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Obstetrics and gynaecology
Basic research

Analysis of the association between the XRCC2 rs3218536 polymorphism and ovarian cancer risk

Cunzhong Yuan
1, 2
,
Xiaoyan Liu
1, 2
,
Rongrong Li
1, 2
,
Shi Yan
1, 2
,
Beihua Kong
1, 2

1.
Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Ji’nan, Shandong, China
2.
Shandong Key Laboratory of Gynecologic Oncology, Qilu Hospital of Shandong University, Ji’nan, Shandong, China
Arch Med Sci 2020; 16 (3): 682–691
Online publish date: 2020/04/25
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Introduction

Ovarian cancer is the leading cause of death from gynecologic cancer in the developed world, with over 220,000 new cases and 140,000 deaths worldwide in 2008 [13]. Ovarian cancer is also a multifactorial disease, as is true of most carcinomas. Genetic factors play an important role in ovarian cancer susceptibility [2, 4].

The genetic factors responsible for ovarian carcinogenesis have been investigated in many studies. MLH1, MSH2, BRCA1, BRCA2, LIN28B, CASP8, SMAD6, RAD51C, RAD51D, RB1, MTDH, and GADD45A have all been implicated in ovarian cancer [1, 513]. Three genome-wide association studies (GWAS) have revealed a strong association between ovarian cancer risk and several common susceptibility alleles in four loci [2, 1416]. The examination of genetic polymorphisms may explain individual differences in cancer risk [17]. However, the results of the three GWAS were not unanimous. Thus, further investigation is required to identify the genes that are associated with a predisposition to ovarian cancer [1, 10].

XRCC2 (X-ray repair cross-complementing group 2), located at 7q36.1, is a functional candidate gene in neoplasia [18, 19]. XRCC2/3 interacts with and stabilizes Rad51, and takes part in the HRR (homologous recombination repair) of DNA DBSs (double-strand breaks) and in cross-link repair in mammalian cells [2022]. XRCC2 polymorphism has been associated with the risk of many cancers, such as breast cancer, prostate cancer, gastric cancer, and thyroid carcinoma [2327].

Although the association between XRCC2 polymorphism and ovarian cancer has been studied [2835], the experimental results remain inconclusive. Furthermore, while meta-analyses of XRCC2 polymorphism and ovarian cancer risk have also been performed [8, 19, 25, 36, 37], the results need to be supplemented. To examine the effect of XRCC2 polymorphism on ovarian cancer risk, we performed a meta-analysis.

Material and methods

Search and selection process

We performed the meta-analysis by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria [38]. We searched the PubMed database using combinations of the following keywords: “XRCC2”, “X-ray repair cross-complementing group 2”, “rs3218536”, “Arg188His”, “R188H”, “ovarian cancer”, and “polymorphism”. Two authors, Yuan and Yan, independently examined the retrieved references to evaluate their appropriateness for inclusion in this meta-analysis. In addition, we investigated all of the references cited in the articles and the relevant reviews. If an article reported results that included a number of studies, each study was treated as a separate comparison in our meta-analysis.

Included studies required the following 3 criteria:

  1. Evaluated XRCC2 polymorphism and ovarian cancer risk;

  2. Provided sufficient data (i.e., a detailed number of genotypes in both the case and control groups);

  3. Included case-control studies.

Data extraction

The data were independently extracted from selected articles according to the pre-specified criteria by the two authors (Yuan and Wang). All of the necessary information, if available, was extracted from each study, including the first author, publication year, country, area of the cases, ethnicity, cases’ source, controls’ source, sample type of the cases, the total number of cases and controls, and the genotype distributions of XRCC2 in both the cases and controls [39]. Disagreements were resolved by joint review and consensus.

Quality score assessment

Eleven studies were independently evaluated by two authors according to a previously established scale for quality assessment (Table I) [2, 40]. The quality score assessment was carried out according to the following criteria: “source of cases”, “source of controls”, “specimens of cases for determining genotypes”, “Hardy-Weinberg equilibrium in controls” and “total sample size”. The total scores ranged from 0 (worst) to 15 (best). Studies scoring ≥ 10 were defined as “high quality”, while those scoring < 10 were defined as “low quality” [2, 40, 41].

Table I

Scale for quality assessment

CriteriaScore
Source of cases:
 Population or cancer registry3
 Mixed (hospital and cancer registry)2
 Hospital1
 Other0
Source of controls:
 Population-based3
 Volunteers or blood bank2
 Hospital-based (cancer-free patients)1
 Not described0
Specimens of cases for determining genotypes:
 Blood or normal tissues3
 Mixed (blood and archival paraffin blocks)1
 Tumor tissues or exfoliated cells of tissue0
Hardy-Weinberg equilibrium in controls:
 Hardy-Weinberg equilibrium3
 Hardy-Weinberg disequilibrium0
Total sample size:
 ≥ 10003
 ≥ 500 and < 10002
 ≥ 200 and < 5001
 < 2000

Statistical analysis

We pooled ORs with 95% CIs, according to the genotype frequencies of the case and control groups, to assess the strength of the association between the XRCC2 polymorphism and ovarian cancer susceptibility [42]. A p-value < 0.05 was considered statistically significant. All of the tests and CIs were two-sided. If the heterogeneity was significant, the pooled ORs were initially measured using the random effects model. Otherwise, the fixed effects model was chosen [41, 43].

XRCC2 polymorphism and ovarian cancer risk analysis was carried out for a homozygote comparison (AA vs. GG), a heterozygote comparison (GA vs. GG), a dominant genetic model (GA + AA vs. GG), and a recessive genetic model (AA vs. GG + GA). In addition, a sensitivity analysis was carried out by omitting each study. Publication bias was examined using a funnel plot. The degree of asymmetry was estimated by Egger’s test (p < 0.05 was considered significant publication bias) [2] [44, 45]. The analysis was completed 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].

Results

Study characteristics

Through the article search, we found 24 articles. Of these articles, we excluded 16 because the studies were irrelevant. We also excluded one article [32] because the study did not report the relevant genotype frequencies. Although we contacted the study’s authors for the genotype frequencies, we did not obtain the genotype frequencies of rs3218536 from that article. Thus, a total of 7 articles included 11 studies [28, 30, 31, 3335, 46] of 5,802 ovarian cancer cases and 9,390 controls. The study flowchart is shown in Figure 1. The 7 articles were all published in English. The characteristics of the 11 studies from the 7 articles are summarized in Table II. The subjects in 10 of the studies [28, 30, 31, 33, 35, 46] were Caucasian. In the 1 other study, Caucasians comprised 94% of the mixed subject group [34]. Thus, most of the subjects in these 11 studies were Caucasian. The sample sizes, including cases and controls, ranged from 100 to 1,811, and the total sample sizes ranged from 200 to 3,124. The quality scores for the individual studies ranged from 5 to 12. The quality scores for 8 of the studies (72.7%) were classified as high quality (≥ 10).

Figure 1

Study flowchart explaining the selection of the five articles included in the meta-analysis

/f/fulltexts/AOMS/40440/AMS-16-3-40440-g001_min.jpg
Table II

Main characteristics of the 11 studies included in the meta-analysis

First authorYearCountryArea of the casesEthnicityCases sourceControls sourceSample type of casesTotal cases/controlsQuality score
Auranen-12005UKEast Anglia and West MidlandsCaucasianCancer registryPopulationBlood729/84212
Auranen-22005DenmarkDenmarkCaucasianPopulationPopulationBlood269/56111
Auranen-32005USANorthern CaliforniaCaucasianCancer registryPopulationBlood315/40411
Auranen-42005UKUnited KingdomCaucasianHospital & cancer registryPopulationBlood275/181111
Beesley-12007AustraliaNew South Wales, Victoria, and QueenslandCaucasianCancer registryPopulationBlood486/96912
Beesley-22007AustraliaNew South Wales and Victorian Cancer RegistriesCaucasianCancer registryPopulationBlood923/81812
Jakubowska2010PolandPolandCaucasianCancer registryHospitalBlood144/2808
Michalska2016PolandInstitute of Polish Mother’s Memorial Hospital, Lodz,CaucasianHospitalHospitalFFPE700/7005
Mohamed2013EgyptZagazig University Hospital at SharkiaCaucasianHospitalHospitalBlood100/1006
Quaye2009DK & UK & USMALOVA from Denmark, SEARCH from UK, and GEOCS from USACaucasianHospital & cancer registryPopulationBlood1337/178711
Webb2005AustraliaQueenslandMixed (Caucasian was 94%)Hospital & cancer registryPopulationBlood524/111811

The distribution of the XRCC2 rs3218536 polymorphism genotype frequencies among the ovarian cancer cases and controls from the 11 studies is shown in Table III. A Hardy-Weinberg disequilibrium of genotype frequencies among the controls was calculated in 11 studies [28, 30, 31, 3335, 46]. In 7 studies [31, 3335], the genotype distribution among the control groups was in agreement with HWE (p > 0.05). In 3 studies [28, 30, 35], the genotype distribution among the control groups was not in agreement with HWE (p < 0.05). In 1 study [46], the genotype distribution among the control groups was not estimable.

Table III

Distribution of the XRCC2 rs3218536 genotype among ovarian cancer cases and controls included in the meta-analysis

First authorYearGenotype distribution (case source)Genotype distribution (controls source)P-HWE (controls)AA vs. GGGA vs. GGGA + AA vs. GGAA vs. GG + GA
GGGAAAGGGAAAOR (95% CI)P-valueOR (95% CI)P-valueOR (95% CI)P-valueOR (95% CI)P-value
Auranen-1200562998270412990.290.25 (0.05–1.16)0.0540.85 (0.64–1.13)0.260.81 (0.61–1.07)0.140.25 (0.05–1.18)0.06
Auranen-220052383104847520.60.41 (0.02–8.50)0.320.84 (0.54–1.31)0.450.82 (0.52–1.28)0.380.42 (0.02–8.68)0.33
Auranen-320052605413316850.50.25 (0.03–2.19)0.181.01 (0.68–1.50)0.960.96 (0.65–1.41)0.830.25 (0.03–2.19)0.18
Auranen-42005251231153826760.091.02 (0.12–8.52)0.980.53 (0.34–0.82)0.00440.54 (0.35–0.83)0.0051.10 (0.13–9.15)0.93
Beesley-1200741467581914280.521.24 (0.40–3.80)0.710.93 (0.68–1.28)0.670.95 (0.70–1.29)0.741.25 (0.41–3.84)0.7
Beesley-22007799117769611570.380.87 (0.30–2.50)0.80.89 (0.67–1.17)0.390.89 (0.68–1.16)0.380.89 (0.31–2.53)0.82
Jakubowska201012816 (GA+AA)24634 (GA+AA)N/EN/EN/EN/EN/E0.89 (0.47–1.68)0.76N/EN/E
Michalska201612080500180400120< 0.00016.25 (4.61–8.48)< 0.00010.30 (0.22–0.42)< 0.00011.67 (1.29–2.17)< 0.000112.08 (9.35–15.61)< 0.0001
Mohamed2013658361660240.0374.00 (1.37–11.67)0.00862.58 (0.94–7.04)0.0592.98 (1.12–7.98)0.0241.78 (0.96–3.29)0.064
Quaye2009115218231505266160.290.24 (0.07–0.84)0.0160.89 (0.73–1.10)0.280.86 (0.70–1.05)0.130.25 (0.07–0.86)0.017
Webb2005451685952156100.231.06 (0.36–3.11)0.920.92 (0.68–1.25)0.590.93 (0.69–1.25)0.621.07 (0.36–3.14)0.91
Auranen-1200562998270412990.290.25 (0.05–1.16)0.0540.85 (0.64–1.13)0.260.81 (0.61–1.07)0.140.25 (0.05–1.18)0.06

[i] N/E – not estimable.

Meta-analysis results

The meta-analysis results of the XRCC2 rs3218536 polymorphism are shown in Tables III and IV, and Figure 2. When all 11 studies were pooled in the meta-analysis, no statistically significant associations between the XRCC2 rs3218536 polymorphism and ovarian cancer risk were found in any of the genetic models (AA vs. GG: OR = 0.96, 95% CI: 0.36–2.53, p = 0.94; GA vs. GG: OR = 0.80, 95% CI: 0.62–1.02, p = 0.07; GA + AA vs. GG: OR = 0.95, 95% CI: 0.79–1.14, p = 0.57; AA vs. GG + GA: OR = 0.90, 95% CI: 0.43–1.89, p = 0.78). However, when the high quality studies were pooled in the meta-analysis, a significant relationship with ovarian cancer risk was discovered (AA vs. GG: OR = 0.59, 95% CI: 0.37–0.94, p = 0.03; GA vs. GG: OR = 0.87, 95% CI: 0.78–0.96, p = 0.009; GA + AA vs. GG: OR = 0.85, 95% CI: 0.77–0.94, p = 0.003; AA vs. GG + GA: OR = 0.60, 95% CI: 0.38–0.95, p = 0.03).

Figure 2

Forest plot summary of ORs and 95% CIs for the association between the XRCC2 rs3218536 polymorphism and ovarian cancer risk in all genetic models

/f/fulltexts/AOMS/40440/AMS-16-3-40440-g002_min.jpg/f/fulltexts/AOMS/40440/AMS-16-3-40440-g002a_min.jpg
Table IV

Results of the meta-analysis for the XRCC2 rs3218536 polymorphism and ovarian cancer risk

Study groupsSample size (case/control)AA vs. GGGA vs. GGGA + AA vs. GGAA vs. GG + GA
OR (95% CI)P-valueaP-valuebOR (95% CI)P-valueaP-valuebOR (95% CI)P-valueaP-valuebOR (95% CI)P-valueaP-valueb
Total5802/93900.96 (0.36–2.53)< 0.00010.94c0.80 (0.62–1.02)< 0.00010.07c0.95 (0.79–1.14)0.00030.57c0.90 (0.43–1.89)< 0.00010.78c
≥ 10 (Quality of studies)4991/86420.59 (0.37–0.94)0.390.03d0.87 (0.78–0.96)0.560.009d0.85 (0.77–0.94)0.590.003d0.60 (0.38–0.95)0.390.03d

a P value of Q-test for heterogeneity test,

b statistically significant results,

c random-effects model was used,

d fixed-effects model was used.

Sensitivity analysis and publication bias

In the sensitivity analysis, we omitted a single study from the pooled OR of the meta-analysis each time [41]. The exclusion of the low quality studies significantly modified the heterogeneity and results of the meta-analysis.

We checked the publication bias by using both Begg’s funnel plot and Egger’s test. The shapes of the four Begg’s funnel plots for all 11 studies showed no obvious asymmetry (Figure 3). The shapes of the four Begg’s funnel plots for the 8 high quality studies also showed no obvious asymmetry (Figure 4). The Egger’s test of the 8 high quality studies showed no significant publication bias for any of the genetic models (data not shown).

Figure 3

Begg’s funnel plot of the XRCC2 rs3218536 polymorphism and ovarian cancer risk in all genetic models for all 11 studies. Each hollow circle represents a separate study for the indicated association, and its size is proportional to the sample size of each study

/f/fulltexts/AOMS/40440/AMS-16-3-40440-g003_min.jpg
Figure 4

Begg’s funnel plot of the XRCC2 rs3218536 polymorphism and ovarian cancer risk in all genetic models for the 8 high quality studies. Each hollow circle represents a separate study for the indicated association, and its size is proportional to the sample size of each study

/f/fulltexts/AOMS/40440/AMS-16-3-40440-g004_min.jpg

Discussion

The XRCC2 gene plays a crucial role in homologous recombination repair and cross-link repair [2022]. Studies have shown that the XRCC2 rs3218536 polymorphism is associated with the risk of many cancers, including prostate cancer, breast cancer, and gastric cancer [2327]. The association between the XRCC2 rs3218536 polymorphism and the risk of ovarian cancer has been extensively studied. A 2015 meta-analysis study reported on the association between the rs3218536 polymorphism and ovarian cancer risk [36]. However, that study did not include all of the studies related to the association between the rs3218536 polymorphism and ovarian cancer risk. In 2015, another study also reported on the association between the rs3218536 polymorphism and ovarian cancer risk [28]. However, those results were inconsistent. Therefore, we performed a meta-analysis of 5,802 ovarian cancer cases and 9,390 controls from 7 published articles and 11 case-control studies.

There were no statistically significant associations between the rs3218536 polymorphism and ovarian cancer risk in any of the genetic models that included all 11 studies. However, a significant relationship with ovarian cancer risk was discovered when the 8 high quality studies were pooled. Thus, the low quality studies seriously interfered with the meta-analysis results. The quality of the study was crucial for detecting a significant relationship between ovarian cancer risk and genetic polymorphisms. Furthermore, most of the subjects were Caucasian [28, 30, 31, 3335, 46], so further studies may be needed to explore the possible relationship between the rs3218536 polymorphism and ovarian cancer risk in other ethnicities, areas, non-Caucasian groups, Africans, and Asians.

In conclusion, to our knowledge, the present meta-analysis on the association between the XRCC2 rs3218536 polymorphism and ovarian cancer risk was performed systematically and comprehensively. In conclusion, this meta-analysis shows that the XRCC2 rs3218536 polymorphism was associated with ovarian cancer risk in high quality studies overall. Non-Caucasian groups and high quality studies should be examined further.

Acknowledgments

This work was supported by the Fundamental Research Funds of Shandong University (2018JC014), the National Natural Science Foundation of China (81874107, 81572554), the Natural Science Foundation of Shandong Province (ZR2016HM38). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conflict of interest

The authors declare no conflict of interest.

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