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. 2024;25(1):43–55. doi: 10.31557/APJCP.2024.25.1.43

Association between XRCC2 Arg188His Polymorphism and Breast Cancer Susceptibility: A Systematic Review and Meta-Analysis

Seye Alireza Dastgheib 1, Soheila Sayad 2,*, Sepideh Azizi 3, Nazanin Hajizadeh 4, Fatemeh Asadian 5, Mojgan Karimi-Zarchi 6, Maedeh Barahman 7, Amirmasoud Shiri 8, Mohammad Manzourolhojeh 9, Kazem Aghili 10, Hossein Neamatzadeh 9
PMCID: PMC10911734  PMID: 38285766

Abstract

Breast cancer is one of the most common cancers in the world and leading cause of cancer-related death among women. Several studies indicated that Arg188His (rs3218536) polymorphism of X-ray repair cross-complementing 2 (XRCC2) may be associated with breast cancer risk. However, this association remains ambiguous. Thus, we performed a meta-analysis to provide more precise conclusion on this issue. A comprehensive search in PubMed, Google Scholar and ISI Web of Science was performed to select all relevant studies. Odds ratios (OR) with corresponding 95% confidence intervals (CI) were applied to assess the strength of the relationships. A total of 17 studies with 5694 breast cancer cases and 6450 healthy subjects were identified. The pooled data revealed that XRCC2 Arg188His polymorphism was marginally with susceptibility to breast cancer globally under the heterozygote contrast (OR = 0.929, 95% CI = 0.873-0.987, p=0.018). Moreover, subgroup analysis by ethnicity revealed that this polymorphism was associated with breast cancer risk among Caucasians. On the whole, the present study demonstrates that the XRCC2 Arg188His polymorphism may contribute to an increased risk of breast cancer.

Key Words: Breast Cancer, XRCC2, Arg188His, Polymorphism, Meta-Analysis

Introduction

Breast cancer is the most common malignancy in women in Europe and the United States and second leading cause of cancer-related death [1-3]. Approximately 320,000 new cases of breast cancer were diagnosed in the United States in 2018, resulting in 41,000 deaths. Moreover, World Health Organization (WHO, 2018) reported that, breast cancer is the most common cancer diagnosed among women in 154 out of 185 countries of the world and it is the leading cause of cancer-related mortality in over 100 countries [4, 5]. Due to the multiformity of the clinical behaviors, it is difficult to predict and diagnosed only with clinical information. Momenimovahed et al., in a review mentioned a numerous risk factors such as demographic factors (gender, age, blood group), reproductive factors (age of menarche, age of menopause, full-term pregnancy, abortion, ovulatory menstrual cycle, pregnancy characteristics), hormonal (hormonal contraceptive methods, ovulation-stimulating drugs, postmenopausal hormone therapy), hereditary (genetic factors and positive family history of breast cancer),breast related (lesser lactation duration, more breast density, benign breast disorder), lifestyle (obesity, alcohol consumption, smoking, coffee, diet, more physical activity, Vitamin D, duration of sleep), which can increase or decrease the possibility of developing breast cancer [6-8]. According to the estimates of the fraction of cases of breast cancer, approximate 47% of breast cancer cases and 41% of the pathological in the total USA population can be ascribed to well-established risk factors [9, 10].

The unambiguous cause of carcinogenesis has not yet been established, but several risk factors conducive to the development of breast cancer are known [11, 12]. Genome studies of the breast cancer involve a great range of the genome pieces [13]. According to a recent study about the heritability of the breast cancer, the best predictive breast cancer tests incorporating multiple SNPs and family history have an area under the curve (AUC) in the range 0.7 to 0.8. BRCA1 and BRCA2 mutations are inherited in an autosomal dominant fashion [14, 15]. Germline mutations in BRCA1 have been identified in 15-20% of women with a family history of breast cancer and 60-80% of women with a family history of both breast and ovarian cancer [16-18]. Moreover, genome-wide association studies (GWAS) have identified over 80 loci significantly associated with sporadic breast cancer, which these variants explain only 16 % of breast cancer heritability [19, 10].

The X-ray repair cross-complementing 2 (XRCC2) gene, located at 7q36.1, is a member of the RecA/Rad51-related protein family that participates in homologous recombination repair (HRR) to maintain chromosome stability and repair DNA damage [13, 20-23]. Thus, XRCC2 is a functional candidate for involvement in cancer progression [24-26]. Common variants within XRCC2, including Arg188His polymorphism, have been identified as potential cancer susceptibility loci in recent studies, although association results are controversial [27, 28]. The non-synonymous variation (rs3218536) caused due to c.563G>A substitution in exon 3 of XRCC2 gene results in substitution of Arg to His amino acid at codon 188. This polymorphism has been proposed associated with an increased risk of breast cancer [29]. A number of studies investigated the relationship between XRCC2 rs3218536 polymorphism and breast cancer susceptibility, but with conflicting results. Thus, we conducted a comprehensive meta-analysis to explore the possible association between XRCC2 rs3218536 polymorphisms and risk of breast cancer.

Materials and Methods

Search Strategy

The present meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. An elaborate search in the PubMed/MEDLINE, Google Scholar, EMBASE, Cochrane Library database, SciELO, Springer Link, African Journals Online, Academic Search, Bielefeld Academic Search Engine, BioOne, Circumpolar Health Bibliographic Database (CHBD), Cochrane Library, Current Contents, DeepDyve , MedRxiv, Europe PubMed Central (Europe PMC), Indian Citation Index (ICI) , Technology Journal database and Egyptian Knowledge Bank (EKB) Chinese Biomedical Database (CBD), China Biology Medicine disc, China National Knowledge Infrastructure (CNKI), Chinese literature (Wan Fang) and China Science databases was carried out for studies that examined the association of XRCC2 Arg188His polymorphism with susceptibility to breast cancer up to January 2023. Moreover, a manually screened reference of relevant studies to identify additional studies was carried out by two authors. The following medical subject headings (MeSH) terms and keywords were applied to identify the publications: (‘’Breast’’ OR ‘‘Tumor’’ OR ‘’Cancer’’ OR ‘’Neoplasm’’) AND (‘‘X-Ray Repair Cross Complementing 2’’ OR ‘‘DNA repair protein XRCC2’’ OR ‘‘XRCC2’’ OR ‘‘rs3218536’’ OR ‘’Arg188His’’ OR ‘’R188H’’) AND (‘’Gene’’ OR ‘’Genotype’’ OR ‘’Allele’’ OR ‘’Polymorphism’’ OR ‘’Single Nucleotide Polymorphisms’’ OR ‘’SNPs’’ OR ‘’Variant‘’ OR ‘’Variation’’ OR ‘’Single Nucleotide Variations’’ OR ‘’Mutation’’). The search was limited to English language articles. In addition, studies were identified by a manual search of references from the original studies. Articles were screened and assessed by two independent authors on the basis of a standard protocol, and any discrepancies were resolved by discussion until a consensus was reached.

Inclusion Criteria

The inclusion criteria for these studies were as follows: 1) studies examined the association of the XRCC2 Arg188His polymorphism with breast cancer risk; 2) studies with case-control or cohort design published in English; 3) studies reported detailed data for estimation of odds ratio (OR) and 95% confidence interval (CI), as well as available allele genotype frequencies for cases and controls. The exclusion criteria were as follows: 1) Studies did not describe the association of XRCC2 Arg188His polymorphism with breast cancer risk; 2) studies focusing on animals or in vitro; 3) studies that did not provide sufficient data for pooling data; 4) case only studies or no controls; 5) linkage studies and family based studies (twins and sibling); 6) case reports, abstracts, comments, conference abstracts, editorials, reviews, meta-analysis; and 7) duplicated studies or data. After deliberate searching, we reviewed all papers in accordance with the criteria defined above for further analysis.

Data Extraction

Two authors extracted data independently and in duplicate, and the data was verified by third author. The data was compared, and any disagreement was discussed and resolved with consensus. The following data was extracted from each studies: first author name, year of publication, ethnicity (Asian, Caucasian, African and mixed populations), country of origin, genotyping methods, number of cases and controls for each genotype, frequencies of genotypes in cases and controls, minor allele frequency (MAF) in controls, and Hardy-Weinberg equilibrium (HWE) in controls. If selected articles did not reported necessary data the corresponding authors was contacted by email to request the missing data. Minor allele frequencies and Hardy-Weinberg equilibrium in control groups were calculated by using excel-based Court lab-HW calculator software.

Statistical Analysis

The strength of the association of XRCC2 Arg188His (rs3218536) polymorphism with susceptibility to breast cancer was examined by odd ratios (ORs) with 95% confidence intervals (CIs). Z-test was carried out to evaluate the statistical significance of pooled ORs. We used five genetic models, i.e., allele (A vs. G), homozygote (AA vs. GG), heterozygote (AG vs. GG), dominant (AA+AG vs. GG) and recessive (AA vs. AG+GG) to evaluate the association of XRCC2 Arg188His polymorphism with susceptibility to breast cancer. The heterogeneity between studies was assessed with the chi-squared based Q-test. A significant p value (<0.10) was used to indicate heterogeneity among studies. Moreover, the I2 statistic was applied to quantify the proportion of the total heterogeneity were used ( I2 < 25 indicates low heterogeneity, 25% ≤ I2 ≤ 50% indicates moderate heterogeneity, and I2 > 50% indicates large heterogeneity). When P <0 .10 or I2 > 50%, the random-effects model (the DerSimonian-Laird method) was utilized to pool the data. Otherwise, the fixed-effects model (the Mantel-Haenszel method) was used [30-33]. For each study, the distribution of genotypes in controls was calculated for departure from HWE to assess the study quality of genotype data in healthy subjects, in which P<0.05 was considered statistically significant. Stratified analysis by ethnicity was performed to identify the specific effects of heterogeneity. Sensitivity analysis by sequentially omitting the single studies and recounting the pooled ORs and 95% CIs utilized to confirm the stability of our data [34-38]. Moreover, Sensitivity analysis was carried out by excluding those studies deviated from HWE for each polymorphism. The funnel plot was utilized to test the publication bias and Egger’s test (linear regression analysis) was used to check the symmetry of funnel plots. An asymmetric plot and the P value of Egger’s test or Begg’s test less than 0.05 were considered as significant publication bias [11, 39, 40]. The statistical analysis for the current meta-analysis study was performed by using the comprehensive meta-analysis (CMA) version 2.20 software (Biostat, USA). All P-values in the meta-analysis were two-sided, and statistical significance was considered when the P-value was less than 0.05.

Results

Characteristics of the included studies

As shown in Figure 1, our initial search yielded 731 studies, with duplicate studies removed resulting in 419 studies remaining. Among them, 139 studies were excluded based on titles and abstracts. Following the inclusion exclusion criteria 208 studies were excluded. Finally, a total of 17 case-control studies in 16 publications [41-55] with 5694 cases and 6450 controls evaluate the association of XRCC2 Arg188His polymorphism with breast cancer risk. In terms of ethnicity, 16 studies were performed among Caucasians, ten studies among Asians, and eight studies were conducted among mixed populations. Three genotyping methods including TaqMan, PCR-RFLP, and Ligase Detection Reaction were used to genotype the XRCC2 Arg188His polymorphism. Genotype distributions in the controls of two studies for breast cancer were not in agreement with HWE (p < 0.05).

Figure 1.

Figure 1

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Flow Diagram of Selecting Eligible Studies for the Meta-Analysis

Overall and Subgroup Analyses

The pooled association of XRCC2 Arg188His polymorphism with breast cancer susceptibility is summarized in Table 1. Seventeen case-control studies with 5694 cases and 6450 controls f XRCC2 Arg188His polymorphism were analyzed. Our pooled data revealed that there was no a significant between XRCC2 Arg188His polymorphism and breast cancer risk under four genetic models, i.e., allele, homozygote, heterozygote, and dominant. However, there was a significant association between this polymorphism and breast cancer susceptibility under the heterozygote model (AG vs. GG: OR = 0.929, 95% CI = 0.873-0.987, p=0.018) (Figure 2). Stratified analysis by ethnicity revealed that the polymorphism was significantly associated with breast cancer among Caucasians women under the heterozygote contrast (AG vs. GG: OR = 0.920, 95% CI = 0.861-0.980, p=0.009) (Table 2). Considering the limited number of studies among Asian and other descendent population, the stratified analyses was only presented for Caucasians. Moreover, significant association was found positive association after removing HWE violation studies under the recessive contrast (AG vs. GG: OR = 1.635, 95% CI = 1.109-2.413, p=0.013).

Table 1.

Characteristics of Studies Included in the Meta-Analysis of XRCC2 Arg188His Polymorphism and Breast Cancer

First author Country Source of Genotyping Case/Control Cases Controls HWE MAF
(Ethnicity) Controls methods Genotype Allele Genotype Allele
GG AG AA G A GG AG AA G A
Rafii 2002 UK(Caucasian) HB TaqMan 519/398 431 82 6 944 94 351 45 2 747 49 0.669 0.062
Kuschel 2002 UK(Caucasian) PB TaqMan 1725/1811 1476 234 15 3186 264 1538 267 6 3343 279 0.116 0.077
Han 2004 USA(Caucasian) PB TaqMan 952/1237 811 134 7 1756 148 1066 165 6 2297 177 0.887 0.072
Webb 2005 Australia(Caucasian) PB TaqMan 1447/783 1251 187 9 2689 205 675 101 7 1451 115 0.144 0.073
Millikan 2005a USA(Caucasian) PB TaqMan 765/678 744 21 0 1509 21 653 25 0 1331 25 0.624 0.018
Millikan 2005b USA(Caucasian) HB TaqMan 1268/1134 1084 176 8 2344 192 982 145 7 2109 159 0.515 0.07
Garcia-Closas 2006 Poland(Caucasian) PB NA 1981/2280 1763 212 6 3738 224 1983 281 16 4247 313 0.085 0.069
Brooks 2008 USA(Caucasian) NA PCR-RFLP 602/602 515 83 4 1113 91 519 78 5 1116 88 0.283 0.073
Loizidou 2008 Cyprus(Caucasian) PB PCR-RFLP 1108/1177 972 135 1 2079 137 999 177 34 2175 245 <0.001 0.101
Pooley 2008 UK(Caucasian) PB TaqMan 4232/4384 3590 610 32 7790 674 3639 711 34 7989 779 0.91 0.089
Silva 2010 Portugal(Caucasian) HB TaqMan 289/548 243 46 0 532 46 445 103 0 993 103 0.015 0.094
Jakubowska 2010 Poland (Caucasian) NA PCR-RFLP 314/290 272 42 0 586 42 254 36 0 544 36 0.259 0.062
Makowska 2012 Poland(Caucasian) NA PCR-RFLP 790/798 212 374 204 798 782 202 406 190 810 786 0.615 0.492
Ding 2014 China(Asian) PB LDR 606/633 166 280 160 612 600 184 305 144 673 593 0.413 0.468
Smolarz 2014 Poland(Caucasian) PB PCR-RFLP 70/70 12 8 50 32 108 18 40 12 76 64 0.205 0.457
Shadrina 2014 Russia(Caucasian) HB PCR-RFLP 659/656 594 65 0 1253 65 587 67 2 1241 71 0.952 0.054
Qureshi 2014 Pakistan (Asian) PB PCR-RFLP 156/150 131 20 5 282 30 137 12 1 286 14 0.216 0.047
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HB, Hospital based; PB, Population based; PCR-RFLP, Polymerase chain reaction-restriction fragment length polymorphism; LDR: Ligase Detection Reaction HWE, Hardy-Weinberg equilibrium; MAF: minor allele frequency.

Figure 2.

Figure 2

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Forest Plots for Association of X XRCC2 Arg188His (rs3218536) Polymorphism with Susceptibility to Breast Cancer. A) Allele (A vs. G)

Table 2.

Meta-Analysis of the association of XRCC2 Arg188His Polymorphism and Breast Cancer

Genetic Model Type of Model Heterogeneity Odds ratio Publication Bias
I2 (%) PH OR 95% CI POR PBeggs PEggers
Overall
A vs. G Random 79.49 <0.001 1.027 0.904-1.167 0.681 0.387 0.142
AA vs. GG Random 66.5 <0.001 1.125 0.770-1.643 0.542 1 0.868
AG vs. GG Fixed 30.49 0.113 0.929 0.873-0.987 0.018 0.592 0.412
AA+AG vs. GG Random 86.39 <0.001 0.964 0.876-1.061 0.451 0.108 0.016
AA vs. AG+GG Random 78.06 <0.001 1.214 0.798-1.847 0.365 0.742 0.695
Caucasian
A vs. G Random 79.49 <0.001 0.998 0.872-1.143 0.979 0.552 0.216
AA vs. GG Random 69.57 <0.001 1.038 0.647-1.665 0.878 0.631 0.76
AG vs. GG Fixed 29.28 0.137 0.92 0.861-0.980 0.009 1 0.779
AA+AG vs. GG Random 87.52 <0.001 1.098 0.892-1.352 0.377 0.165 0.033
AA vs. AG+GG Random 80.92 <0.001 1.354 0.774-2.371 0.289 0.45 0.856
HWE
A vs. G Random 73.54 <0.001 1.077 0.956-1.213 0.225 0.165 0.033
AA vs. GG Random 54.02 0.01 1.232 0.892-1.701 0.206 0.582 0.555
AG vs. GG Fixed 31.58 0.116 0.943 0.885-1.006 0.074 0.428 0.312
AA+AG vs. GG Random 86.57 <0.001 1.196 0.973-1.471 0.089 0.047 0.009
AA vs. AG+GG Random 73.75 <0.001 1.635 1.109-2.413 0.013 0.854 0.28
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Sensitivity analysis and test of heterogeneity

Sensitivity analysis was conducted to estimate the influence of some individual study on pooled results by calculating the ORs before and after exclusion of a single article from meta-analysis in turn. No outlying study was observed to significantly change the pooled ORs after it was removed. There was a significant heterogeneity was observed XRCC2 Arg188His polymorphism under four genetic models, i.e., allele (I2=79.49%, Ph=<0.001), homozygote (I2=66.50%, Ph=0.042), dominant (I2=86.39%, Ph=<0.001) and recessive (I2=78.06%, Ph=<0.001) in our meta-analysis (Table 2). Therefore, a meta-regression analysis was carried out to observe the source of heterogeneity in the general variables. However, our results showed that the ethnicity and HWE status were not all associated with the large heterogeneity

Publication bias

The Egger’s test and Begg’s funnel plot were used to assess the publication bias of the studies involved in this meta-analysis. The results showed that there was statistically significant evidence of publication bias under dominant genetic model (PBegg’s=0.108, PEggers=0.016, Figure 3 and 4). Therefore, we used the Duval and Tweedie non-parametric ‘‘trim and fill’’ method to the publication bias. The results showed that the current meta-analysis with and without ‘‘trim and fill’’ did not draw different results, indicating that our results were statistically reliable.

Figure 3.

Figure 3

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Begg's Funnel Plot of Publication Bias Test for association of X XRCC2 Arg188His (rs3218536) Polymorphism with Susceptibility to Breast Cancer. A) Allele (A vs. G); B) homozygote (AA vs. GG); C) heterozygote (AG vs. GG)

Figure 2.

Figure 2

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Forest Plots for Association of X XRCC2 Arg188His (rs3218536) Polymorphism with Susceptibility to Breast Cancer. B) homozygote (AA vs. GG); C) heterozygote (AG vs. GG); D) dominant (AA+AG vs. GG); E) and recessive (AA vs. AG+GG).

Figure 3.

Figure 3

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Begg's Funnel Plot of Publication Bias Test for association of X XRCC2 Arg188His (rs3218536) Polymorphism with Susceptibility to Breast Cancer. D) dominant (AA+AG vs. GG); E) and recessive (AA vs. AG+GG).

Figure 4.

Figure 4

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Begg's Funnel Plot of Publication Bias Test before (Blue) and after (Red) Trim-and-Fill Method for association of X XRCC2 Arg188His (rs3218536) Polymorphism with Susceptibility to Breast Cancer under the Dominant Model (AA+AG vs. GG).

Discussion

Although there have been tremendous advances in elucidating genetic risk factors underlying both familial and sporadic breast cancer, much of the genetic contribution to breast cancer etiology remains unknown [56-58]. Several meta-analyses have evaluated the association of XRCC2 Arg188His polymorphism with susceptibility to breast cancer [59, 60, 28, 35, 21]. We performed a meta-analysis of case-control studies to resolve the controversial results reported in previous studies. Seventeen case-control studies with 5694 cases and 6450 controls f XRCC2 Arg188His polymorphism were analyzed. Overall, the polymorphism was found to be significantly associated with breast cancer susceptibility under the heterozygote genetic model. In 2006, García-Closas et al., in a two population-based studies in USA and Poland, and meta-analyses examined the association of 19 polymorphisms at seven genes (XRCC2, XRCC3, BRCA2, ZNF350, BRIP1, XRCC4, LIG4) with susceptibility to breast cancer in two population-based studies in USA (3,368 cases and 2,880 controls) and Poland (1,995 cases and 2,296 controls). Their results showed that the polymorphisms at these genes are unlikely to have a substantial overall association with breast cancer risk; however, weak associations are possible for XRCC3 (T241M and IVS7-14A>G), BRCA2 N372H, and ZNF350 S472P [45]. In 2007, Breast Cancer Association Consortium (BCAC) in a meta-analysis evaluated risk of breast cancer using data from up to 12 studies onADH1C I350V, AURKA F31I, BRCA2 N372H, CASP8 D302H, ERCC2 D312N, IGFBP3 -202 c>a, LIG4 D501D, PGR V660L, SOD2 V16A, TGFB1 L10P, TP53 R72P, XRCC1 R399Q, XRCC2 R188H, XRCC3 T241M, XRCC3 5’ UTR, and XRCC3 IVS7-14 polymorphisms. The pooled data showed a borderline significant association for five polymorphisms (CASP8 D302H, IGFBP3 -202 c>a, PGR V660L, SOD2 V16A, and TGFB1 L10P). however, there was not association with breast cancer risk foe remaining polymorphism [27]. He et al. in a meta-analysis of 45 case-control studies from 26 publications with 30868 cases and 38656 controls have evaluated XRCC2 Arg188His polymorphism relation with cancer risk. Their pooled data showed that there was no a significant association between the XRCC2 Arg188His polymorphism and risk of breast cancer [60]. Yu et al., in a meta-analysis based on 16 studies with 18,341 cases and 19,028 controls revealed that there was no a significant association between XRCC2 Arg188His and risk of breast cancer susceptibility under all five genetic models [28]. Lin et al., genotyped 12 XRCC2 tagging single nucleotide polymorphisms (SNPs) in 1131 breast cancer cases and 1148 healthy subjects from the Sheffield Breast Cancer Study (SBCS), and examined their associations with breast cancer risk and survival by estimating ORs and HRs, and their corresponding 95% CIs. Their results showed a significant association with breast cancer risk in the SBCS dataset was the XRCC2 Arg188His polymorphism [61].

The presence of heterogeneity might distort the results of a meta-analysis [62-64]. Many factors may contribute to the strong heterogeneity among overall analysis. The heterogeneity might be explained by sampling errors and the small number of samples in some studies or chance or real differences in populations or in interactions with other risk factors [65-68]. To explore the sources of heterogeneity in this meta-analysis, a subgroup analysis by ethnicity and HWE was performed. Stratified analyses revealed that the heterogeneity was not significantly reduced or disappeared, which indicated that ethnicity and HWE status could not partly explain the source of heterogeneity. However, these results indicated that the effect of XRCC2 Arg188His may not be modified by ethnicity and HWE.

To our knowledge, this is the most comprehensive meta-analysis which has first investigated the association between the XRCC2 Arg188His polymorphism and susceptibility of breast cancer. However, several limitations should be taken into consideration when explaining the results: First, most of the studies included in this study were carried out among Caucasians and the number of included studies was relatively small in Asians. Therefore, the association XRCC2 Arg188His polymorphism with risk of breast cancer in other ethnicity remained unclear. Thus, to obtain more precise results, further studies with larger sample size and involving different ethnicities especially Asians and African are necessary. Second, only studies published in English were included, so relevant articles published in other languages were possibly missed, and this may have resulted in the relatively small sample size and causing a language bias. Moreover, this meta-analysis enrolled published articles only, while some related articles may remain unpublished, possibly resulting in publication bias. Third, several important confounding factors, such as age, smoking, drinking, family history of breast cancer, environmental exposures and lifestyle, were not considered for stratification analysis because relevant data was insufficient in the primary reports. Finally, this meta-analysis could not address the gene-gene and gene-environmental interactions in the association of XRCC2 Arg188His polymorphism with breast cancer risk. Therefore, future studies that include detailed information on exposures to environmental factors to assess the possible gene-gene and gene-environment interactions in the association between XRCC2 Arg188His polymorphism and risk of breast and ovarian cancer are required.

In summary, our pooled data revealed that the XRCC2 Arg188His (rs3218536) polymorphism was associated with increased risk of breast cancer risk globally and among Caucasian women. Additional large studies with high methodological quality especially among other descendent should be included in future meta-analyses to validate the association between the XRCC2 Arg188His (rs3218536) polymorphism with breast cancer.

Author Contribution Statement

Conceptualization: Seye Alireza Dastgheib, Soheila Sayad, Sepideh Azizi; Data curation: Nazanin Hajizadeh, Fatemeh Asadian; Formal analysis: Seyed Alireza Dastgheib, Hossein Neamatzadeh; Investigation: Kazem Aghili, Maedeh Barahman; Methodology: Mojgan Karimi-Zarchi, Maedeh Barahman; Supervision: Mohammad Manzourolhojeh, Amirmasoud Shiri; Validation: Seye Alireza Dastgheib, Mojgan Karimi-Zarchi; Writing – original draft: Soheila Sayad, Maedeh Barahman; Writing – review & editing: Soheila Sayad, Hossein Neamatzadeh.

Acknowledgements

Funding

The authors did not receive support from any organization for this study.

Conflicts of interest/Competing interests

The authors declare that they have no conflict of interest.

Ethics approval

This article does not contain any studies with human participants or animals performed by any of the authors. An ethical approval was not necessary as this study was a meta-analysis based on previous studies.

Consent to participate

Not applicable for this manuscript.

Data availability

The dataset used and/or analyzed during this study is available from the corresponding author on a reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The dataset used and/or analyzed during this study is available from the corresponding author on a reasonable request.

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