Skip to main content
NCBI home page
Asian Pacific Journal of Cancer Prevention : APJCP logoLink to Asian Pacific Journal of Cancer Prevention : APJCP
. 2017;18(1):263–270. doi: 10.22034/APJCP.2017.18.1.263

Genetic Association of XRCC1 Gene rs1799782, rs25487 and rs25489 Polymorphisms with Risk of Thyroid Cancer: a Systematic Review and Meta-Analysis

Jamal Jafari Nedooshan 1, Mohammad Forat Yazdi 2,*, Hossein Neamatzadeh 3, Masoud Zare Shehneh 4, Saeed Kargar 1, Niloofar Seddighi 5
PMCID: PMC5563111  PMID: 28240845

Abstract

Background:

A number of case-control studies have evaluated associations between the X-ray cross complementary group 1 protein (XRCC1) gene rs1799782 (Arg194Trp), rs25487 (Arg399Gln) and rs25489 (Arg280His) polymorphisms and thyroid cancer (TC) risk, but the results remain inconclusive.

Materials and Methods:

A systematic literature search was performed using PubMed and Google Scholar Search. According to defined criteria data were extracted and pooled odds ratios with 95% confidence intervals were calculated under five genetic models.

Results:

A total of 8 studies with 1,672 cases and 2,805 controls for the rs1799782 polymorphism, 14 studies with 2,506 cases and 5,180 controls for the rs25487 polymorphism, and 11 studies with 2,197 cases and 4,761 controls for the rs25489 polymorphism were included in this meta-analysis. Overall, there was a statistical association between XRCC1 rs1799782 polymorphism and TC risk with the homozygote genetic model (TT vs. CC: OR = 1.815, 95% CI = 1.115-2.953, p= 0.016) and the recessive genetic model (TT vs. TC+ CC: OR = 1.854, 95% CI = 1.433-2.399, p= <0.001). In the subgroup analysis by ethnicity, significantly increased TC risk was observed only in Asians under the recessive model (TT vs. TC+ CC: OR = 1.816, 95% CI = 1.398-2.358, p= <0.001). In addition, there was no positive association between XRCC1 rs25487 and rs25489 polymorphisms and risk of TC. However, there was a significant association between XRCC1 rs25487 polymorphism risk of TC among Caucasians with allele genetic comparison (A vs. G: OR= 0.882, 95% CI = 0.794-0.979, p= 0.136) and dominant genetic comparison (AA+AG vs. GG: OR=0.838, 95% CI = 0.728-0.965, p= 0.014).

Conclusions:

The results of our meta-analysis suggest an increased risk of TC with the XRCC1 rs1799782 and rs25487 polymorphisms. However, the XRCC1 rs25489 polymorphism appeared to be without influence.

Keywords: Thyroid cancer, XRCC1 gene, polymorphism, association, meta-analysis

Introduction

Thyroid cancer (TC) is the most common malignancy of the endocrine system in human, which accounts for nearly 3.8% of newly diagnosed cancers annually (Visciano et al., 2015; SEER, 2016). According to the last reports, the incidence of TC is the third fastest rising cancer diagnosis in the USA, which its incidence is rapidly increasing from 7.6 to 14.9 per 100,000 in a decade (between 2000 and 2012) (De Lellis, 2004; Morris et al., 2013).

Thyroid malignancies are categorized into several subtypes including follicular (FTC), papillary (PTC), medullary (MTC), undifferentiated, Hurthle cell and a subgroup of rare morphologies such as mucoepidermoid, oncocytic carcinomas and squamous (DeLellis, 2004; Schneider et al., 2013). In addition, TC could be categorized as either sporadic or familial, which only 5-7% of TC cases are familial (Nagy and Ringel, 2015; Haugen et al. 2016). According to the studies, a TC risk factors is very complex, simply is anything that causes to increase the susceptibility of TC. However, a combination of genetic and environmental factors (predominantly including: age, gender, ethnicity, family history, radiation exposure and iodine intake) likely contributes to the development of TC. The underlying genetics cause of TC varies based on its histology. The genetic cause of MTC is well identified. Hereditary MTC is caused by mutations in the RET proto-oncogene that cause multiple endocrine neoplasia 2A (MEN2A) syndrome characterized by MTC, parathyroid hyperplasia and pheochromocytoma, and multiple endocrine neoplasia 2A (MEN2B) syndrome characterized by MTC, pheochromocytoma, mucosal neuromas, and tall, asthenic habitus. However, the genetic causes of familial non-medullary thyroid carcinoma (FNMTC) are less understood (Morrison et al., 2009; Nagy and Ringel, 2015).

Associations between X-ray cross complementary group 1 protein (XRCC1) gene polymorphisms and multiple cancers have already been reported. Three major polymorphisms of the XRCC1 gene have been identified at codon 194 (rs1799782, C > T substitution at position 26304, exon 6, Arg to Trp), at codon 280 (rs25489, C > T substitution at position 43552260, exon 9, Arg to His), and at codon 399 (rs25487, G > A substitution at position 28152, exon 10, Arg to Gln) (Garcia et al. 2011; Santos et al. 2012; and Halkova et al. 2016). Recently, several studies have demonstrated that the polymorphism of XRCC1 gene was associated with the TC. However, these results were inconsistent. And for the relatively small sample size of the published studies, it is necessary to accumulate data from different studies to provide evidence on the association of XRCC1 gene polymorphisms with risk of TC. Moreover, in recent years more studies with large sample have been published. Therefore, we performed a meta-analysis to further estimate the overall risk of TC caused by the XRCC1 rs1799782 (Arg194Trp), rs25487 (Arg399Gln) and rs25489 (Arg280His) polymorphisms in patients.

Materials and Methods

Literature search strategy

The databases include Pubmed, Google Scholar, MEDLINE, ISI Web of Science and SCOPUS database up to January 5th, 2017 to identify all relevant articles on the subject. We have used various combinations of keywords to screen for potentially relevant studies, including “Thyroid cancer”; “DNA repair gene”, “XRCC1” or “XRCC1 DNA repair protein”; “Genetic polymorphism” or “single nucleotide polymorphism” or “polymorphism” or “SNP” or “mutation” or “variation”, with restricted to English language and only published studies with full-text articles available. All eligible studies were retrieved, then we also manually searched the references of included studies to identify more potentially relevant articles.

Including and Excluding Criteria

Studies included to the meta-analysis had to be consistent with the following criteria: (1) only studied on human; (2) only the case–control studies and cohorts, (3) studies have evaluated the XRCC1 rs1799782 (Arg194Trp), rs25487 (Arg399Gln) and rs25489 (Arg280His) polymorphisms and TC risk, and (4) sufficient published data (specially frequency of the genotypes) for estimating an odds ratio (OR) with 95% confidence interval (CI). Major reasons for exclusion of studies were as follows: (1) not on human, (2) not cancer research (3) only on patients, (4) duplicate of previous papers, and (5) have not sufficient data about frequency of genotypes.

Data extraction

Two authors carefully and independently were extracted the data from all eligible publications using a structured table. The following items were considered: first author’s name, year of publication, ethnicity, and country of study population, number of cases and controls, genotype number in cases and controls, and p- value for Hardy-Weinberg equilibrium (HWE). The subject’s ethnicities were categorized as Caucasian, Asian, or African. Disagreements were resolved in consultation with the third reviewer.

Statistical Analysis

An ethical approval was not necessary needed as this is a meta-analysis based on previous studies. The strength of association between XCCR1 gene polymorphism and TC risk was tested by odds ratios (ORs) with 95% confidence intervals (CIs) using Z test. The summarized ORs were performed for rs1799782 (allele model: T vs. C, heterozygote model: TC vs. CC, homozygote model: TT vs. CC, dominant model: TT+TC vs. CC, and recessive model: TT vs. TC+CC), rs25487 (allele model: A vs. G, heterozygote model: AG vs. GG, homozygote model: AA vs. GG, dominant model: AA+AG vs. GG, and recessive model: AA vs. AG+GG), rs25489 (allele model: A vs. G, heterozygote model: AG vs. GG, homozygote model: AA vs. GG, dominant model: AA+AG vs. GG, and recessive model: AA vs. AG+GG) polymorphisms.

The Chi-squared Q-test and I2 statistics were used to identify the heterogeneity among included publications (Zintzaras et al., 2005). The fixed-effects model (the Mantel–Haenszel method) is used when the effects are assumed to be homogenous (P ≥ 0.1 or I2 <50%). Otherwise, the random effects model (the DerSimonian and Laird method) is used when they are heterogeneous (P < 0.1 or I2 ≥ 50%). Subgroup analyses by ethnicity was also performed to identify the substantial heterogeneity. Additionally, the effect of each single study on the overall estimate was determined by application of one-way sensitivity analysis. The sensitivity analysis was performed by omitting 1 study at a time. To examine the potential publication bias in the meta analysis, Begg’s funnel plot and Egger’s test were used; P<0.05 indicated that the result was statistically significant (Song et al., 1998; Peters et al., 2006). All the statistical analyses were performed by comprehensive meta-analysis (CMA) V2.0 software (Biostat, USA). Two-sided P values < 0.05 were considered statistically significant.

Results

Characteristics of the published studies

Initially, we have identified 39 publications, among which 18 irrelevant articles were excluded. Thus, 21 publications were eligible. Among these publications, six publications were excluded because they were review articles and other polymorphisms of XRCC1 gene, and also one paper was excluded because of it subject overlapped with other included study. As seen in Tables 1-3, 14 case–control studies were selected in the final meta-analysis, including 8 case–control studies with a total of 1,672 cases and 2,805 controls concerning the XRCC1 rs1799782 polymorphism (Table 1), 14 studies with a total of 2,506 cases and 5,180 controls for XRCC1 rs25487 polymorphism (Table 2), and 11 studies with a total of 2,197 cases and 4,761 controls for XRCC1 rs25489 polymorphism (Table 3). The article performed by Akulevich et al. was separated as 2 studies for they evaluated 2 different Russian and Belarus population. The year of publication ranged between 2008 and 2016. There were 7 studies of Caucasian descendants (Sigurdsson et al., 2009; Akulevich et al., 2009; Ho et al., 2009; Garcia et al., 2011; Santos et al. 2012; and Halkova et al. 2016) and 7 studies of Asian descendants (Zhu et al., 2004; Chiang et al., 2008; Siraj et al. 2008; Esfahani et al. 2011; Ryu et al., 2011; Wang et al., 2015 and Yan et al., 2015). The populations came from different countries, including China, India, Iran, Brazil, Russia, Belarus, Korea, Spain, Portugal, Czech and Kingdom of Saudi Arabia (KSA). Genotype distributions in the controls of 3 publication (predominantly, the publication of Wang et al., 2015) were not in agreement with HWE.

Table 1.

Characteristics of Studies Included in the Meta Analysis of XRCC1 rs1799782 Polymorphism and TC

First author Country (Ethnicity) Case/Control Cases Control HWE
Genotype Allele Genotype Allele
CC CT TT C T CC CT TT C T
Chiang et al. 2008 China (Asian) 283/469 127 119 37 373 193 254 119 36 627 191 0.002
Ho et al. 2009 USA (Caucasian) 251/503 203 45 3 451 51 433 69 1 935 71 0.306
Esfahani et al. 2011 Iran (Asian) 157/187 136 18 3 290 24 166 20 1 352 22 0.641
Ryu et al. 2011 Korea (Asian) 111/100 59 43 9 161 61 37 49 14 123 77 0.728
Santos et al. 2012 Portugal (Caucasian) 109/217 98 8 2 204 12 196 21 0 413 21 0.453
Yan et al. 2015 China (Asian) 276/403 124 112 40 360 192 202 173 28 577 229 0.267
Wang et al. 2015 China (Asian) 276/552 181 52 43 414 138 411 95 46 917 187 <0.001
Halkova et al. 2016 Czech (Caucasian) 209/374 178 31 0 387 31 314 59 1 687 61 0.304
Open in a new tab

Table 2.

Characteristics of Studies Included in the Meta Analysis of XRCC1 rs25487 Polymorphism and TC

First author Country (Ethnicity) Case/Control Cases Controls HWE
Genotype Allele Genotype Allele
GG AG AA G A GG AG AA G A
Zhu et al. 2004 China (Asian) 105/105 49 44 12 208 134 57 45 3 159 51 0.092
Chiang et al. 2008 China (Asian) 283/469 150 110 23 410 156 277 165 27 719 219 0.711
Siraj et al. 2008 Saudi Arabia (Asian) 50/299 35 13 2 83 17 142 72 15 356 102 0.164
Sigurdsson et al. 2009 Russia (Caucasian) 24/892 12 10 2 34 14 460 343 89 1,263 521 0.036
Akulevich et al. 2009 Russia (Caucasian) 132/398 65 53 14 183 81 158 193 47 509 287 0.302
Akulevich et al. 2009 Belarus (Caucasian) 123/199 55 50 18 160 86 75 100 22 250 144 0.185
Ho et al. 2009 USA (Caucasian) 251/503 133 99 19 365 137 220 216 67 656 350 0.229
Esfahani et al. 2011 Iran (Asian) 155/190 78 60 17 216 94 83 87 20 253 127 0.69
Ryu et al. 2011 Korea (Asian) 111/100 87 17 7 191 31 72 19 9 163 37 0.002
Garcia et al. 2011 Spain (Caucasian) 402/479 153 186 47 492 280 196 212 66 604 344 0.476
Santos et al. 2012 Portugal (Caucasian) 109/217 46 50 13 142 76 87 105 25 279 155 0.428
Wang et al. 2015 China (Asian) 276/552 138 105 32 381 169 290 206 56 786 318 0.034
Yan et al. 2015 China (Asian) 276/403 146 108 22 400 152 176 173 54 525 281 0.271
Halkova et al. 2016 Czech (Caucasian) 209/374 97 81 31 275 143 164 160 50 488 260 0.272
Open in a new tab

Table 3.

Characteristics of Studies Included in the Meta Analysis of XRCC1 rs25489 Polymorphism and TC

First author Country (Ethnicity) Case/Control Cases Controls HWE
Genotype Allele Genotype Allele
GG GA AA G A GG GA AA G A
Chiang et al. 2008 China (Asian) 283/469 224 54 5 502 64 349 113 7 811 127 0.528
Siraj et al. 2008 Saudi Arabia (Asian) 50/299 33 12 5 78 22 129 79 21 337 121 0.088
Sigurdson et al. 2009 Russia (Caucasian) 25/896 24 1 0 49 1 800 96 0 1696 96 0.902
Akulevich et al. 2009 Russia (Caucasian) 132/398 117 15 0 249 15 366 32 0 764 32 0.403
Akulevich et al. 2009 Belarus (Caucasian) 123/195 113 10 0 236 10 176 19 0 371 19 0.474
Ho et al. 2009 USA (Caucasian) 251/503 229 22 0 480 22 453 50 0 956 50 0.24
Esfahani et al. 2011 Iran (Asian) 170/193 146 23 1 315 25 173 18 2 364 22 0.065
Garcia et al. 2011 Spain (Caucasian) 402/479 337 58 3 732 64 426 44 3 896 50 0.123
Wang et al. 2015 China (Asian) 276/552 153 91 32 397 155 322 174 56 818 286 <0.001
Yan et al. 2015 China (Asian) 276/403 218 52 6 488 64 298 97 8 693 113 0.974
Halkova et al. 2016 Czech (Caucasian) 209/374 188 19 2 395 23 338 36 0 712 36 0.328
Open in a new tab

Meta-analysis

XRCC1 rs1799782 Polymorphism

Table 4 listed the main results of the meta-analysis of XRCC1 rs1799782 (Arg194Trp) polymorphism and TC risk (Figure 1A). When all the eligible studies were pooled into the meta-analysis of XRCC1 Arg194Trp polymorphism, significantly increased risk of TC was observed in homozygote (TT vs. CC: OR = 1.815, 95% CI = 1.115-2.953, p= 0.016) and recessive (TT vs. TC+CC: OR = 1.854, 95% CI = 1.433-2.399, p= <0.001). In the subgroup analysis by ethnicity, significantly increased TC risk was observed in Asians only under recessive model (TT vs. TC+CC: OR = 1.816, 95% CI = 1.398-2.358, p= <0.001) by using fixed-effect model, but not among Caucasians.

Table 4.

Meta-Analysis of the Association of XRCC1 rs1799782 Polymorphism with TC

Genetic model Type of model Heterogeneity Odds ratio
I2 (%) PH OR 95% CI POR
Overall
T vs. C Random 77.5 <0.001 1.276 0.980-1.660 0.07
TC vs. CC Random 64.0 0.007 1.122 0.856-1.470 0.406
TT vs. CC Random 51.9 0.042 1.815 1.115-2.953 0.016
TT+TC vs. CC Random 77.9 <0.001 1.232 0.895-1.696 0.201
TT vs. TC+CC Fixed 37.8 0.128 1.854 1.433-2.399 <0.001
Ethnicity
Caucasian
T vs. C Fixed 29.1 0.244 1.202 0.919-1.572 0.179
TC vs. CC Fixed 19.1 0.29 1.092 0.782-1.527 0.605
TT vs. CC Fixed 0.0 0.389 4.031 0.828-19.620 0.084
TT+TC vs. CC Fixed 21.3 0.281 1.161 0.872-1.544 0.307
TT vs. TC+CC Fixed 0.0 0.397 3.956 0.813-19.246 0.088
Asian
T vs. C Random 84.9 <0.001 1.323 0.932-1.879 0.117
TC vs. CC Random 75.8 0.002 1.141 0.774-1.683 0.504
TT vs. CC Random 66.1 0.019 1.681 0.995-2.838 0.052
TT+TC vs. CC Random 85.2 <0.001 1.289 0.823-2.020 0.267
TT vs. TC+CC Fixed 52.9 0.075 1.816 1.398-2.358 <0.001
Open in a new tab
Figure 1.

Figure 1

Open in a new tab

Forest Plots Showed Significant Association between XRCC1 Polymorphisms and TC Risk. A: XRCC1 rs1799782 polymorphism (Allele model: T vs. C) and B: XRCC1 rs25487 polymorphism (Homozygote model: AA vs. GG)

XRCC1 rs25487 Polymorphism

The main results of XRCC1 rs25487 (Arg399Gln) polymorphism meta-analysis are listed in Table 5. Overall, there was no evidence of an association between TC risk and the XRCC1 rs25487 polymorphism in the different genetic models when all the eligible studies were pooled into the meta-analysis (A vs. G: OR= 1.131, 95% CI = 0.829-1.543, p= 0.136; AG vs. GG: OR= 0.903, 95% CI = 0.811-1.006, p= 0.063; AA vs. GG: OR= 0.892, 95% CI = 0.690-1.153, p=0.382, Figure 1B; AA+AG vs. GG: OR= 0.880, 95% CI = 0.766-1.012, p= 0.073; and AA vs. AG+GG: OR= 0.940, 95% CI = 0.797-1.109, p= 0.462). For ethnicity, the results showed XRCC1 rs25487 polymorphism was associated with increased risk of TC among Caucasians under allele genetic comparison (A vs. G: OR= 0.882, 95% CI = 0.794-0.979, p= 0.136) and dominant genetic comparison (AA+AG vs. GG: OR=0.838, 95% CI = 0.728-0.965, p= 0.014; Table 2), but not among Asians.

Table 5.

Meta-Analysis of the Association of XRCC1 rs25487 Polymorphism with TC

Genetic model Type of model Heterogeneity Odds ratio
I2 (%) PH OR 95% CI POR
Overall
A vs. G Random 93.3 <0.001 1.131 0.829-1.543 0.136
AG vs. GG Fixed 14.4 0.296 0.903 0.811-1.006 0.063
AA vs. GG Random 48.4 0.022 0.892 0.690-1.153 0.382
AA+AG vs. GG Random 42.3 0.048 0.88 0.766-1.012 0.073
AA vs. AG+GG Fixed 33.0 0.111 0.94 0.797-1.109 0.462
Ethnicity
Caucasian
A vs. G Fixed 0.0 0.429 0.882 0.794-0.979 0.018
AG vs. GG Fixed 8.6 0.363 0.861 0.742-1.001 0.051
AA vs. GG Fixed 1.4 0.414 0.835 0.663-1.051 0.124
AA+AG vs. GG Fixed 0.0 0.541 0.838 0.728-0.965 0.014
AA vs. AG+GG Fixed 6.8 0.376 0.89 0.716-1.106 0.249
Asian
A vs. G Random 96.2 <0.001 1.435 0.762-2.699 0.263
AG vs. GG Fixed 23.3 0.251 0.95 0.814-1.108 0.512
AA vs. GG Random 67.8 0.005 0.982 0.591-1.631 0.944
AA+AG vs. GG Random 62.9 0.013 0.927 0.719-1.195 0.559
AA vs. AG+GG Fixed 50.8 0.058 0.906 0.711-1.154 0.423
Open in a new tab

XRCC1 rs25489 Polymorphism

As shown in Table 6, no significant association was detected between the XRCC1 rs25489 (Arg280His) polymorphism and TC risk under all five genetic models (A vs. G: OR = 1.044, 95 % CI =.848-1.183, P = 0.507; AG vs. GG: OR = 0.984, 95 % CI = 0.948-1.141, P = 0.836; AA vs. GG: OR = 1.154, 95 % CI = 0.803-1.658, P = 0.439, AA + AG vs. GG: OR = 1.023, 95 % CI = 0.887-1.179, P = 0.758 and AA vs. AG+GG: OR = 1.206, 95 % CI = 0.846-1.719, P = 0.300). Furthermore, when stratified by ethnicity, there were no associations between XRCC1 rs25489 polymorphism and TC risk under all five genetic models in both Asians and Caucasians.

Table 6.

Meta-Analysis of the Association of XRCC1 rs25489 Polymorphism with TC.

Genetic model Type of model Heterogeneity Odds ratio
I2 (%) PH OR 95% CI POR
Overall
A vs. G Fixed 23.4 0.22 1.044 0.920-1.183 0.507
AG vs. GG Fixed 42.4 0.067 0.984 0.848-1.141 0.836
AA vs. GG Fixed 0.0 0.891 1.154 0.803-1.658 0.439
AA+AG vs. GG Fixed 32.8 0.137 1.023 0.887-1.179 0.758
AA vs. AG+GG Fixed 0.0 0.894 1.206 0.846-1.719 0.3
Ethnicity
Caucasian
A vs. G Fixed 15.1 0.317 1.205 0.955-1.520 0.116
AG vs. GG Fixed 29.5 0.214 1.172 0.916-1.500 0.206
AA vs. GG Fixed 19.9 0.264 1.939 0.468-8.026 0.361
AA+AG vs. GG Fixed 24.9 0.248 1.194 0.936-1.521 0.153
AA vs. AG+GG Fixed 24.8 0.249 1.855 0.448-7.673 0.394
Asian
A vs. G Fixed 21.5 0.278 0.983 0.847-1.142 0.825
AG vs. GG Fixed 44.5 0.125 0.89 0.738-1.073 0.222
AA vs. GG Fixed 0.0 0.974 1.113 0.765-1.619 0.575
AA+AG vs. GG Fixed 31.6 0.211 0.943 0.790-1.124 0.511
AA vs. AG+GG Fixed 0.0 0.968 1.172 0.813-1.690 0.395
Open in a new tab

Test of heterogeneity

For XRCC1 rs1799782 (Arg194Trp) polymorphism, when we have pooled the data a significant heterogeneity observed in heterozygote (I2=64.0%, PH=0.007), homozygote (I2=51.90%, PH=0.042) and dominant (I2=77.9%, PH=0.007) genetic models (Table 4). After subjects stratified by ethnicity, the heterogeneity obviously disappeared in the Caucasians (heterozygote: I2=19.13%, PH=0.290; homozygote: I2=0.0%, PH=0.389 and dominant: I2=21.3%, PH=0.281). However, heterogeneity was still present among the Asians (heterozygote: I2=75.8%, PH=0.002; homozygote: I2=66.1%, PH=0.019and dominant: I2=85.2%, PH=<0.001). Therefore, the observed heterogeneity between the included studies might be due to the ethnicities.

Sensitivity Analysis

We have performed sensitivity analysis by omitting 1 study at a time, but the estimate of overall effect did not change noticeably. In addition, when we excluded the studies not in agreement with HWE, the statistical significance of the results not changed.

Publication Bias

We have used Begg’s funnel plot and Egger’s test to assess the publication bias. However, as show in Figure 2A, 2B, the funnel plots did not reveal any obvious asymmetry in all genotypes in overall population, and the results of Begg’s test revealed no publication bias (P>0.05).

Figure 2.

Figure 2

Open in a new tab

Begg’s Funnel Plots of XRCC1 Gene Polymorphisms and TC Risk for Publication Bias Test. Each Point Represents a Separate Study for the Indicated Association. A: XRCC1 rs1799782 polymorphism (Allele model: T vs. C) and B: XRCC1 rs25487 polymorphism (Dominant model: AA+AG vs. GG)

Discussion

The XRCC1 plays an important role in the base excision repair (BER) pathway and interacts with DNA polymerase Beta (POLB), Poly ADP ribose Polymerase (PARP) and DNA ligase III (Zhang et al., 2006). The XRCC1gene (Gene ID 37414; OMIM 21171001 and 21174504), is 33 kb long and located at chromosome 19q13.3, consists of 17 exons, and encodes a 2.2 kb transcript, which produces an enzyme called X-ray cross-complementing group 1 that is involved in base excision repair pathway (Wang et al., 2015). XRCC1 polymorphisms disrupt the interaction of XRCC1 with other enzymatic proteins and consequently overwhelm DNA repair capacity, which leads to genetic instability and carcinogenesis (Forat Yazdi et al., 2014).

In the present meta-analysis, we have evaluated the association between three most common XRCC1 gene polymorphisms including rs1799782 (Arg194Trp), rs25487 (Arg399Gln) and rs25489 (Arg280His) polymorphisms and risk of TC. To the best of our knowledge, this is the most comprehensive meta-analysis of the relationship between XRCC1 polymorphisms and the risk of TC. We have found the absence of rs25487 (Arg399Gln) and rs25489 (Arg280His) polymorphisms are significantly associated with an increased risk of TC, while the rs1799782 (Arg194Trp) polymorphism significantly associated with development of TC in the overall analysis. However, there was a significant association between XRCC1 rs25487 polymorphism risk of TC among Caucasians under allele genetic comparison (A vs. G: OR= 0.882, 95% CI = 0.794-0.979, p= 0.136) and dominant genetic comparison (AA+AG vs. GG: OR=0.838, 95% CI = 0.728-0.965, p= 0.014). Moreover, the T allele of XRCC1 rs1799782 and A allele of XRCC1 rs25487 may be as a marker for increased susceptibility to TC. Similarly, in a meta-analysis Qian et al. have not an association between XRCC1 rs25487 (Arg399Gln) and rs25489 (Arg280His) polymorphisms and TC risk in the overall analysis. However, they have not found such association for third polymorphism with risk of TC, too (Qian et al., 2012). The contribution of rs1799782 (Arg194Trp) polymorphism in development of TC was identified by Zhao et al. in meta-analysis of five studies, comprising 911 patients and 1476 controls, recently. However, inconsistent with our results, Li et al., (2014) and Wu et al., (2014) in the two different meta-analysis of 8 and 10 studies not found a significant association between TC risk and the three polymorphisms of XRCC1 gene in all genetic Models. Due to the difference in genetic backgrounds and the environment in which the subjects were lived, we have performed a subgroup analysis by ethnicity, however we found a significant association between rs1799782 and rs25487 polymorphism and TC risk in Asians and Caucasians, respectively.

Interestingly, in meta-analysis Yan et al., (2015) based on previous studies quoted that the XRCC1 rs25489 polymorphism is related to different cancers in Asian populations, including gastric cancer, bladder cancer, lung cancer, and colorectal cancer. While, this meta-analysis results and three previous meta-analysis by Qian et al., (2012) Li et al., (2014) and Wu et al., (2014) there was not such association between XRCC1 rs25489 polymorphism and risk of TC. Therefore, it seems the A allele of XRCC1 rs25489 may not be as a marker for increased susceptibility to TC.

To the best of our knowledge, the current meta-analysis made a more convincing and detailed evaluation than the previous meta-analysis did. However, there are some limitations should be also recognized in this meta-analysis. First, the included studies were restricted to just English literature, which might bias the results. Second, severe TC is a multifactorial condition that results from complex interactions between genes and environmental factors such as age, gender, ethnicity, family history, radiation exposure and iodine intake. Therefore, we might fail to receive the true associations when we only considered those three XRCC1 gene polymorphisms, but neglect the role of other genetic, polymorphisms, and environmental factors in TC. Finally, the sample size of subgroup analysis by ethnicity was limited, which may causes to reduce the power of analyses. Therefore, further studies with large sample sizes are required to gain more precise results.

In summary, the results of the meta-analysis suggest an increased risk role of the XRCC1 rs1799782 and rs25487 polymorphisms in TC development. However, there was no association between the XRCC1 rs25489 polymorphisms and TC risk. More studies with a larger sample size is needed to further evaluate the association XRCC1 gene polymorphisms and TC risk.

References

  1. Akulevich NM, Saenko VA, Rogounovitch TI, et al. Polymorphisms of DNA damage response genes in radiationrelated and sporadic papillary thyroid carcinoma. Endocr Relat Cancer. 2009;16:491–503. doi: 10.1677/ERC-08-0336. [DOI] [PubMed] [Google Scholar]
  2. Chiang FY, Wu CW, Hsiao PJ, et al. Association between polymorphisms in DNA base excision repair genes XRCC1, APE1, and ADPRT and differentiated thyroid carcinoma. Clin Cancer Res. 2008;14:5919–24. doi: 10.1158/1078-0432.CCR-08-0906. [DOI] [PubMed] [Google Scholar]
  3. DeLellis RA. Pathology of genetics and tumours of endocrine organs. Lyon: IARC Press; 2004. [Google Scholar]
  4. Fard-Esfahani P, Fard-Esfahani A, Fayaz S, et al. Association of Arg194Trp, Arg280His and Arg399Gln polymorphisms in X-ray repair cross-complementing group 1 gene and risk of differentiated thyroid carcinoma in Iran. Iran Biomed J. 2011;15:73–8. [PMC free article] [PubMed] [Google Scholar]
  5. Forat-Yazdi M, Gholi-Nataj M, Neamatzadeh H, et al. Association of XRCC1 Arg399Gln polymorphism with colorectal cancer risk: A HuGE meta-analysis of 35 studies. Asian Pac J Cancer Prev. 2015;16:3285–91. doi: 10.7314/apjcp.2015.16.8.3285. [DOI] [PubMed] [Google Scholar]
  6. Garcia-Quispes WA, Perez-Machado G, Akdi A, et al. Association studies of OGG1, XRCC1, XRCC2 and XRCC3 polymorphisms with differentiated thyroid cancer. Mutat Res. 2011;67:709–10. doi: 10.1016/j.mrfmmm.2011.03.003. [DOI] [PubMed] [Google Scholar]
  7. Halkova T, Dvorakova S, Sykorova V, et al. Polymorphisms in selected DNA repair genes and cell cycle regulating genes involved in the risk of papillary thyroid carcinoma. Cancer Biomark. 2016;17:97–106. doi: 10.3233/CBM-160622. [DOI] [PubMed] [Google Scholar]
  8. Haugen BR, Alexander EK, Bible KC, et al. 2015 American thyroid association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: The American thyroid association guidelines task force on thyroid nodules and differentiated thyroid cancer. Thyroid. 2016;26:1–133. doi: 10.1089/thy.2015.0020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Ho T, Li G, Lu J, et al. Association of XRCC1 polymorphisms and risk of differentiated thyroid carcinoma: a case-control analysis. Thyroid. 2009;19:129–35. doi: 10.1089/thy.2008.0153. [DOI] [PubMed] [Google Scholar]
  10. Kanavos P. The rising burden of cancer in the developing world. Ann Oncol. 2006;17:15–23. doi: 10.1093/annonc/mdl983. [DOI] [PubMed] [Google Scholar]
  11. Li C, Xiang X, Zhou Y. No association between XRCC1 genetic polymorphisms and differentiated thyroid carcinoma risk: a meta-analysis. Mol Biol Rep. 2014;41:7613–21. doi: 10.1007/s11033-014-3651-2. [DOI] [PubMed] [Google Scholar]
  12. Morrison PJ, Atkinson AB. Genetic aspects of familial thyroid cancer. Oncologist. 2009;14:571–77. doi: 10.1634/theoncologist.2009-0046. [DOI] [PubMed] [Google Scholar]
  13. Nagy R, Ringel MD. Genetic predisposition for nonmedullary thyroid cancer. Horm Cancer. 2015;6:13–20. doi: 10.1007/s12672-014-0205-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Morris LG, Sikora AG, Tosteson TD, et al. The increasing incidence of thyroid cancer: the influence of access to care. Thyroid. 2013;23:885–91. doi: 10.1089/thy.2013.0045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Namazi A, Abedinzadeh M, Nourbaksh P, et al. Association between the XRCC3 Thr241Met polymorphism and risk of colorectal cancer: a meta-analysis of 5,193 cases and 6,645 controls. Asian Pac J Cancer Prev. 2015;16:2263–8. doi: 10.7314/apjcp.2015.16.6.2263. [DOI] [PubMed] [Google Scholar]
  16. Peters JL, Sutton AJ, Jones DR, et al. Comparison of two methods to detect publication bias in meta-analysis. JAMA. 2006;295:676–80. doi: 10.1001/jama.295.6.676. [DOI] [PubMed] [Google Scholar]
  17. Qian K, Liu K, Xu F, et al. X-Ray repair cross-complementing group 1(XRCC1) genetic polymorphisms and thyroid carcinoma risk: a meta-analysis. Asian Pac J Cancer Prev. 2012;13:6385–90. doi: 10.7314/apjcp.2012.13.12.6385. [DOI] [PubMed] [Google Scholar]
  18. Ryu RA, Tae K, Min HJ, et al. XRCC1 polymorphisms and risk of papillary thyroid carcinoma in a Korean sample. J Korean Med Sci. 2011;26:991–95. doi: 10.3346/jkms.2011.26.8.991. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Santos LS, Branco SC, Silva SN, et al. Polymorphisms in base excision repair genes and thyroid cancer risk. Oncol Rep. 2012;28:1859–68. doi: 10.3892/or.2012.1975. [DOI] [PubMed] [Google Scholar]
  20. Schneider D, Chen H. New developments in the diagnosis and treatment of thyroid cancer. CA Cancer J Clin. 2013;63:373–94. doi: 10.3322/caac.21195. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Sigurdson AJ, Land CE, Bhatti P, et al. Thyroid nodules, polymorphic variants in DNA repair and RET-related genes, and interaction with ionizing radiation exposure from nuclear tests in Kazakhstan. Radiat Res. 2009;171:77–88. doi: 10.1667/RR1327.1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Siraj AK, Al-Rasheed M, Ibrahim M, et al. RAD52 polymorphisms contribute to the development of papillary thyroid cancer susceptibility in Middle Eastern population. J Endocrinol Invest. 2008;31:893–9. doi: 10.1007/BF03346438. [DOI] [PubMed] [Google Scholar]
  23. Song F, Gilbody S. Bias in meta-analysis detected by a simple, graphical test. Increase in studies of publication bias coincided with increasing use of meta-analysis. BMJ. 1998;316:471. [PMC free article] [PubMed] [Google Scholar]
  24. Surveillance Epidemiology and End Results Program. n.d. 2016. Latest Retrieved January 4. from http://seer.cancer.gov/
  25. Visciano C, Prevete N, Liotti F, et al. Tumor-associated mast cells in thyroid cancer. Int J Endocrinol 2015. 2015:705169. doi: 10.1155/2015/705169. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Wang C, Ai Z. Association of XRCC1 polymorphisms with thyroid cancer risk. Tumor Biol. 2014;35:4791–97. doi: 10.1007/s13277-014-1629-2. [DOI] [PubMed] [Google Scholar]
  27. Wu FF, He XF, Shen HW, et al. Association between the XRCC1 polymorphisms and thyroid cancer risk: a meta-analysis from case-control studies. PLoS One. 2014;9:e87764. doi: 10.1371/journal.pone.0087764. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Yan L, Li Q, Li X, et al. Association studies between XRCC1, XRCC2, XRCC3 polymorphisms and differentiated thyroid carcinoma. Cell Physiol Biochem. 2016;38:1075–84. doi: 10.1159/000443058. [DOI] [PubMed] [Google Scholar]
  29. Yan J, Wang X, Tao H, et al. Meta-analysis of the relationship between XRCC1-Arg399Gln and Arg280His polymorphisms and the risk of prostate cancer. Sci Rep. 2015;5:9905. doi: 10.1038/srep09905. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Zhao J, Tan X, Zhao M, et al. Association between the X-ray repair cross-complementing group 1 Arg194Trp polymorphism and thyroid carcinoma susceptibility: A meta-analysis. Genet Mol Res. 2016:15. doi: 10.4238/gmr.15037793. [DOI] [PubMed] [Google Scholar]
  31. Zhu QX, Bian JC, Shen Q, et al. Genetic polymorphisms in X-ray repair cross-complementing gene 1 and susceptibility to papillary thyroid carcinoma. Zhonghua Liu Xing Bing Xue Za Zhi. 2004;25:702–5. [PubMed] [Google Scholar]
  32. Zintzaras E, Ioannidis JP. HEGESMA: genome search meta-analysis and heterogeneity testing. Bioinformatics. 2005;21:3672–73. doi: 10.1093/bioinformatics/bti536. [DOI] [PubMed] [Google Scholar]

Articles from Asian Pacific Journal of Cancer Prevention : APJCP are provided here courtesy of West Asia Organization for Cancer Prevention

RESOURCES