BioMed Research International

Volume 2014 (2014), Article ID 648137, 9 pages

http://dx.doi.org/10.1155/2014/648137

Review Article

Analyzing Association of the XRCC3 Gene Polymorphism with Ovarian Cancer Risk

Cunzhong Yuan,¹ Xiaoyan Liu,¹ Shi Yan,¹ Cunfang Wang,² and Beihua Kong¹

¹Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, Shandong 250012, China

²Shandong Provincial Key Laboratory of Microbiological Engineering, Qilu University of Technology, Jinan, Shandong 250000, China

Received 11 October 2013; Revised 25 April 2014; Accepted 20 May 2014; Published 10 June 2014

Academic Editor: Danny N. Dhanasekaran

Copyright © 2014 Cunzhong Yuan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

This meta-analysis aims to examine whether the XRCC3 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, ), heterozygote comparison (T1T2 versus T1T1: OR = 1.10, 95% CI = 1.00–1.21, ), and the recessive genetic model (T2T2 versus T1T1+T1T2: OR = 0.67, 95% CI = 0.52–0.87, ). 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, ). 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.

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, and MTDH 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 (4541 A > G) is located in 5′UTR and the SNP rs1799796 (17893 A > G) is located in intron 5 [20]. So the two SNPs do not change the proteins of XRCC3. 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 between XRCC3 polymorphism and ovarian cancer has been studied [20, 25–29]; however, those experimental results remain confusing. To summarize the effect of the XRCC3 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 between XRCC3 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 of XRCC3 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 (Table 1), 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 between XRCC3 polymorphism and ovarian cancer susceptibility, according to the genotype frequencies of cases and controls groups [32]. 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].

The XRCC3 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 ( 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
12

Table 3: Distribution of XRCC3 rs861539 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)

OR (95% CI)

OR (95% CI)

OR (95% CI)

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

Test  for 

heterogeneity

Test  for 

heterogeneity

Test  for 

 heterogeneity

Table 4: Distribution of XRCC3 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)

OR (95% CI)

OR (95% CI)

OR (95% CI)

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

Test  for  

heterogeneity

Test  for  

heterogeneity

Test  for  

heterogeneity

Table 5: Distribution of XRCC3 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)

OR (95% CI)

OR (95% CI)

OR (95% CI)

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

Test  for  

heterogeneity

Test  for  

heterogeneity

Test  for  

heterogeneity

Figure 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 Tables 3, 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 of XRCC3 rs861539 polymorphisms are shown in Table 3. No statistically significant associations between XRCC3 rs861539 polymorphisms and ovarian cancer risk were observed in any genetic models (T2T2 versus T1T1: OR = 0.95, 95% CI = 0.85–1.06, ; T1T2 versus T1T1: OR = 0.95, 95% CI = 0.88–1.03, ; T1T2+T2T2 versus T1T1: OR = 0.95, 95% CI = 0.88–1.02, ; T2T2 versus T1T1+T1T2: OR = 0.97, 95% CI = 0.88–1.08, ).

For XRCC3 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, ), heterozygote comparison (T1T2 versus T1T1: OR = 1.10, 95% CI = 1.00–1.21, ), and the recessive genetic model (T2T2 versus T1T1+T1T2 : OR = 0.67, 95% CI = 0.52–0.87, ). However, no statistically significant associations were observed in dominant genetic model (T1T2+T2T2 versus T1T1: OR = 1.06, 95% CI = 0.96–1.15, ).

For XRCC3 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, ). However no statistically significant associations were observed in homozygote comparison (T2T2 versus T1T1: OR = 1.07, 95% CI = 0.93–1.24, ), dominant genetic model (T1T2+T2T2 versus T1T1: OR = 0.94, 95% CI = 0.86–1.03, ), and the recessive genetic model (T2T2 versus T1T1+T1T2: OR = 1.13, 95% CI = 0.98–1.29, ).

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

The XRCC3 gene is required for genomic stability [36]. It was reported that the XRCC3 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 of XRCC3.

For rs861539 polymorphisms, no correlation with ovarian cancer risk was observed in any genetic models. However, For XRCC3 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 for XRCC3.

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 between XRCC3 polymorphisms and ovarian cancer risk in other ethnicities.

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.

Abbreviations

CIs: | Confidence intervals
HWE: | Hardy-Weinberg equilibrium
ORs: | Odds ratios
XRCC3: | X-ray repair cross-complementing group 3.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (81001166, 81272857), Independent Innovation Foundation of Shandong University (2012TS142), Natural Science Foundation of Shandong Province (ZR2010HQ050), Science and Technology Development Project of Shandong Province (2013GNC11306, czfz02), China Postdoctoral Science Foundation (201104636, 20100471551), and Soft Science Research Project of Shandong Province (2013RKE27054). The funders had no role in study design, data collection or analysis, decision to publish, or preparation of the paper.

References

R. Siegel, D. Naishadham, and A. Jemal, “Cancer statistics, 2012,” CA: A Cancer Journal for Clinicians, vol. 62, no. 1, pp. 10–29, 2012.

H. A. Risch, “Hormonal etiology of epithelial ovarian cancer, with a hypothesis concerning the role of androgens and progesterone,” Journal of the National Cancer Institute, vol. 90, no. 23, pp. 1774–1786, 1998.

A. Meindl, H. Hellebrand, C. Wiek et al., “Germline mutations in breast and ovarian cancer pedigrees establish RAD51C as a human cancer susceptibility gene,” Nature Genetics, vol. 42, no. 5, pp. 410–414, 2010.

C. Loveday, C. Turnbull, E. Ramsay et al., “Germline mutations in RAD51D confer susceptibility to ovarian cancer,” Nature Genetics, vol. 43, no. 9, pp. 879–882, 2011.

J. Yin, K. Lu, J. Lin et al., “Genetic variants in TGF-β pathway are associated with ovarian cancer risk,” PLoS ONE, vol. 6, no. 9, Article ID e25559, 2011.

X. Ma, J. Zhang, S. Liu, Y. Huang, B. Chen, and D. Wang, “Polymorphisms in the CASP8 gene and the risk of epithelial ovarian cancer,” Gynecologic Oncology, vol. 122, no. 3, pp. 554–559, 2011.

J. Permuth-Wey, D. Kim, Y.-Y. Tsai et al., “LIN28B polymorphisms influence susceptibility to epithelial ovarian cancer,” Cancer Research, vol. 71, no. 11, pp. 3896–3903, 2011.

M. G. M. Braem, L. J. Schouten, P. H. M. Peeters, P. A. van den Brandt, and N. C. Onland-Moret, “Genetic susceptibility to sporadic ovarian cancer: a systematic review,” Biochimica et Biophysica Acta, vol. 1816, no. 2, pp. 132–146, 2011.

L. M. Pelttari, T. Heikkinen, D. Thompson et al., “RAD51C is a susceptibility gene for ovarian cancer,” Human Molecular Genetics, vol. 20, no. 16, pp. 3278–3288, 2011.

S. J. Ramus, A. C. Antoniou, K. B. Kuchenbaecker et al., “Ovarian cancer susceptibility alleles and risk of ovarian cancer in BRCA1 and BRCA2 mutation carriers,” Human Mutation, vol. 33, no. 4, pp. 690–702, 2012.

C. Yuan, X. Li, S. Yan, Q. Yang, X. Liu, and B. Kong, “The MTDH (-470G>A) polymorphism is associated with ovarian cancer susceptibility,” PLoS ONE, vol. 7, no. 12, Article ID e51561, 2012.

K. L. Bolton, J. Tyrer, H. Song, et al., “Common variants at 19p13 are associated with susceptibility to ovarian cancer,” Nature Genetics, vol. 42, no. 10, pp. 880–884, 2010.

E. L. Goode, G. Chenevix-Trench, H. Song, et al., “A genome-wide association study identifies susceptibility loci for ovarian cancer at 2q31 and 8q24,” Nature Genetics, vol. 42, no. 10, pp. 874–879, 2010.

H. Song, S. J. Ramus, J. Tyrer, et al., “A genome-wide association study identifies a new ovarian cancer susceptibility locus on 9p22. 2.,” Nature Genetics, vol. 41, no. 9, pp. 996–1000, 2009.

Y. Li, H. Zhao, L. Sun, L. Huang, Q. Yang, and B. Kong, “MDM2 SNP309 is associated with endometrial cancer susceptibility: a meta-analysis,” Human Cell, vol. 24, no. 2, pp. 57–64, 2011.

R. S. Tebbs, Y. Zhao, J. D. Tucker et al., “Correction of chromosomal instability and sensitivity to diverse mutagens by a cloned cDNA of the XRCC3 DNA repair gene,” Proceedings of the National Academy of Sciences of the United States of America, vol. 92, no. 14, pp. 6354–6358, 1995.

P. Zhan, Q. Wang, Q. Qian, and L.-K. Yu, “XRCC3 Thr241Met gene polymorphisms and lung cancer risk: a meta-analysis,” Journal of Experimental & Clinical Cancer Research, vol. 32, article 1, 2013.

B. Zhao, J. Ye, B. Li, Q. Ma, G. Su, and R. Han, “DNA repair gene XRCC3 Thr241Met polymorphism and glioma risk: a meta-analysis,” International Journal of Clinical and Experimental Medicine, vol. 6, no. 6, pp. 438–443, 2013.

G. Matullo, D. Palli, M. Peluso et al., “XRCC1, XRCC3, XPD gene polymorphisms, smoking and 32P-DNA adducts in a sample of healthy subjects,” Carcinogenesis, vol. 22, no. 9, pp. 1437–1445, 2001.

A. Auranen, H. Song, C. Waterfall et al., “Polymorphisms in DNA repair genes and epithelial ovarian cancer risk,” International Journal of Cancer, vol. 117, no. 4, pp. 611–618, 2005.

Q.-H. Yin, C. Liu, L. Li, X.-Y. Zu, and Y.-J. Wang, “Association between the XRCC3 T241M polymorphism and head and neck cancer susceptibility: a meta-analysis of case-control studies,” Asian Pacific Journal of Cancer Prevention, vol. 13, no. 10, pp. 5201–5205, 2012.

X.-F. He, W. Wei, J. Su et al., “Association between the XRCC3 polymorphisms and breast cancer risk: meta-analysis based on case-control studies,” Molecular Biology Reports, vol. 39, no. 5, pp. 5125–5134, 2012.

X. Tian, Y. Tian, P. Ma, et al., “Association between the XRCC3 C241T polymorphism and lung cancer risk in the Asian population,” Tumour Biology, vol. 34, no. 5, pp. 2589–2597, 2013.

X.-F. He, W. Wei, J.-L. Li et al., “Association between the XRCC3 T241M polymorphism and risk of cancer: evidence from 157 case-control studies,” Gene, vol. 523, no. 1, pp. 10–19, 2013.

L. Quaye, J. Tyrer, S. J. Ramus et al., “Association between common germline genetic variation in 94 candidate genes or regions and risks of invasive epithelial ovarian cancer,” PLoS ONE, vol. 4, no. 6, Article ID e5983, 2009.

J. Beesley, S. J. Jordan, A. B. Spurdle et al., “Association between single-nucleotide polymorphisms in hormone metabolism and DNA repair genes and epithelial ovarian cancer: results from two Australian studies and an additional validation set,” Cancer Epidemiology Biomarkers and Prevention, vol. 16, no. 12, pp. 2557–2565, 2007.

P. M. Webb, J. L. Hopper, B. Newman et al., “Double-strand break repair gene polymorphisms and risk of breast or ovarian cancer,” Cancer Epidemiology Biomarkers and Prevention, vol. 14, no. 2, pp. 319–323, 2005.

C. L. Pearce, K. Chung, M. C. Pike, and A. H. Wu, “Increased ovarian cancer risk associated with menopausal estrogen therapy is reduced by adding a progestin,” Cancer, vol. 115, no. 3, pp. 531–539, 2009.

C. L. Pearce, A. M. Near, D. J. van den Berg et al., “Validating genetic risk associations for ovarian cancer through the international Ovarian Cancer Association Consortium,” British Journal of Cancer, vol. 100, no. 2, pp. 412–420, 2009.

D.-K. Jiang, W.-Z. Wang, W.-H. Ren, L. Yao, B. Peng, and L. Yu, “TP53 Arg72Pro polymorphism and skin cancer risk: a meta-analysis,” Journal of Investigative Dermatology, vol. 131, no. 1, pp. 220–228, 2011.

S.-Q. Shen, D.-K. Jiang, G.-Y. Liu, F. Chen, and L. Yu, “Meta-analysis shows significant association of the TP53 Arg72Pro with ovarian cancer risk,” Molecular Biology Reports, vol. 39, no. 4, pp. 4683–4690, 2012.

N. Mantel and W. Haenszel, “Statistical aspects of the analysis of data from retrospective studies of disease,” Journal of the National Cancer Institute, vol. 22, no. 4, pp. 719–748, 1959.

R. DerSimonian and N. Laird, “Meta-analysis in clinical trials,” Controlled Clinical Trials, vol. 7, no. 3, pp. 177–188, 1986.

M. Egger, G. D. Smith, M. Schneider, and C. Minder, “Bias in meta-analysis detected by a simple, graphical test,” British Medical Journal, vol. 315, no. 7109, pp. 629–634, 1997.

B. S. Pedersen, P. A. Konstantinopoulos, M. A. Spillman, and S. De, “Copy neutral loss of heterozygosity is more frequent in older ovarian cancer patients,” Genes Chromosomes and Cancer, vol. 52, no. 9, pp. 794–801, 2013.

C. X. Cheng, M. Xue, K. Li, and W. S. Li, “Predictive value of XRCC1 and XRCC3 gene polymorphisms for risk of ovarian cancer death after chemotherapy,” Asian Pacific Journal of Cancer Prevention, vol. 13, no. 6, pp. 2541–2545, 2012.

P. Gonzalez-Hormazabal, J. M. Reyes, R. Blanco et al., “The BARD1 Cys557Ser variant and risk of familial breast cancer in a South-American population,” Molecular Biology Reports, vol. 39, no. 8, pp. 8091–8098, 2012.

J. Clague, G. Wilhoite, A. Adamson, A. Bailis, J. N. Weitzel, and S. L. Neuhausen, “RAD51C germline mutations in breast and ovarian cancer cases from high-risk families,” PLoS ONE, vol. 6, no. 9, Article ID e25632, 2011.

Y. Drew, E. A. Mulligan, W.-T. Vong et al., “Therapeutic potential of poly(ADP-ribose) polymerase inhibitor AG014699 in human cancers with mutated or methylated BRCA1 or BRCA2,” Journal of the National Cancer Institute, vol. 103, no. 4, pp. 334–346, 2011.

P. Gottipati, B. Vischioni, N. Schultz et al., “Poly(ADP-ribose) polymerase is hyperactivated in homologous recombination-defective cells,” Cancer Research, vol. 70, no. 13, pp. 5389–5398, 2010.

A. Jakubowska, J. Gronwald, J. Menkiszak et al., “BRCA1-associated breast and ovarian cancer risks in Poland: no association with commonly studied polymorphisms,” Breast Cancer Research and Treatment, vol. 119, no. 1, pp. 201–211, 2010.

P. Danoy, E. Sonoda, M. Lathrop, S. Takeda, and F. Matsuda, “A naturally occurring genetic variant of human XRCC2 (R188H) confers increased resistance to cisplatin-induced DNA damage,” Biochemical and Biophysical Research Communications, vol. 352, no. 3, pp. 763–768, 2007.