# Analyzing Association of theXRCC3 Gene Polymorphism with Ovarian Cancer Risk

Authors: Cunzhong Yuan; Xiaoyan Liu; Shi Yan; Cunfang Wang; Beihua Kong
Journal: BioMed Research International
 (2014)

---

## Abstract

This meta-analysis aims to examine whether theXRCC3 polymorphisms are associated with ovarian cancer risk. Eligible case-control studies were identified through search in PubMed. Pooled odds ratios (ORs) were appropriately derived from fixed effects models. We therefore performed a meta-analysis of 5,302 ovarian cancer cases and 8,075 controls from 4 published articles and 8 case-control studies for 3 SNPs of XRCC3. No statistically significant associations between XRCC3 rs861539 polymorphisms and ovarian cancer risk were observed in any genetic models. For XRCC3 rs1799794 polymorphisms, we observed a statistically significant correlation with ovarian cancer risk using the homozygote comparison (T2T2 versus T1T1: OR = 0.70, 95% CI = 0.54–0.90, P
=
0.005), heterozygote comparison (T1T2 versus T1T1: OR = 1.10, 95% CI = 1.00–1.21, P
=
0.04), and the recessive genetic model (T2T2 versus T1T1+T1T2: OR = 0.67, 95% CI = 0.52–0.87, P
=
0.002). For XRCC3 rs1799796 polymorphisms, we also observed a statistically significant correlation with ovarian cancer risk using the heterozygote comparison (T1T2 versus T1T1: OR = 0.91, 95% CI = 0.83–0.99, P
=
0.04). In conclusion, this meta-analysis shows that the XRCC3 were associated with ovarian cancer risk overall for Caucasians. Asian and African populations should be further studied.


---

## Body

## 1. Introduction

Ovarian cancer is the leading cause of the female reproductive system, with over 220,000 new cases and over 140,000 deaths worldwide in 2008 [1]. As most of the carcinomas, ovarian cancer is a multifactorial disease. Genetic factors are considered to influence the susceptibility of glioma genetic factors which all play significant roles in its susceptibility [2]. The genetic basis of ovarian carcinogenesis has been investigated in many studies.BRCA1,BRCA2,MLH1,MSH2,SMAD6,RAD51C,RAD51D,RB1,LIN28B,CASP8, andMTDH have all been implicated [3–11]. Recently, several common susceptibility alleles in four loci to be strongly associated with ovarian cancer risk have been found in three genome-wide association studies (GWAS) [12–14]. Examination of gene polymorphisms may explain individual differences in cancer risk [15].XRCC3 (X-ray repair cross-complementing group 3) belongs to a family of genes responsible for repairing DNA double strand breaks caused by normal metabolic processes or exposure to ionizing radiation [16].XRCC3 interacts and stabilizes Rad51 and involves in HRR (homologous recombinational repair) for DBSs (double strand breaks of DNA) and cross-link repair in mammalian cells [17, 18]. The SNP rs861539 lead to Thr241Met amino acid substitution, that may affect the function and/or its interaction with other proteins involved in DNA damage and repair [17, 19]. The SNP rs1799794 (4541A >G) is located in 5′UTR and the SNP rs1799796 (17893A >G) is located in intron 5 [20]. So the two SNPs do not change the proteins ofXRCC3.XRCC3 polymorphism was associated with the risks of many cancers, such as lung cancer, breast cancer, and head and neck cancer [21–24]. The association betweenXRCC3 polymorphism and ovarian cancer has been studied [20, 25–29]; however, those experimental results remain confusing. To summarize the effect of theXRCC3 polymorphism on the risk for ovarian cancer, we performed a meta-analysis.


## 2. Methods

### 2.1. Search and Selection Process

The search of the PubMed database was performed using the following keywords: “X-ray repair cross-complementing group 3,” “XRCC3,” “rs861539,” “T241M,” “rs1799794,” “a4541g,” “rs1799796,” “a17893g,” “polymorphism,” “ovarian cancer,” and their combination. Two authors (Yuan and Wang) independently checked all the references retrieved to assess their appropriateness for the inclusion in this meta-analysis. In addition, we checked all the references cited in the articles and relevant reviews. For overlapping and republished studies, only the study with the largest samples was included. If an article reported results including different studies, each study was treated as a separate comparison in our meta-analysis.Included studies met 3 criteria:(1)
evaluating the association betweenXRCC3 polymorphisms and ovarian cancer risk;
(2)
using sufficient published data to enable estimation of an odds ratio (OR) with its 95% confidence interval (CI);
(3)
using respective or prospective cohort case-control studies.


### 2.2. Data Extraction

Two authors (Yuan and Wang) independently extracted data from selected articles according to the inclusion criteria and reached a consensus on all items.The following information was extracted from each study if available: the first author, year of publication, countries, area of the cases, the ethnicity of the population, the cases source, the sample type of cases, the numbers of cases and controls, and the genotype distributions ofXRCC3 in both cases and controls.


### 2.3. Quality Score Assessment

Two authors independently evaluated the quality of the 8 studies according to the scale for quality assessment (Table1), which has been described previously [30, 31]. Quality score assessment was performed according to “source of cases,” “source of controls,” “specimens of cases for determining genotypes,” “Hardy-Weinberg equilibrium in controls,” and “total sample size.” Total scores ranged from 0 (worst) to 15 (best). Studies scoring ≥10 were defined as “high quality,” and those <10 were defined as “low quality.”Table 1
Scale for quality assessment.
Criteria
Score
Source of cases
Population or cancer registry
3
Mixed (hospital and cancer registry)
2
Hospital
1
Other
0
Source of controls
Population based
3
Volunteers or Blood bank
2
Hospital based (cancer-free patients)
1
Not described
0
Specimens of cases for determining genotypes
Blood or normal tissues
3
Mixed (blood and archival paraffin blocks)
1
Tumor tissues or exfoliated cells of tissue
0
Hardy-Weinberg equilibrium in controls
Hardy-Weinberg equilibrium
3
Hardy-Weinberg disequilibrium
0
Total sample size
≥1000
3
≥500 and <1000
2
≥200 and <500
1
<200
0


### 2.4. Statistical Analysis

Pooled ORs with 95% CIs were calculated to access the strength of association betweenXRCC3 polymorphism and ovarian cancer susceptibility, according to the genotype frequencies of cases and controls groups [32]. P
<
0.05 was considered statistically significant; all tests and CIs were two sided. If the heterogeneity was significant, the pooled ORs were initially measured by the random effects model. Else, the fixed-effects model was chosen [33].TheXRCC3 polymorphism and ovarian cancer risk were performed for a homozygote comparison (T2T2 versus T1T1), heterozygote comparison (T1T2 versus T1T1), dominant genetic model (T1T2+T2T2 versus T1T1), and the recessive genetic model (T2T2 versus T1T1+T1T2). In addition, sensitivity analysis was performed by omitting each study. Publication bias was estimated using a funnel plot. The degree of asymmetry was examined by t Egger’s test (P
<
0.05 was considered significant publication bias) [34]. The analysis was carried out using Review Manager statistical software (RevMan version 5.0.17.0; The Nordic Cochrane Center, Rigshospitalet, Copenhagen, Denmark) and STATA software (version 11.2, Stata Corporation, College Station, TX, USA). Hardy-Weinberg equilibrium (HWE) was calculated using a web-based statistical tool (http://ihg.gsf.de/cgi-bin/hw/hwa1.pl).


## 2.1. Search and Selection Process

The search of the PubMed database was performed using the following keywords: “X-ray repair cross-complementing group 3,” “XRCC3,” “rs861539,” “T241M,” “rs1799794,” “a4541g,” “rs1799796,” “a17893g,” “polymorphism,” “ovarian cancer,” and their combination. Two authors (Yuan and Wang) independently checked all the references retrieved to assess their appropriateness for the inclusion in this meta-analysis. In addition, we checked all the references cited in the articles and relevant reviews. For overlapping and republished studies, only the study with the largest samples was included. If an article reported results including different studies, each study was treated as a separate comparison in our meta-analysis.Included studies met 3 criteria:(1)
evaluating the association betweenXRCC3 polymorphisms and ovarian cancer risk;
(2)
using sufficient published data to enable estimation of an odds ratio (OR) with its 95% confidence interval (CI);
(3)
using respective or prospective cohort case-control studies.


## 2.2. Data Extraction

Two authors (Yuan and Wang) independently extracted data from selected articles according to the inclusion criteria and reached a consensus on all items.The following information was extracted from each study if available: the first author, year of publication, countries, area of the cases, the ethnicity of the population, the cases source, the sample type of cases, the numbers of cases and controls, and the genotype distributions ofXRCC3 in both cases and controls.


## 2.3. Quality Score Assessment

Two authors independently evaluated the quality of the 8 studies according to the scale for quality assessment (Table1), which has been described previously [30, 31]. Quality score assessment was performed according to “source of cases,” “source of controls,” “specimens of cases for determining genotypes,” “Hardy-Weinberg equilibrium in controls,” and “total sample size.” Total scores ranged from 0 (worst) to 15 (best). Studies scoring ≥10 were defined as “high quality,” and those <10 were defined as “low quality.”Table 1
Scale for quality assessment.
Criteria
Score
Source of cases
Population or cancer registry
3
Mixed (hospital and cancer registry)
2
Hospital
1
Other
0
Source of controls
Population based
3
Volunteers or Blood bank
2
Hospital based (cancer-free patients)
1
Not described
0
Specimens of cases for determining genotypes
Blood or normal tissues
3
Mixed (blood and archival paraffin blocks)
1
Tumor tissues or exfoliated cells of tissue
0
Hardy-Weinberg equilibrium in controls
Hardy-Weinberg equilibrium
3
Hardy-Weinberg disequilibrium
0
Total sample size
≥1000
3
≥500 and <1000
2
≥200 and <500
1
<200
0


## 2.4. Statistical Analysis

Pooled ORs with 95% CIs were calculated to access the strength of association betweenXRCC3 polymorphism and ovarian cancer susceptibility, according to the genotype frequencies of cases and controls groups [32]. P
<
0.05 was considered statistically significant; all tests and CIs were two sided. If the heterogeneity was significant, the pooled ORs were initially measured by the random effects model. Else, the fixed-effects model was chosen [33].TheXRCC3 polymorphism and ovarian cancer risk were performed for a homozygote comparison (T2T2 versus T1T1), heterozygote comparison (T1T2 versus T1T1), dominant genetic model (T1T2+T2T2 versus T1T1), and the recessive genetic model (T2T2 versus T1T1+T1T2). In addition, sensitivity analysis was performed by omitting each study. Publication bias was estimated using a funnel plot. The degree of asymmetry was examined by t Egger’s test (P
<
0.05 was considered significant publication bias) [34]. The analysis was carried out using Review Manager statistical software (RevMan version 5.0.17.0; The Nordic Cochrane Center, Rigshospitalet, Copenhagen, Denmark) and STATA software (version 11.2, Stata Corporation, College Station, TX, USA). Hardy-Weinberg equilibrium (HWE) was calculated using a web-based statistical tool (http://ihg.gsf.de/cgi-bin/hw/hwa1.pl).


## 3. Results

### 3.1. Study Characteristics

Through the literature search, 13 articles were found. Eight articles [35–42] were excluded as irrelevant study. One study [26] was excluded because it was carried out on overlapping populations with another, more samples eligible study [27]. Total 4 articles including 8 studies were selected on 5,302 ovarian cancer cases and 8,075 controls for 3 SNPs [20, 25–27] (Figure 1). These studies were all published in English. The main characteristics of the 4 studies are shown in Table 2. All subjects in these studies were Caucasians. The sample sizes (cases and controls) ranged from 1,478 to 5,906. Quality scores for all studies were high quality (≥10). Distribution of rs861539 polymorphisms genotype frequencies among ovarian cancer cases and controls of the 2 studies is shown in Table 3. Distribution of rs1799794 polymorphisms genotype frequencies is shown in Table 4 and distribution of rs1799796 polymorphisms genotype frequencies is shown in Table 5.Table 2
Main characteristics of the studies included in the meta-analysis.
First author
Year
Country
Area of the cases
Ethnicity
Cases source
Controls source
Sample type of cases
Total cases/controls
Quality score
Auranen [20]
2005
Mixed (UK-USA)
Royal Marsden Hospital in London and 6 counties in Northern California
Caucasian
Mixed (hospital and cancer registry)
Population
Blood
1665/4241
14
Beesley [26]
2007
Australia
New South Wales and Victorian Cancer Registries
Caucasian
Cancer registry
Population
Blood
731/747
15
Quaye [25]
2009
Mixed (DK-UK-USA)
MALOVA from Denmark-SEARCH from the UK-and GEOCS from the USA.
Caucasian
Mixed (hospital and cancer registry)
Population
Blood
1461/2299
14
Webb [27]
2005
Australia
New South Wales-Victoria and Queensland
Caucasian
Mixed (hospital and cancer registry)
Volunteers
Mixed (blood and archival paraffin blocks)
1445/788
12Table 3
Distribution ofXRCC3 rs861539 genotype among ovarian cancer cases and controls included in the meta-analysis.
Firstauthor
Year
Genotypes distribution(Case source)
Genotypes distribution(Controls source)
P-HWE(Controls)
T2T2 versus T1T1
T1T2 versus T1T1
T1T2+T2T2 versus T1T1
T2T2 versus T1T1+T1T2
T1T1
T1T2
T2T2
T1T1
T1T2
T2T2
OR (95% CI)
P
OR (95% CI)
P
OR (95% CI)
P
OR (95% CI)
P
Auranen [20]
2005
676
762
227
1712
1946
583
Yes
0.99 [0.83, 1.18]
0.88
0.99 [0.88, 1.12]
0.89
0.99 [0.88, 1.11]
0.87
0.99 [0.84, 1.17]
0.91
Beesley [26]
2007
291
339
101
288
351
108
Yes
0.93 [0.67, 1.27]
0.63
0.96 [0.77, 1.19]
0.69
0.95 [0.77, 1.17]
0.62
0.95 [0.71, 1.27]
0.72
Quaye [25]
2009
545
612
175
784
958
282
Yes
0.89 [0.72, 1.11]
0.31
0.92 [0.79, 1.07]
0.27
0.91 [0.79, 1.05]
0.21
0.93 [0.76, 1.14]
0.51
Webb [27]
2005
591
656
198
307
375
106
Yes
0.97 [0.74, 1.28]
0.83
0.91 [0.75, 1.10]
0.32
0.92 [0.77, 1.10]
0.37
1.02 [0.79, 1.32]
0.87
Total
2103
2369
701
3091
3630
1079
0.95 [0.85, 1.06]
0.37
0.95 [0.88, 1.03]
0.22
0.95 [0.88, 1.02]
0.19
0.97 [0.88, 1.08]
0.63
Test  for
heterogeneity
P
=
0.91
Test  for
heterogeneity
P
=
0.88
Test  for
heterogeneity
P
=
0.82
Test  for
heterogeneity
P
=
0.77Table 4
Distribution ofXRCC3 rs1799794 genotype among ovarian cancer cases and controls included in the meta-analysis.
First author
Year
Genotypes distribution(Case source)
Genotypes distribution(Controls source)
P-HWE(Controls)
T2T2 versus T1T1
T1T2 versus T1T1
T1T2+T2T2 versus T1T1
T2T2 versus T1T1+T1T2
T1T1
T1T2
T2T2
T1T1
T1T2
T2T2
OR (95% CI)
P
OR (95% CI)
P
OR (95% CI)
P
OR (95% CI)
P
Auranen [20]
2005
1060
550
48
2551
1188
161
Yes
0.72 [0.52, 1.00]
0.048
1.11 [0.98, 1.26]
0.087
1.07 [0.95, 1.20]
0.29
0.69 [0.50, 0.96]
0.027
Quaye [25]
2009
940
484
37
1505
713
89
Yes
0.67 [0.45, 0.99]
0.04
1.09 [0.94, 1.25]
0.25
1.04 [0.91, 1.19]
0.57
0.65 [0.44, 0.96]
0.027
Total
2000
1034
85
4056
1901
250
0.70 [0.54, 0.90]
0.005
1.10 [1.00, 1.21]
0.04
1.06 [0.96, 1.15]
0.24
0.67 [0.52, 0.87]
0.002
Test  for
heterogeneity
P
=
0.77
Test  for
heterogeneity
P
=
0.83
Test  for
heterogeneity
P
=
0.78
Test  for
heterogeneity
P
=
0.80Table 5
Distribution ofXRCC3 rs1799796 genotype among ovarian cancer cases and controls included in the meta-analysis.
First author
Year
Genotypes distribution(Case source)
Genotypes distribution(Controls source)
P-HWE(Controls)
T2T2 versus T1T1
T1T2 versus T1T1
T1T2+T2T2 versus T1T1
T2T2 versus T1T1+T1T2
T1T1
T1T2
T2T2
T1T1
T1T2
T2T2
OR (95% CI)
P
OR (95% CI)
P
OR (95% CI)
P
OR (95% CI)
P
Auranen [20]
2005
769
692
203
1757
1776
433
Yes
1.07 [0.89, 1.29]
0.47
0.89 [0.79, 1.01]
0.062
0.93 [0.83, 1.04]
0.188
1.13 [0.95, 1.35]
0.17
Quaye [25]
2009
676
608
177
1040
1006
253
Yes
1.08 [0.87, 1.33]
0.5
0.93 [0.81, 1.07]
0.31
0.96 [0.84, 1.09]
0.536
1.11 [0.91, 1.37]
0.30
Total
1445
1300
380
2797
2782
686
1.07 [0.93, 1.24]
0.33
0.91 [0.83, 0.99]
0.04
0.94 [0.86, 1.03]
0.16
1.13 [0.98, 1.29]
0.08
Test  for
heterogeneity
P
=
0.97
Test  for
heterogeneity
P
=
0.65
Test  for
heterogeneity
P
=
0.69
Test  for
heterogeneity
P
=
0.90Figure 1
Study flow chart explaining the selection of the four articles included in the meta-analysis.Hardy-Weinberg disequilibrium of genotype frequencies among the controls was calculated in three studies.


### 3.2. Association of Individual Polymorphisms with Ovarian Cancer

The heterogeneity analysis has been carried out. As it was shown in Tables3, 4, and 5, the heterogeneities of 3 SNPs are all not significant. So the fixed-effects model was chosen for 3 SNPs.The meta-analysis results ofXRCC3 rs861539 polymorphisms are shown in Table 3. No statistically significant associations betweenXRCC3 rs861539 polymorphisms and ovarian cancer risk were observed in any genetic models (T2T2 versus T1T1: OR = 0.95, 95% CI = 0.85–1.06, P
=
0.37; T1T2 versus T1T1: OR = 0.95, 95% CI = 0.88–1.03, P
=
0.22; T1T2+T2T2 versus T1T1: OR = 0.95, 95% CI = 0.88–1.02, P
=
0.19; T2T2 versus T1T1+T1T2: OR = 0.97, 95% CI = 0.88–1.08, P
=
0.63).ForXRCC3 rs1799794 polymorphisms, two studies [16, 18, 20, 21, 23, 24] (3,119 cases and 6,207 controls) were eligible. The meta-analysis results of rs1799794 polymorphisms are shown in Table 4. We observed a statistically significant correlation with ovarian cancer risk using the homozygote comparison (T2T2 versus T1T1: OR = 0.70, 95% CI = 0.54–0.90, P
=
0.00
5), heterozygote comparison (T1T2 versus T1T1: OR = 1.10, 95% CI = 1.00–1.21, P
=
0.04), and the recessive genetic model (T2T2 versus T1T1+T1T2 : OR = 0.67, 95% CI = 0.52–0.87, P
=
0.002). However, no statistically significant associations were observed in dominant genetic model (T1T2+T2T2 versus T1T1: OR = 1.06, 95% CI = 0.96–1.15, P
=
0.24).ForXRCC3 rs1799796 polymorphisms, the meta-analysis results were shown in Table 4. We observed a statistically significant correlation with ovarian cancer risk using the heterozygote comparison (T1T2 versus T1T1: OR = 0.91, 95% CI = 0.83–0.99, P
=
0.04). However no statistically significant associations were observed in homozygote comparison (T2T2 versus T1T1: OR = 1.07, 95% CI = 0.93–1.24, P
=
0.33), dominant genetic model (T1T2+T2T2 versus T1T1: OR = 0.94, 95% CI = 0.86–1.03, P
=
0.16), and the recessive genetic model (T2T2 versus T1T1+T1T2: OR = 1.13, 95% CI = 0.98–1.29, P
=
0.08).


### 3.3. Publication Bias and Sensitivity Analysis

The publication bias was tested by Begg’s funnel plot and Egger’s test for three SNPs. Egger’s test results did not show any evidence of publication bias for any of the genetic models of the three SNPs (data not shown). The shape of the four Begg’s funnel plots showed no evidence of obvious asymmetry of the three SNPs (data not shown).In the sensitivity analysis, the corresponding pooled ORs were not altered, when the fixed-effects model was changed to random-effects model. So it revealed that the results of this meta-analysis were stable.


## 3.1. Study Characteristics

Through the literature search, 13 articles were found. Eight articles [35–42] were excluded as irrelevant study. One study [26] was excluded because it was carried out on overlapping populations with another, more samples eligible study [27]. Total 4 articles including 8 studies were selected on 5,302 ovarian cancer cases and 8,075 controls for 3 SNPs [20, 25–27] (Figure 1). These studies were all published in English. The main characteristics of the 4 studies are shown in Table 2. All subjects in these studies were Caucasians. The sample sizes (cases and controls) ranged from 1,478 to 5,906. Quality scores for all studies were high quality (≥10). Distribution of rs861539 polymorphisms genotype frequencies among ovarian cancer cases and controls of the 2 studies is shown in Table 3. Distribution of rs1799794 polymorphisms genotype frequencies is shown in Table 4 and distribution of rs1799796 polymorphisms genotype frequencies is shown in Table 5.Table 2
Main characteristics of the studies included in the meta-analysis.
First author
Year
Country
Area of the cases
Ethnicity
Cases source
Controls source
Sample type of cases
Total cases/controls
Quality score
Auranen [20]
2005
Mixed (UK-USA)
Royal Marsden Hospital in London and 6 counties in Northern California
Caucasian
Mixed (hospital and cancer registry)
Population
Blood
1665/4241
14
Beesley [26]
2007
Australia
New South Wales and Victorian Cancer Registries
Caucasian
Cancer registry
Population
Blood
731/747
15
Quaye [25]
2009
Mixed (DK-UK-USA)
MALOVA from Denmark-SEARCH from the UK-and GEOCS from the USA.
Caucasian
Mixed (hospital and cancer registry)
Population
Blood
1461/2299
14
Webb [27]
2005
Australia
New South Wales-Victoria and Queensland
Caucasian
Mixed (hospital and cancer registry)
Volunteers
Mixed (blood and archival paraffin blocks)
1445/788
12Table 3
Distribution ofXRCC3 rs861539 genotype among ovarian cancer cases and controls included in the meta-analysis.
Firstauthor
Year
Genotypes distribution(Case source)
Genotypes distribution(Controls source)
P-HWE(Controls)
T2T2 versus T1T1
T1T2 versus T1T1
T1T2+T2T2 versus T1T1
T2T2 versus T1T1+T1T2
T1T1
T1T2
T2T2
T1T1
T1T2
T2T2
OR (95% CI)
P
OR (95% CI)
P
OR (95% CI)
P
OR (95% CI)
P
Auranen [20]
2005
676
762
227
1712
1946
583
Yes
0.99 [0.83, 1.18]
0.88
0.99 [0.88, 1.12]
0.89
0.99 [0.88, 1.11]
0.87
0.99 [0.84, 1.17]
0.91
Beesley [26]
2007
291
339
101
288
351
108
Yes
0.93 [0.67, 1.27]
0.63
0.96 [0.77, 1.19]
0.69
0.95 [0.77, 1.17]
0.62
0.95 [0.71, 1.27]
0.72
Quaye [25]
2009
545
612
175
784
958
282
Yes
0.89 [0.72, 1.11]
0.31
0.92 [0.79, 1.07]
0.27
0.91 [0.79, 1.05]
0.21
0.93 [0.76, 1.14]
0.51
Webb [27]
2005
591
656
198
307
375
106
Yes
0.97 [0.74, 1.28]
0.83
0.91 [0.75, 1.10]
0.32
0.92 [0.77, 1.10]
0.37
1.02 [0.79, 1.32]
0.87
Total
2103
2369
701
3091
3630
1079
0.95 [0.85, 1.06]
0.37
0.95 [0.88, 1.03]
0.22
0.95 [0.88, 1.02]
0.19
0.97 [0.88, 1.08]
0.63
Test  for
heterogeneity
P
=
0.91
Test  for
heterogeneity
P
=
0.88
Test  for
heterogeneity
P
=
0.82
Test  for
heterogeneity
P
=
0.77Table 4
Distribution ofXRCC3 rs1799794 genotype among ovarian cancer cases and controls included in the meta-analysis.
First author
Year
Genotypes distribution(Case source)
Genotypes distribution(Controls source)
P-HWE(Controls)
T2T2 versus T1T1
T1T2 versus T1T1
T1T2+T2T2 versus T1T1
T2T2 versus T1T1+T1T2
T1T1
T1T2
T2T2
T1T1
T1T2
T2T2
OR (95% CI)
P
OR (95% CI)
P
OR (95% CI)
P
OR (95% CI)
P
Auranen [20]
2005
1060
550
48
2551
1188
161
Yes
0.72 [0.52, 1.00]
0.048
1.11 [0.98, 1.26]
0.087
1.07 [0.95, 1.20]
0.29
0.69 [0.50, 0.96]
0.027
Quaye [25]
2009
940
484
37
1505
713
89
Yes
0.67 [0.45, 0.99]
0.04
1.09 [0.94, 1.25]
0.25
1.04 [0.91, 1.19]
0.57
0.65 [0.44, 0.96]
0.027
Total
2000
1034
85
4056
1901
250
0.70 [0.54, 0.90]
0.005
1.10 [1.00, 1.21]
0.04
1.06 [0.96, 1.15]
0.24
0.67 [0.52, 0.87]
0.002
Test  for
heterogeneity
P
=
0.77
Test  for
heterogeneity
P
=
0.83
Test  for
heterogeneity
P
=
0.78
Test  for
heterogeneity
P
=
0.80Table 5
Distribution ofXRCC3 rs1799796 genotype among ovarian cancer cases and controls included in the meta-analysis.
First author
Year
Genotypes distribution(Case source)
Genotypes distribution(Controls source)
P-HWE(Controls)
T2T2 versus T1T1
T1T2 versus T1T1
T1T2+T2T2 versus T1T1
T2T2 versus T1T1+T1T2
T1T1
T1T2
T2T2
T1T1
T1T2
T2T2
OR (95% CI)
P
OR (95% CI)
P
OR (95% CI)
P
OR (95% CI)
P
Auranen [20]
2005
769
692
203
1757
1776
433
Yes
1.07 [0.89, 1.29]
0.47
0.89 [0.79, 1.01]
0.062
0.93 [0.83, 1.04]
0.188
1.13 [0.95, 1.35]
0.17
Quaye [25]
2009
676
608
177
1040
1006
253
Yes
1.08 [0.87, 1.33]
0.5
0.93 [0.81, 1.07]
0.31
0.96 [0.84, 1.09]
0.536
1.11 [0.91, 1.37]
0.30
Total
1445
1300
380
2797
2782
686
1.07 [0.93, 1.24]
0.33
0.91 [0.83, 0.99]
0.04
0.94 [0.86, 1.03]
0.16
1.13 [0.98, 1.29]
0.08
Test  for
heterogeneity
P
=
0.97
Test  for
heterogeneity
P
=
0.65
Test  for
heterogeneity
P
=
0.69
Test  for
heterogeneity
P
=
0.90Figure 1
Study flow chart explaining the selection of the four articles included in the meta-analysis.Hardy-Weinberg disequilibrium of genotype frequencies among the controls was calculated in three studies.


## 3.2. Association of Individual Polymorphisms with Ovarian Cancer

The heterogeneity analysis has been carried out. As it was shown in Tables3, 4, and 5, the heterogeneities of 3 SNPs are all not significant. So the fixed-effects model was chosen for 3 SNPs.The meta-analysis results ofXRCC3 rs861539 polymorphisms are shown in Table 3. No statistically significant associations betweenXRCC3 rs861539 polymorphisms and ovarian cancer risk were observed in any genetic models (T2T2 versus T1T1: OR = 0.95, 95% CI = 0.85–1.06, P
=
0.37; T1T2 versus T1T1: OR = 0.95, 95% CI = 0.88–1.03, P
=
0.22; T1T2+T2T2 versus T1T1: OR = 0.95, 95% CI = 0.88–1.02, P
=
0.19; T2T2 versus T1T1+T1T2: OR = 0.97, 95% CI = 0.88–1.08, P
=
0.63).ForXRCC3 rs1799794 polymorphisms, two studies [16, 18, 20, 21, 23, 24] (3,119 cases and 6,207 controls) were eligible. The meta-analysis results of rs1799794 polymorphisms are shown in Table 4. We observed a statistically significant correlation with ovarian cancer risk using the homozygote comparison (T2T2 versus T1T1: OR = 0.70, 95% CI = 0.54–0.90, P
=
0.00
5), heterozygote comparison (T1T2 versus T1T1: OR = 1.10, 95% CI = 1.00–1.21, P
=
0.04), and the recessive genetic model (T2T2 versus T1T1+T1T2 : OR = 0.67, 95% CI = 0.52–0.87, P
=
0.002). However, no statistically significant associations were observed in dominant genetic model (T1T2+T2T2 versus T1T1: OR = 1.06, 95% CI = 0.96–1.15, P
=
0.24).ForXRCC3 rs1799796 polymorphisms, the meta-analysis results were shown in Table 4. We observed a statistically significant correlation with ovarian cancer risk using the heterozygote comparison (T1T2 versus T1T1: OR = 0.91, 95% CI = 0.83–0.99, P
=
0.04). However no statistically significant associations were observed in homozygote comparison (T2T2 versus T1T1: OR = 1.07, 95% CI = 0.93–1.24, P
=
0.33), dominant genetic model (T1T2+T2T2 versus T1T1: OR = 0.94, 95% CI = 0.86–1.03, P
=
0.16), and the recessive genetic model (T2T2 versus T1T1+T1T2: OR = 1.13, 95% CI = 0.98–1.29, P
=
0.08).


## 3.3. Publication Bias and Sensitivity Analysis

The publication bias was tested by Begg’s funnel plot and Egger’s test for three SNPs. Egger’s test results did not show any evidence of publication bias for any of the genetic models of the three SNPs (data not shown). The shape of the four Begg’s funnel plots showed no evidence of obvious asymmetry of the three SNPs (data not shown).In the sensitivity analysis, the corresponding pooled ORs were not altered, when the fixed-effects model was changed to random-effects model. So it revealed that the results of this meta-analysis were stable.


## 4. Discussion

TheXRCC3 gene is required for genomic stability [36]. It was reported that theXRCC3 polymorphism increased the risk of many cancers, including ovarian cancer [36]. However, the results have been inconsistent. We preformed the meta-analysis including 5,302 ovarian cancer cases and 8,075 controls for 3 SNPs ofXRCC3.For rs861539 polymorphisms, no correlation with ovarian cancer risk was observed in any genetic models. However, ForXRCC3 rs1799794 and rs1799796 polymorphisms, we observed a statistically significant correlation with ovarian cancer risk. It was shown that the difference between different SNP sites was considerable forXRCC3.All of the literature was of high quality. All study subjects were Caucasian. The global multicenter studies can provide more valuable conclusions. So further studies should be done to explore the possible relationships betweenXRCC3 polymorphisms and ovarian cancer risk in other ethnicities.In conclusion, this meta-analysis shows that theXRCC3 were associated with ovarian cancer risk overall for Caucasians. Asian and African populations should be further studied.



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*Source: 648137-2014-06-10.xml*