 152 SNPs in eight reactions. The procedures were performed according to the manufacturer’s standard protocol. SNPs which showed association with ESCC in the primary set of samples (p < 0.05) were combined and a new assay design was performed to genotype them in a single reaction. ESCC-associated SNPs identified in the primary set of samples were genotyped in the germline DNA of the validation set of samples. 10% blinded quality control samples were included in each plate; the concordance rates ranged from 98% to 100%. The average genotyping call rate was 98% (ranging from 95% to 100%). Two ESCC-associated SNPs were failed for genotyping in the primary set of samples; they were genotyped using TaqMan assay on ABI 7500 fast real-time system (Applied Biosystems Co., Foster City, CA, USA).

Statistical Analysis

The permutation version of the exact test was done to test for Hardy–Weinberg Equilibrium. All the genotypes first compared in the primary sample set and then the identified ESCC-associated variants were genotyped in the validation sample set. Haplotype analysis using expectation maximization algorithm [18] was utilized for comparing SNPs tagged by multiple markers. The significance level of α = 0.05 was used for all comparisons. Genotyping results of the initially ESCC-associated variants in the primary and validation sample sets were combined to make a joint sample set. Genotypes of the cases and controls in the joint sample set were assessed in Turkmen and non-Turkmen. All case-control comparisons were adjusted for age and ethnicity, using multivariate logistic regression and the adjusted P-values and odds ratios (OR) were reported. The p-values of the comparisons in the final analysis were not adjusted for multiple testing.

In order to exclude the possibility that there were technical errors in genotyping between the typing of subjects in the primary and validation sets, we re-genotyped all the seven variants genotyped in the validation set in the primary sample set using the genotyping assay used for the validation sample set. The concordance rate of the genotyping results was 97%.

The possibility that there is spurious population stratification in the Turkmen was addressed by principle component analysis using the Eigenstrat method [19] for identifying any population stratification in the primary sample set which we had genotypes of 130 tagSNPs plus 22 dysfunctional variants for them. By plotting the Eigenvectors of the first two principle components with the largest Eigenvalues for the cases and the controls [figure 1], it appears that population stratification was not present. All analyses were done by SNP Variation Suit V.7 (Golden Helix Inc., Bozeman, MT, USA).

Figure 1.

Figure 1

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Eigenvectors of the first two principle components with the largest Eigenvalues have been plotted for the primary sample set using genotyping data of 152 SNPs.

Results

Initially, we genotyped 152 SNPs on a primary set of 197 case and 254 controls. These represent the first samples to be collected in the course of the project and were all Turkmen. These SNPs included 22 functional variants of 15 genes and 130 tagSNPs from these genes. The results of the 17 dysfunctional variants and three slow metabolizing alleles of NAT2 gene in the primary set of 451 samples are shown in table 3. The frequencies of the minor alleles of all variants except one (ALDH2 Glu487Lys) were greater than 1%. All were in Hardy-Weinberg equilibrium.

Table 3.

Genotypes of the dysfunctional variants on the 197 cases and 254 controls of the primary sample set

Variant Gene MAF* Cases, n (%) Controls, n (%) OR† P-Value†
Arg48His ADH1B 0.21
Arg/Arg 129 (66.5) 145 (57.3) 1.00 1.00
Arg/His 59 (30.5) 96 (37.9) 0.66 0.04
His/His 6 (3.0) 12 (4.8) 0.70 0.17
Dominant 0.64 0.03
Recessive 0.61 0.33
Glu487Lys ALDH2 0.01
Glu/Glu 196 (99.5) 248 (98.4) 1.00 1.00
Glu/Lys 1 (0.5) 4 (1.6) 0.28 0.22
Lys/Lys 0 (0.0) 0 (0.0) --- ---
c.870A>G CCND1 0.48
A/A 53 (27.2) 79 (31.6) 1.00 1.00
A/G 83 (42.6) 116 (46.4) 1.09 0.72
G/G 59 (30.2) 55 (22.0) 1.30 0.04
Dominant 1.27 0.26
Recessive 1.61 0.03
Ala148Thr CDKN2A 0.03
Ala/Ala 186 (95.9) 235 (94.0) 1.00 1.00
Ala/Thr 8 (4.1) 15 (6.0) 0.71 0.45
Thr/Thr 0 (0.0) 0 (0.0) --- ---
Ile462Val CYP1A1 0.13
Ile/Ile 149 (75.6) 186 (74.1) 1.00 1.00
Ile/Val 47 (23.8) 61 (24.3) 0.98 0.92
Val/Val 1 (0.6) 4 (1.6) 0.50 0.18
Dominant 0.96 0.86
c.-1019C>T CYP2E1 0.05
C/C 175 (91.1) 225 (90.4) 1.00 1.00
C/T 15 (7.8) 24 (9.6) 0.76 0.42
T/T 2 (1.1) 0 (0.0) --- ---
Dominant 0.83 0.59
Arg293Gln ECRG1 0.28
Arg/Arg 103 (52.8) 130 (52.0) 1.00 1.00
Arg/Gln 70 (35.9) 101 (40.4) 0.84 0.39
Gln/Gln 22 (11.3) 19 (7.6) 1.24 0.21
Dominant 0.96 0.84
Recessive 1.69 0.12
Asp312Asn ERCC2 0.37
Asp/Asp 77 (40.1) 104 (41.8) 1.00 1.00
Asp/Asn 87 (45.3) 115 (46.2) 1.06 0.77
Asn/Asn 28 (14.6) 30 (12.0) 1.15 0.39
Dominant 1.14 0.50
Recessive 1.27 0.40
Lys751Gln ERCC2 0.36
Lys/Lys 77 (39.1) 99 (39.3) 1.00 1.00
Lys/Gln 94 (47.7) 119 (47.2) 1.06 0.77
Gln/Gln 26 (13.2) 34 (13.5) 1.02 0.90
Dominant 1.06 0.76
Recessive 1.03 0.93
Ile105Val GSTP1 0.28
Ile/Ile 103 (52.6) 126 (50.4) 1.00 1.00
Ile/Val 77 (39.3) 105 (42.0) 0.90 0.63
Val/Val 16 (8.1) 19 (7.6) 0.99 0.96
Dominant 0.90 0.58
Recessive 1.07 0.84
Lys178Arg MGMT 0.14
Lys/Lys 163 (83.2) 213 (84.9) 1.00 1.00
Lys/Arg 30 (15.3) 35 (13.9) 1.14 0.64
Arg/Arg 3 (1.5) 3 (1.2) 1.17 0.71
Dominant 1.14 0.62
Leu84Phe MGMT 0.09
Leu/Leu 142 (72.4) 185 (74.0) 1.00 1.00
Leu/Phe 53 (27.0) 63 (25.2) 1.02 0.92
Phe/Phe 1 (0.6) 2 (0.8) 0.76 0.65
Dominant 1.03 0.90
Glu429Ala MTHFR 0.40
Glu/Glu 74 (75.6) 94 (74.1) 1.00 1.00
Glu/Ala 92 (23.8) 109 (24.3) 1.03 0.88
Ala/Ala 29 (0.6) 47 (1.6) 0.86 0.30
Dominant 0.94 0.75
Recessive 0.73 0.23
Ala222Val MTHFR 0.25
Ala/Ala 102 (52.3) 151 (60.4) 1.00 1.00
Ala/Val 79 (40.5) 84 (33.6) 1.47 0.09
Val/Val 14 (7.2) 15 (6.0) 1.16 0.46
Dominant 1.44 0.10
Recessive 1.10 0.81
Ser326Cys OGG1 0.32
Ser/Ser 91 (46.4) 124 (49.4) 1.00 1.00
Ser/Cys 76 (38.8) 102 (40.6) 1.03 0.87
Cys/Cys 29 (14.8) 25 (10.0) 1.20 0.24
Dominant 1.10 0.62
Recessive 1.36 0.30
Arg72Pro TP53 0.36
Arg/Arg 77 (39.3) 108 (43.0) 1.00 1.00
Arg/Pro 90 (45.9) 116 (46.2) 1.12 0.59
Pro/Pro 29 (14.8) 27 (10.8) 1.17 0.31
Dominant 1.18 0.39
Recessive 1.36 0.29
Arg399Gln XRCC1 0.37
Lys/Lys 78 (39.1) 94 (39.3) 1.00 1.00
Lys/Gln 89 (47.7) 120 (47.2) 0.93 0.71
Gln/Gln 26 (13.2) 35 (13.5) 1.00 0.98
Dominant 0.91 0.62
Recessive 1.02 0.94
Gly286Glu(M3) NAT2 0.08
Gly/Gly 165 (84.2) 209 (83.6) 1.00 1.00
Gly/Glu 29 (14.8) 39 (15.6) 0.86 0.57
Glu/Glu 2 (1.0) 2 (0.8) 0.90 0.84
Dominant 0.86 0.56
M1 ‡ NAT2
wt/wt 97 (51.9) 130 (53.3) 1.00 1.00
wt/m1 77 (41.2) 93 (38.1) 1.12 0.58
m1/m1 13 (6.9) 21 (8.6) 0.92 0.67
Dominant 1.07 0.72
Recessive 0.90 0.56
M2 ‡ NAT2
wt/wt 91 (46.7) 114 (45.8) 1.00 1.00
wt/m2 84 (43.1) 102 (41.0) 1.01 0.97
m2/m2 20 (10.2) 33 (13.2) 0.91 0.54
Dominant 0.96 0.83
Recessive 0.90 0.50
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*

Minor Allele Frequency

†

P-value and odds ratio (OR) are adjusted for age using multivariate logistic regression analysis.

‡

M1: combination of Ile114Thr and Leu161Leu variants; M2: combination of Arg197Gln and Tyr94Tyr variants

In addition, we selected 130 tagSNPs of 15 different genes on the primary set of samples. These SNPs had not previously been studied in ESCC. Eight tagSNPs had minor allele frequencies (MAF) of less than 0.01 and three tagSNPs were not in Hardy-Weinberg equilibrium. These 11 tagSNPs were excluded from the analysis. The remaining 119 tagSNPs were analyzed in the primary set of 197 cases and 254 controls.

Two functional variants showed associations with ESCC in the primary sample set with p-values less than 0.05 (the Arg48His variant from ADH1B gene and c.870A>G from CCND1). Five tagSNPs, from five different genes, also showed significant associations (p-values <0.05) in the primary set. These seven SNPs (two functional variants and five tagSNPs) were then genotyped in the validation set of samples. The results reported below are based on the combined data set (primary and validation set). Results were analyzed separately for Turkmen and non-Turkmen and for the two ethnic groups combined. Results were reported in the form of odds ratios for the three genotype classes, with the most common homozygous genotype being the reference class. Results were reported assuming dominant and recessive models.

The strongest association was observed with the His allele of the Arg48His variant of ADH1B gene. This allele was associated with a decreased risk of ESCC under a recessive model [table 4]. Compared to those who carried no His allele, who carried two His alleles were at 0.63 times the risk (P = 0.0002).

Table 4.

Genotyping results of the Arg48His variant of ADH1B gene on the joint sample set

Turkmens, n (%) Non-Turkmen, n (%) Total samples, n (%)
Cases (281) Controls (811) OR* P-Value* Cases (465) Controls (562) OR* P-Value* Cases (746) Controls (1373) OR* P-Value*
Arg/Arg 190 (67.6) 488 (60.2) 1.00 1.00 300 (64.5) 339 (60.3) 1.00 1.00 490 (65.7) 827 (60.2) 1.00 1.00
Arg/His 80 (28.5) 288 (35.5) 0.72 0.03 152 (32.7) 183 (32.5) 0.92 0.58 232 (31.1) 471 (34.3) 0.84 0.10
His/His 8 (2.8) 34 (4.2) 0.72 0.11 13 (2.8) 39 (6.9) 0.57 0.0006 21 (2.8) 73 (5.3) 0.63 0.0002
Dominant 0.69 0.02 0.81 0.13 0.77 0.01
Recessive 0.59 0.19 0.33 0.0007 0.41 0.0004
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*

P-value and odds ratio (OR) were adjusted for age in Turkmens and for age and ethnicity for Non-Turkmens and total samples using multivariate logistic regression analysis.

The G allele of the c.870A>G variant of CCND1 gene was associated with an increased risk of ESCC under both additive and recessive models. The association was limited to Turkmen [table 5]. Those who were homozygotes for the G allele had 1.5 fold higher chance of having ESCC in comparison to A/A and G/A carriers of this variant (P = 0.02). Three tagSNPs from ALDH2 (rs886205), MGMT (rs7087131) and TP53 (rs1625895) genes were associated with increased risks of ESCC in Turkmen only [table 6].

Table 5.

Genotyping results of the c.870A>G variant of CCND1 gene on the joint sample set

Turkmens, n (%) Non-Turkmen, n (%)
Cases (281) Controls (811) OR* P-Value* Cases (465) Controls (562) OR* P-Value*
A/A 81 (28.8) 270 (33.3) 1.00 1.00 130 (27.9) 164 (29.2) 1.00 1.00
A/G 126 (44.8) 376 (46.4) 1.05 0.77 238 (51.2) 290 (51.6) 0.90 0.51
G/G 72 (25.6) 161 (19.9) 1.23 0.03 97 (20.9) 107 (19.0) 0.99 0.91
Dominant 1.20 0.26 0.93 0.65
Recessive 1.50 0.02 1.07 0.67
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*

P-value and odds ratio (OR) were adjusted for age in Turkmens and for age and ethnicity in non-Turkmens using multivariate logistic regression analysis.

Table 6.

Genotyping results of the five initially ESCC-associated tagSNPs on the joint sample set

variant Gene Turkmens, n (%) Non-Turkmen, n (%)
Cases (281) Controls (811) OR* P-Value* Cases (465) Controls (562) OR* P-Value*
rs886205 ALDH2
T/T 141 (50.2) 355 (43.8) 1.00 1.00 247 (53.1) 316 (56.2) 1.00 1.00
T/C 113 (40.2) 344 (42.4) 0.85 0.31 176 (37.8) 205 (36.5) 1.05 0.73
C/C 24 (8.5) 106 (13.1) 0.73 0.01 40 (8.6) 41 (7.3) 1.11 0.43
Dominant 0.78 0.09 1.08 0.57
Recessive 0.58 0.02 1.20 0.46
rs7087131 MGMT
G/G 203 (72.2) 533 (65.7) 1.00 1.00 351(75.5) 424 (75.4) 1.00 1.00
G/A 72 (25.6) 246 (30.3) 0.74 0.07 99 (21.3) 124 (22.1) 0.92 0.60
A/A 3 (1.1) 27 (3.3) 0.49 0.007 13 (2.8) 13 (2.3) 1.06 0.77
Dominant 0.68 0.02 0.94 0.67
Recessive 0.26 0.01 1.12 0.78
rs1625895 TP53
G/G 167 (59.4) 546 (67.3) 1.00 1.00 263 (56.6) 310 (55.2) 1.00 1.00
G/A 97 (34.5) 231 (28.5) 1.51 0.01 173 (37.2) 209 (37.2) 0.98 0.87
A/A 15 (5.3) 30 (3.7) 1.36 0.08 27 (5.8) 43 (7.6) 0.98 0.89
Dominant 1.54 0.005 0.98 0.86
Recessive 1.65 0.15 0.95 0.86
rs6552135 ECRG1
A/A 99 (35.6) 244 (30.8) 1.00 1.00 146 (32.4) 164 (29.9) 1.00 1.00
A/G 114 (41.0) 372 (46.9) 0.70 0.08 209 (46.3) 269 (49.0) 0.92 0.57
G/G 65 (23.4) 177 (22.3) 0.96 0.68 96 (21.3) 116 (21.1) 0.98 0.84
Dominant 0.77 0.09 0.94 0.65
Recessive 1.13 0.47 1.01 0.94
rs3916874 ERCC2
G/G 194 (69.3) 514 (63.9) 1.00 1.00 328 (70.7) 377 (67.4) 1.00 1.00
G/C 75 (26.8) 262 (32.6) 0.74 0.07 124 (26.7) 162 (29.0) 0.91 0.53
C/C 11 (3.9) 28 (3.5) 1.04 0.85 12 (2.6) 20 (3.6) 0.75 0.13
Dominant 0.77 0.09 0.85 0.27
Recessive 1.17 0.69 0.55 0.12
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*

P-value and odds ratio (OR) were adjusted for age in Turkmens and for age and ethnicity in non-Turkmens using multivariate logistic regression analysis.

Discussion

We studied 22 different dysfunctional variants of 15 genes which were previously associated with ESCC. Notably, the odds ratios reported for these variants in the earlier studies were typically 2.0 and above [11]. Given that the frequencies of the minor alleles of these variants in our population were almost all above 0.1 [table 3], the power of our study to detect odds ratios above 2.0 exceeded 90%. However, of the 22 variants tested, only two polymorphisms (ADH1B Arg48His and CCND1 c.870A>G) showed significant associations at the p = 0.05 level in the entire data set and for only one of these (ADH1B Arg48His) was the association compelling.

The His allele of the Arg48His variant of ADH1B gene was associated with a decreased risk of ESCC in both Turkmen and non-Turkmen under a recessive model [table 5]. ADH1B is one of seven different genes which encode five different classes of alcohol dehydrogenase (ADH) enzymes. These enzymes are involved in the oxidation of alcohol groups of a variety of substrates. ADH class I, which is responsible for the oxidation of ethanol to acetaldehyde, is a homodimer or heterodimer of three related subunits (α, β and γ) encoded by ADH1A, ADH1B and ADH1C respectively [20]. All ADH enzymes use NAD+/NADH as their oxidizing cofactor. The arginine residue at codon 48 of ADH1B is in close contact with its cofactor and seems to have a crucial role on modulating the protein’s ethanol oxidizing capability. When the arginine is replaced by histidine at this codon, the enzyme activity increases by 70–80 fold [20]. In a rodent model, ADH1B, which has proline at this codon, is not capable of oxidizing ethanol [21].

In previous studies, the Arg allele of ADH1B has been consistently reported to increase the risk of ESCC by 1.6- to 4-fold among alcohol drinkers, compared to individuals with two His alleles [2, 11, 22]. The suggested mechanism is that alcohol and acetaldehyde persist for a longer time in the circulation of individuals with the Arg allele, who are slow metabolizers. These individuals tend to experience less flushing upon exposure to alcohol and are prone to alcoholism [23]. We confirmed that the presence of the Arg allele at codon 48 of the ADH1B protein increases the risk of ESCC by 2.4 fold in comparison to two His alleles (p = 4 × 10−4). Considering that only 4.5 % of our study subjects were alcohol drinkers, this finding suggests that the association is not due to metabolism of alcohol per se. Our observation is contrary to other studies which report no association between Arg48His variant of the ADH1B gene and the risk of ESCC among non-drinkers [22, 24, and 25]. Most studies which showed the association of Arg48His variant of ADH1B with ESCC in alcohol drinkers also report an association between the Glu487Lys variant of ALDH2 and esophageal cancer [11]. The ALDH2 protein with lysine residue at codon 487 is catalytically inactive and can not metabolize acetaldehyde, a known carcinogen [23]. We did not observe this association.

Our data suggests that, in Northern Iran, ESCC may be related to an environmental factor other than ethanol, but that is also metabolized by ADH1B. 1-methypyrene (1-MP) might be the environmental factor. 1-MP is a carcinogenic polycyclic aromatic hydrocarbon (PAH) which is metabolized to 1-hydroxymethylpyrene (1-HMP) by CYP450 enzymes. 1-HMP could be activated by sulphotransferases (SULTs) to 1- Sulfooxymethylpyrene (1-SMP), which is capable of forming DNA adducts, but most of 1-HMP is inactivated by ADH1B and excreted in urine and does not form DNA adducts [26, 27].

Polycyclic aromatic hydrocarbons (PAH) have been shown to be esophageal carcinogens in both animal and human studies. In a two-year feeding study in mice, a dose-response relationship was shown between benzo[a]pyrene intake and ESCC incidence [28]. Occupational exposure to PAHs in Sweden was reported to double the risk of ESCC [29]. Other lines of evidence which support the contribution of PAHs in the pathogenesis of ESCC come from a high-risk region in China, and include high urinary levels of PAH metabolites in residents [30], increased concentrations of PAHs in cooked and uncooked foods in the region [28] and the presence of PAH-DNA adducts in ESCC tissue samples of patients [31].

PAHs require metabolic activation to become carcinogens. Purely aromatic PAHs, such as benzo[a]pyrene, are activated via the diol-epoxide pathway [32], but alkylated PAHs, such as 1-MP, are metabolized by an alternative pathway named the aralkylating hydrocarbon pathway [33]. In this pathway [figure 2], 1-MP is hydroxylated to 1-HMP by cytochrome P450 enzymes such as CYP1A1 [34]. 1-HMP subsequently forms reactive sulphuric acid ester (1- SMP) by the action of sulphotransferases [35, 36]. 1- SMP forms DNA adducts and has been shown to be mutagenic in rats [33] and in Salmonella Typhimurium [37]. 1- SMP is also reacts with glutathione and is excreted in urine as methylpyrenyl mercapturic acid (MPMA). The urine MPMA constitutes only 2% of the administrated 1-HMP to rats; 80% of it is excreted as free 1-pyrenylcarboxylic acid (1-PCA) and its glucuronic acid or other conjugates [26]. 1-PCA is an inactivate metabolite of 1-HMP which has been oxidized by ADH1B [27]. Therefore ADH1B competes with 1-SMP for the metabolism of 1-HMP and ADH1B activity decreases the formation of DNA-adducts. If ADH1B is the major enzyme for the inactivation of 1-HMP, then other substrates of ADH1B are expected to interfere with inactivation of 1-HMP and increase the amount of its activated metabolite. This has been exhibited by concurrent administration of 1-HMP and ethanol to rats, thereby enhancing the hepatic level of PAH-DNA adducts by 15-fold [26]. The competition of ethanol with 1-HMP for oxidation by ADH1B could also explain the synergistic carcinogenic effect of alcohol and cigarette smoke (which is rich in 1-MP, the precursor of 1-HMP) [27].

Figure 2.

Figure 2

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The Suggested Metabolic Pathway for 1- Methylpyrene (1-MP). 1-MP is first metabolized to 1-Hydroxymethypyrene (1-HMP) by CYP450 enzymes. The Majority (~80%) of 1-HMP is detoxified by alcohol dehydrogenase (ADH1B) to 1-Formylpyrene (1-FP) and then to 1-Pyrenyl carboxylic acid (1-PCA) by aldehyde dehydrogenase (ALDH). 1-PCA and its glucoronic acid conjugates are excreted in urine. Some of (~20%) 1-HMP is transformed to 1-Sulfooxymethylpyrene (1-SMP) of which the majority form DNA- adducts and tiny of it (~2%) detoxified by nucleophile glutathione and form Methylpyrenyl mercapturic acid (MPMA).

It is possible that individuals with two slow-metabolizing Arg alleles at codon 48 of ADH1B gene do not inactivate 1-HMP as efficiently as those who carry one or more His alleles at this codon, thus more 1-HMP will be available for conversion to 1-SMP by sulphutransferases and consequently, more DNA-adducts will be formed [figure 2]. The high urinary levels of 1-hydroxypyrene (1-OHP) which characterize exposure to PAHs in the normal population of this region of Iran support our hypothesis. The median urine level of 1-OHP was reported to be 4.2 pmol/ml in Iran [38] – this is almost 18 times higher than the mean 1-OHP urine level in American office workers [39]. Although 1-OHP is a metabolite of pyrene, rather than of 1-MP, PAHs exist as a mixture in the environment and pyrene is always present in the mixture [40]. The urinary level of 1-OHP as a pyrene metabolite is representative of exposure to a mixture of PAHs, which could also include 1-MP.

There was no interaction between Arg48His variant and smoking or opium use observed in our study. Also the 1-OHP urine level of those who are neither smokers nor opium users was shown to be very high in this region [38]. Therefore, neither smoking nor opium is likely to be the source of 1-MP in our study subjects. However, charcoal-broiled red meat is rich in PAHs that could include 1-MP [41]. Turkmen have ready access to red meat and, in this region, eating charcoal-broiled red meat is common.

The second functional variant associated with ESCC in this study was c.870A>G of the Cyclin D1 (CCND1) gene. Among Turkmen, the G allele of this variant increased the risk of ESCC by 1.5-fold, under a recessive model. The CCND1 protein initiates cell cycle progression and promotes cell proliferation through cyclin-dependent kinases (CDK4 and CDK6) by inactivation of retinoblastoma protein (RB) [42, 43]. Tumours of several different types, including esophagus, may over-express CCND1 [44]. Alternative splicing of CCND1 at the exon 4 and intron 4 boundaries results in the formation of two different transcripts, CCND1a and CCND1b [45]. CCND1b has enhanced transforming activity, in comparison to CCND1a [46] and overexpression of CCND1b has been shown in esophageal tumor cells [47]. The c.870A>G variant, which is located at the end of exon 4, is believed to be associated with alternative splicing of CCND1 between transcripts a and b. Although some studies reported the A allele at this codon favors the formation of transcript b [45, 48], others have detected no association [49, 50]. A recent study described the preferential formation of transcript b for genes with the G allele [51]. The association of c.870A>G variant and different cancer types is also controversial [52, 53]. In some studies, the A allele is associated with an increased risk of cancer and in others the G allele is the high-risk variant [52, 53]. These discrepancies suggest that there are cis- or trans-acting modifiers which, in combination with the c.870A>G variant, determine the splicing of CCND1 [52].

In addition to these two functional variants, tagSNPs from ALDH2 (rs886205), MGMT (rs7087131) and TP53 (rs1625895) were also associated with ESCC in Turkmen. Aldehyde dehydrogenase 2 (ALDH2) catalyzes the conversion of acetaldehyde to acetate. Acetaldehyde is the major carcinogenic metabolite of ethanol. The rs886205 variant is the substitution of T by C nucleotide at the 5’ untranslated region of Exon 1 at the promoter region of ALDH2. A transfection assay showed that the expression activity of the T allele is approximately 50% less than the C allele in hepatoma cells [54], also the acetaldehyde- and ethanol-induced gene expression of genes with the T allele was lower than those with the C allele [55]. These reports are consistent with our finding that the C allele (minor allele) of this variant decreases the risk of ESCC under a recessive model (OR = 0.58, p = 0.02); possibly by increasing the expression of ALDH2, which metabolizes the aldehyde group of carcinogens like acetaldehyde.

The rs7087131 variant of MGMT was also found to be associated with ESCC. O6-methylguanine is formed by alkylating agents such as nitrosamines, which are associated with an increased risk of ESCC [56]. O6-methylguanine has been found in the epithelial cells of the esophagus of individuals living in the high-risk region of China [57]. Unrepaired O6-methylguanine tends to pair with thymine and introduces G:C to A:T transitions during cell replication [58]. O6-methylguanine-DNA methytransferase (MGMT) repairs O6-methylguanine by transferring the methyl group to a cysteine residue at codon 145 of the enzyme [59]. Binding of methyl group to the cytosine residues of MGMT is an irreversible reaction and after repairing each single O6-methylguanine, the protein is ubiquitylated and degraded and needs to be replaced by a new protein. Therefore, it is expected that cells with low expression of MGMT can not repair O6-methylguanine efficiently. Low expression of MGMT in cells can result from either promoter hypermethylation of the gene or by cis-acting nucleotide polymorphisms which change the binding sites of key regulatory factors [60]. A group of SNPs at the 5’ half of the gene were described to be in association with MGMT activity in white blood cells. [60]. The rs7087131 variant is also located at the 5’ half of the MGMT gene in the intronic region between exons 2 and 3 and directly or indirectly might affect expression level of the gene and contribute to the risk of ESCC in Turkmens. In our study, the substitution of G by A nucleotide in this variant decreased the risk of ESCC in Turkmens under a recessive model (OR = 0.26, P=0.01).

The last tagSNP identified in this study to be associated with ESCC in Turkmen was the rs1625895 variant of TP53. TP53 is a tumor suppressor protein which is activated by phosphorylation following a genotoxic event and prevents the cell from proceeding from G1 to S phase of the cell cycle [42]. This variant is the result of a nucleotide substitition (G to A) in the intronic region of TP53 gene (between exons 6 and 7). In our study, the A allele of this variant was associated with a 1.5-fold increase in the risk of ESCC under a dominant model (p = 0.005). This variant has been described in association with colorectal [61] and nasopharyngeal [62] cancer. Recently, one study reported that the rs1625895 variant is associated with a higher level of micronuclei in the blood cells of individuals exposed to vinyl chloride [63].

We observed more evidence for a genetic component for ESCC in Turkmen, compared to non-Turkmens, consistent with previous report of a high incidence of ESCC among Turkmen [64] and our previous study which showed strong familial component to esophageal cancer among Turkmen [7]. The different associations in Turkmen and non-Turkmen could be due to chance, but could also be the result of social and environmental differences in the populations. Second, these variants may be indirectly associated with ESCC, and the responsible variants might be in linkage disequilibrium with these variants in Turkmen, but not in other groups. Third, genetic modifier variants may be required to increase cancer risk and the relevant genetic modifier alleles may be prevalent in Turkmen, but not in non-Turkmen.

In conclusion, this study extends our earlier observations that genetic factors play an important role in the high incidence of esophageal cancer in the northeast of Iran. A strong and significant association was seen with the Arg48His allele of ADH1B gene. Interestingly, the variant is related to the risk of ESCC in alcohol non-drinkers, suggesting that other carcinogens might be causative. It is important that these observations be confirmed in larger series of patients, ideally with reference to exposure data and incorporating data from other biomarkers, such as DNA adducts. To date, we have strong evidence that both BRCA2 and ADH1B contribute to the burden of esophageal cancer in Northern Iran, but the known variants in these two genes are not sufficient to explain the high incidence of esophageal cancer in the region, or the high familial relative risk, and further studies are needed.

Acknowledgments

We thank Dr. Jafar Bashiri from the case-control study center (Aras Clinic) in Ardabil city, Ms. Safura Kor from the case-control study center (Atrak Clinic) and Mrs. Goharshad Goglani and Mrs. Mina Bahrami from cohort study center in Gonbad city for helping with study subject recruitment. We also appreciate the kind help of Cheryl Crozier from Analytical Genetics Technology Centre of the University of Toronto and Alexandre Belisle from the Genotyping Platform of the McGill University and Genome Quebec Innovation Centre for helping in performing the genetic tests of this study. This study was supported by grants from the Canadian Institutes of Health Research (CIHR) and the Digestive Disease Research Center (DDRC), Tehran University of Medical Sciences (TUMS).

Contributor Information

MR Akbari, Digestive Disease Research Center, Tehran University of Medical Sciences, Tehran, Iran; Women’s College Research Institute, University of Toronto, Toronto, Canada; Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Canada.

R Malekzadeh, Digestive Disease Research Center, Tehran University of Medical Sciences, Tehran, Iran.

R Shakeri, Digestive Disease Research Center, Tehran University of Medical Sciences, Tehran, Iran.

D Nasrollahzadeh, Digestive Disease Research Center, Tehran University of Medical Sciences, Tehran, Iran.

M Foumani, Women’s College Research Institute, University of Toronto, Toronto, Canada.

Y Sun, Women’s College Research Institute, University of Toronto, Toronto, Canada.

A Pourshams, Digestive Disease Research Center, Tehran University of Medical Sciences, Tehran, Iran.

M Sotoudeh, Digestive Disease Research Center, Tehran University of Medical Sciences, Tehran, Iran.

P Boffetta, International Agency for Research on Cancer, Lyon, France.

SM Dawsey, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA.

P Ghadirian, Epidemiology Research Unit Research Centre, CHUM- Hôtel-Dieu, University of Montreal, Montreal, Canada.

SA Narod, Women’s College Research Institute, University of Toronto, Toronto, Canada.

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