Basic Research

XRCC1 Arg399Gln and Arg194Trp polymorphisms in prostate cancer risk: a meta-analysis

Article metrics


Epidemiological studies have evaluated the association between X-ray repair cross-complementing group 1 gene (XRCC1) Arg399Gln and Arg194Trp polymorphisms and risk of prostate cancer (PCa). However, the results from the published studies on the association between these two XRCC1 polymorphisms and PCa risk are conflicting. To derive a more precise estimation of association between the XRCC1 polymorphisms and risk of PCa, we performed a meta-analysis. A comprehensive search was conducted to identify all case–control studies of XRCC1 polymorphisms and PCa risk. We used odds ratios (ORs) with 95% confidence intervals (CIs) to assess the strength of the association. Overall, we found that both Arg399Gln and Arg194Trp polymorphisms were not significantly associated with PCa risk. However, in stratified analysis by ethnicity, we found that the Arg399Gln polymorphism was significantly associated with PCa risk in Asian population (Gln/Gln vs Arg/Arg: OR=1.46, 95% CI: 1.05–2.03, P=0.03; Gln/Gln vs Arg/Gln+Arg/Arg: OR=1.48, 95% CI: 1.12–1.95, P=0.01). In this meta-analysis, we found that both Arg399Gln and Arg194Trp polymorphisms were not related to overall PCa risk. However, in subgroup analysis we found a suggestion that XRCC1 399Gln allele might be a low-penetrent risk factor for PCa only in Asian men.


Prostate cancer (PCa) is one of the most common malignancies of males in western countries.1 The etiology of PCa is poorly understood, with genetic predisposition and environmental factors likely contributing to the risk.2 The most recognized factors associated with PCa risk are ethnicity, age, and family history of the disease. Besides, studies have reported that both endogenous/exogenous hormonal substances and some gene polymorphisms could be potential risk factors of PCa.3, 4, 5 Recent studies suggest that genetic polymorphisms of genes involved in DNA repair, including the X-ray repair cross-complementing group 1 gene (XRCC1), might affect susceptibility to PCa.6, 7, 8

XRCC1 gene, located on chromosome 19q13.2–13.3, is involved in the repair of DNA base damage and single-strand DNA breaks by binding DNA ligase III at its carboxyl and by binding DNA polymerase and poly (adenosine diphosphate ribose) polymerase at the site of damaged DNA.9 Deletion of the XRCC1 in mice results in an embryonic lethal phenotype.10 Chinese hamster ovary cell lines with mutations in the XRCC1 have shown a reduced ability to repair single strand breaks in DNA and concomitant cellular hypersensitivity to ionizing radiation and alkylating agents.11

XRCC1 has an essential role in removing endogenous and exogenous DNA damage. Arg399Gln (exon 10, base 28152 G to A, arginine to glutamine) and Arg194Trp (exon 6, base 26304 C to T, arginine to tryptophane) are two common polymorphisms of XRCC1. These two polymorphisms, involving an amino acid change at evolutionarily conserved regions, could alter the XRCC1 function. The XRCC1 Arg399Gln variant is located at the COOH-terminal side of the poly (adenosine diphosphate ribose) polymerase-interacting domain within a relatively non-conserved region between conserved residues of the BRCT (first described at the carboxyl terminus of the breast cancer protein breast cancer 1) domain, which is indicated as a protein–protein interaction module in many proteins involved in DNA repair.12 In addition, the variant may be related with several phenotypic alterations, including higher levels of sister chromatid exchange,13 aflatoxin B1–DNA adducts, glycophorin A mutations14 and polyphenol DNA adducts.15 The Arg194Trp polymorphism, which occurs in nuclear antigen-binding region of the proliferating cell, is suggested to enhance individual DNA repair capability.16 So far, many studies have focused on the associations between PCa risk and DNA repair pathway gene polymorphisms. However, the results of these studies6, 7, 8, 17, 18, 19, 20, 21, 22, 23, 24, 25 are conflicting and inconclusive. Therefore, it is highly necessary to perform a quantitative and systematic study with rigorous methods. In the present study, a meta-analysis was performed from all eligible studies to address the association between XRCC1 polymorphisms (Arg399Gln and Arg194Trp) and PCa risk.

Materials and methods

Publication search

PubMed and EMBASE were searched (the last search was updated in March 2011) using the search terms: ‘XRCC1 or X-ray repair cross-complementing group 1’, ‘polymorphism’, and ‘prostate cancer or prostate neoplasm’. All published papers in English language with available full text matching the eligible criteria were retrieved. In addition, we also checked the references of relevant reviews and eligible articles that our search retrieved. Two investigators searched the literature and extracted data independently.

Inclusion, exclusion criteria and data abstraction

For inclusion in this meta-analysis, the identified articles had to provide information on the following: (1) evaluation of XRCC1 Arg399Gln and/or Arg194Trp polymorphisms and PCa risk, (2) using a case–control design and (3) sufficient data for examining an odds ratio (OR) with 95% confidence interval (CI); (4) the most recent and/or the largest study with extractable data should be included concerning studies with overlapping patients and the controls. Major reasons for the exclusion of studies were as follows: (1) no control cases; (2) no usable data reported; (3) duplicates. For each of the eligible case–control studies, the following data were collected: first author, publishing year, ethnicity of subjects, source of controls, numbers of genotyped cases and controls, genotyping methods. Different ethnic descents were categorized as Caucasian, Asian and African. For studies21, 23 including different ethnic populations, the data were collected separately whenever possible and served as independent studies. Because one study25 presented the data for genotypes as ‘Arg/Arg, Arg/Gln+ Gln/Gln’ without data for all three genotypes, we can only calculate the OR for the dominant model.

Statistical analysis

The strength of the association between the XRCC1 Arg399Gln and Arg194Trp polymorphisms and PCa risk was measured by ORs with 95% CIs. For XRCC1 Arg399Gln polymorphism, we first examined the risk of the variant genotypes Gln/Gln or Gln/Arg on PCa compared with the wild-type Arg/Arg homozygote. Then, the risk of (Gln/Gln+Gln/Arg) vs Arg/Arg and Gln/Gln vs (Gln/Arg+Arg/Arg) for PCa was evaluated in dominant and recessive models. For XRCC1 Arg194Trp polymorphism, we evaluated the same effects. Stratified and meta-regression analyses were performed by ethnicities.

Heterogeneity assumption was checked by a χ2-based Q-test.26 A P-value of more than 0.05 for the Q-test indicated a lack of heterogeneity among the studies, so the summary OR estimate of each study was calculated by the fixed-effects model (the Mantel–Haenszel method). Otherwise, the random effects model (DerSimonian and Laird method) was used.27, 28 The significance of the pooled OR was determined by the Z-test and P<0.05 was considered as statistically significant. To evaluate the covariate effects, subgroup and meta-regression analyses were conducted based on different ethnicities. Monte Carlo permutation test was performed (iterations=20 000) to adjust the P-value for multiple comparisons based on the work by Higgins et al.29 Knapp and Hartung adjustment was also used to control the type I error rate.30

To investigate whether publication bias might affect the validity of the estimates, funnel plot was constructed. An asymmetric plot suggests a possible publication bias. Funnel plot asymmetry was assessed by the method of Egger's linear regression test, a linear regression approach to measure funnel plot asymmetry on the natural logarithm scale of OR. The significance of the intercept was determined by the t-test suggested by Egger (P<0.05 was considered representative of statistically significant publication bias). Hardy–Weinberg equilibrium in the control group was tested using the Pearson's χ2-test for goodness of fit; P<0.05 was considered significant.

All statistical analyses were performed with the Stata software (version 9.0; StataCorp LP, College Station, TX, USA) using two-sided P-values.


Eligible studies

Fourteen studies were included based on the search criteria for PCa susceptibility related to the XRCC1 Arg399Gln and Arg194Trp polymorphisms. Study characteristics were summarized in Supplementary Table 1. There were five studies of subjects of Asian descent, seven studies of subjects of Caucasian descent and two studies of subjects of African descent. Among these studies, seven studies have investigated only Arg399Gln polymorphism, whereas seven studies included Arg399Gln and Arg194Trp polymorphisms. Therefore, there were 14 studies with 3782 cases and 3775 controls concerning the Arg399Gln polymorphism, and seven studies with 2153 cases and 2219 controls concerning the Arg194Trp polymorphism.

All the studies used frequency-matched controls to the cases by age, sex or ethnicity. A classical PCR-restriction fragment length polymorphism assay was conducted in nine studies. The amplification refractory mutation system, Applied Biosystems (ABI, Foster City, CA, USA) SNPlex (ABS) Genotyping system, matrix-assisted laser desorption ionization time of flight mass spectrometry and homogeneous MassExtend (HM) multiplex assays were used in the remaining five studies. The genotype distributions among the controls of all studies followed Hardy–Weinberg equilibrium except for one study23 in African population for the Arg194Trp polymorphism.


In the overall Arg399Gln analysis, the Gln/Gln and Arg/Gln genotypes were not significantly associated with increased risk of PCa (Gln/Gln vs Arg/Arg: OR=1.04, 95% CI: 0.90–1.22, P=0.58; Arg/Gln vs Arg/Arg: OR=0.93, 95% CI: 0.84–1.03, P=0.17; Table 1, Figure 1). A similar negative association was maintained in dominant and recessive models (Gln/Gln+Arg/Gln vs Arg/Arg: OR=0.99, 95% CI: 0.90–1.09, P=0.84 and Gln/Gln vs Arg/Gln+Arg/Arg: OR=1.11, 95% CI: 0.96–1.27, P=0.16; Table 1). Similarly, the XRCC1 Arg194Trp polymorphism was not found significantly associated with increased risk of PCa (date were shown in Supplementary Table 2, Figure 2).

Table 1 Stratified analysis of XRCC1 Arg399Gln polymorphism with PCa risk
Figure 1

Forest plot of prostate cancer risk associated with XRCC1 Arg399Gln polymorphism (for Gln/Gln vs Arg/Arg). The squares and horizontal lines correspond to the study-specific odds ratio (OR) and 95% confidence interval (CI). The area of the squares reflects the weight (inverse of the variance). The diamond represents the summary OR and 95% CI.

Figure 2

Forest plot of prostate cancer risk associated with XRCC1 Arg194Trp polymorphism (for Trp/Trp vs Arg/Arg). The squares and horizontal lines correspond to the study-specific odds ratio (OR) and 95% confidence interval (CI). The area of the squares reflects the weight (inverse of the variance). The diamond represents the summary OR and 95% CI.

In the stratified analysis by ethnicity for Arg399Gln polymorphism, our results show that the Gln/Gln genotype was significantly associated with increased risk of PCa in Asian population (Gln/Gln vs Arg/Arg: OR=1.46, 95% CI:1.05–2.03, P=0.03; Table 1, Figure 1). This similar positive association was maintained in recessive model (Gln/Gln vs Arg/Gln+Arg/Arg: OR=1.48, 95% CI: 1.12–1.95, P=0.01; Table 1, Figure 1). However, in the stratified analysis for Arg194Trp polymorphism, we did not find any positive relationship between the Arg194Trp polymorphism and PCa risk (date were shown in Supplementary Table 2, Figure 2). After P-value correction for multiple testing, there remains some weak evidences that the association was positive considering as extreme or more extreme as the observed condition (Gln/Gln vs Arg/Arg: corrected P=0.060; Gln/Gln vs Arg/Gln+Arg/Arg, corrected P=0.066).31

As well as multiple testing, we have also applied Knapp and Hartung adjustment to explore the association. The results further confirm the conclusion that genetic variable Arg399Gln associated with PCa risk in Asian population (Gln/Gln vs Arg/Arg: P=0.046; Gln/Gln vs Arg/Gln+Arg/Arg, P=0.040).

Test of heterogeneity

For Arg399Gln polymorphism, the heterogeneity was reckoned between each of the studies using the Q-test: homozygote comparison (Gln/Gln vs Arg/Arg: Pheterogeneity=0.37); heterozygote comparison (Gln/Arg vs Arg/Arg: Pheterogeneity=0.09); dominant model comparison (Gln/Gln+ Gln/Arg vs Arg/Arg: Pheterogeneity=0.05); and recessive model comparison (Gln/Gln vs Gln/Arg+ Arg/Arg: Pheterogeneity=0.34). For Arg194Trp polymorphism, the heterogeneity was reckoned between each of the studies using the Q-test: homozygote comparison (Trp/Trp vs Arg/Arg: Pheterogeneity=0.21); heterozygote comparison (Gln/Arg vs Arg/Arg: Pheterogeneity=0.01); dominant model comparison (Gln/Gln+ Gln/Arg vs Arg/Arg: Pheterogeneity=0.01) and recessive model comparison (Gln/Gln vs Gln/Arg+ Arg/Arg: Pheterogeneity=0.41).

Sensitivity analysis

In the sensitivity analysis, the influence of each study on the pooled OR was examined by repeating the meta-analysis while omitting each study, one at a time. This procedure confirmed the stability of our overall result. In addition, when excluding one study23 in African population for the Arg194Trp polymorphism that did not follow Hardy-Weinberg equilibrium, the estimated pooled OR still did not significantly change at all (data not shown).

Publication bias

Begg's funnel plot and Egger's test were conducted to assess the publication bias of the literature. The shape of funnel plots did not reveal any evidence of funnel plot asymmetry. The statistical results still did not show publication bias (for Arg399Gln polymorphism: P=0.13 for Gln/Gln vs Arg/Arg, P=0.72 for Gln/Arg vs Arg/Arg, P=0.75 for dominant model and P=0.09 for recessive model; for Arg194Trp polymorphism: P=0.22 for Trp/Trp vs Arg/Arg, P=0.60 for Trp/Arg vs Arg/Arg, P=0.69 for dominant model and P=0.20 for recessive model).


We conducted a systematic search of the literatures and combined the available results in this meta-analysis, which is a useful strategy for elucidating genetic factors in cancer.32, 33, 34 XRCC1, which is involved in the DNA repair, has been studied extensively about the relationship with different cancers, such as urothelial bladder cancer,35 breast cancer,36 PCa25, 37 and so on. Previous results of the studies on the relationship between XRCC1 polymorphisms and PCa risk were contradictory. These inconsistent results are possibly because of a small effect of the polymorphism on PCa risk or the relatively low statistical power of the published studies. Hence, the meta-analysis was needed to provide a quantitative approach for combining the results of various studies with the same topic, and for estimating and explaining their diversity.

The present meta-analysis, including 3782 cases and 3775 controls concerning the Arg399Gln polymorphism and 2153 cases and 2219 controls concerning the Arg194Trp polymorphism, explored the association between two potentially functional polymorphisms in the XRCC1 gene and PCa risk. Overall, we did not find that the variant genotypes of the XRCC1 Arg399Gln and Arg194Trp polymorphisms were significantly associated with PCa risk. However, in stratified analysis by ethnicity, we found that the variant genotype of the XRCC1 Arg399Gln polymorphism was significantly associated with the risk of PCa only in Asian population.

To reduce the type 1 error resulted by several covariates, we performed permutation method for multiple testing rather than Bonferroni method considering its limitations.38 We could still detect the low-penetrant risk in Asian population even after stimulation for 20 000 times as extreme or more extreme condition.31 This could be interpreted as describing the degree of ‘surprise’ one might have about the observed result for Asian population, considering all the covariates are being examined.29

Moreover, permutation method sometimes resulted in tests with very low power.39, 40 Therefore, we have also performed Knapp and Hartung adjustment (supported by Higgins et al.29 as well) to correct P-value. Knapp and Hartung method could effectively decrease the false-positive rates in meta-regression and adjust the coverage probability of the CIs to be much closer to reality. It is based on t distribution in place of the standard normal distribution when calculating P-values and CIs. Even though the Knapp and Hartung modification will usually lead to more conservative P-values,41 our modified results (P<0.05) credibly support the genetic variant association available in Asian population. It is consistent with the evidence from multiple testing or stratified analysis.

It was suggested that the XRCC1 Arg399Gln polymorphism was also associated with other cancers in Asian population, which included colorectal cancer,42 lung cancer,43 basal cell carcinoma44 and so on. The study by Wang et al.42 indicated that the XRCC1 399Gln allele was associated with a significantly increased rectal cancer risk among men. Besides, XRCC1 Arg399Gln polymorphism may alter the risk of lung cancer in women nonsmokers in China.43 Kang et al.44 found that the XRCC1 Arg399Gln polymorphism was associated with an increased risk of basal cell carcinoma.44 Given the important roles of XRCC1 gene, it is biologically plausible that XRCC1 polymorphism may modulate the risk of PCa. The XRCC1 Arg399Gln polymorphism was shown to be correlated with DNA repair activity and associated with susceptibility to various cancers, including PCa.18, 21 The presence of the Gln/Gln genotype of XRCC1 has been shown to be associated with tumor-suppressor gene TP53 mutations,45 elevated levels of sister chromatid exchanges15 and higher level of DNA adducts.14 Thus, it may alter PCa susceptibility, especially in Asian population.

We found that the variant genotype of the XRCC1 Arg399Gln polymorphism, in Caucasian population, was not associated with significant increase in PCa risk. This result was consistent with previous studies.17, 19, 24 Although the XRCC1 Arg194Trp polymorphism may be associated with DNA repair activity, no significant association of the variant genotype with PCa risk was found in Caucasian and Asian populations, suggesting the influence of the genetic variant may be masked by the presence of other as-yet unidentified causal genes involved in PCa. Besides, a possible role of ethnic differences in genetic backgrounds and the environment they lived in may affect this result. Other factors such as selection bias, different matching criteria may also have a role.

The DNA repair gene XRCC1 codes for a scaffolding protein physically associated with DNA polymerase β, DNA ligase III, human AP endonuclease, polynucleotide kinase and poly (adenosine diphosphate ribose) polymerase, which functions in a complex to facilitate base excision repair and single-strand break-repair processes. The base excision repair pathway mainly removes non-bulky base adducts produced by methylation, oxidation or reduction by ionizing radiation or oxidative damage. Several studies46, 47, 48 suggested a possible interaction between XRCC1 polymorphisms and environmental factors on cancer risk. The gene–environment interactions have been of great interest to evaluate the exact roles of genetic polymorphisms. However, lacking of the original data of these studies limited our further evaluation of potential gene–environment interactions.

Some limitations of this meta-analysis should be addressed. First, lacking of the original data limited our further evaluation of potential interactions because the interactions among gene–gene, gene–environment, and even different polymorphic loci of the same gene may modulate PCa risk. Second, our result was based on unadjusted estimates, although a more precise analysis should be conducted if more detailed data were available, which would allow for an adjusted estimate by other factors such as age, smoking and alcohol. Lacking of the information for the data analysis may cause serious confounding bias. Third, there was not enough data on African population in this meta-analysis, especially for Arg194Trp polymorphism.

In summary, this meta-analysis provided evidence of the association between the XRCC1 Arg399Gln and Arg194Trp polymorphisms and PCa risk, supporting the hypothesis that both Arg399Gln and Arg194Trp polymorphisms were not related to overall PCa risk. In subgroup analysis, the significant association of XRCC1 399Gln allele with PCa risk was found only in Asian men. However, more sophisticated gene–gene and gene–environment interactions should also be considered in future analysis, which should lead to better, comprehensive understanding of the association between the XRCC1 polymorphisms and PCa risk.


  1. 1

    Jemal A, Siegel R, Ward E, Murray T, Xu J, Smigal C et al. Cancer statistics, 2006. CA Cancer J Clin 2006; 56: 106–130.

  2. 2

    Schaid DJ . The complex genetic epidemiology of prostate cancer. Hum Mol Genet 2004; 13 (Spec No 1): R103–R121.

  3. 3

    Bosland MC . The role of steroid hormones in prostate carcinogenesis. J Natl Cancer Inst Monogr 2000; 27: 39–66.

  4. 4

    Xu B, Xu Z, Cheng G, Min ZC, Mi Y, Zhang ZZ et al. Association between polymorphisms of TP53 and MDM2 and prostate cancer risk in southern Chinese. Cancer Genet Cytogenet 2010; 202: 76–81.

  5. 5

    Lichtenstein P, Holm NV, Verkasalo PK, Iliadou A, Kaprio J, Koskenvuo M et al. Environmental and heritable factors in the causation of cancer--analyses of cohorts of twins from Sweden, Denmark, and Finland. N Engl J Med 2000; 343: 78–85.

  6. 6

    Mandal RK, Gangwar R, Mandhani A, Mittal RD . DNA repair gene X-ray repair cross-complementing group 1 and xeroderma pigmentosum group D polymorphisms and risk of prostate cancer: a study from North India. DNA Cell Biol 2010; 29: 183–190.

  7. 7

    Hamano T, MaTsui H, Ohtake N, Nakata S, Suzuki K . Polymorphism of DNA repair genes, XRCC1 and XRCC3, and susceptibility to familial prostate cancer in a Iapanese population. Asia Pac J Cli Oncol 2008; 4: 21–26.

  8. 8

    Xu Z, Hua LX, Qian LX, Yang J, Wang XR, Zhang W et al. Relationship between XRCC1 polymorphisms and susceptibility to prostate cancer in men from Han, Southern China. Asian J Androl 2007; 9: 331–338.

  9. 9

    Caldecott KW, Aoufouchi S, Johnson P, Shall S . XRCC1 polypeptide interacts with DNA polymerase beta and possibly poly (ADP-ribose) polymerase, and DNA ligase III is a novel molecular ‘nick-sensor’ in vitro. Nucleic Acids Res 1996; 24: 4387–4394.

  10. 10

    Tebbs RS, Flannery ML, Meneses JJ, Hartmann A, Tucker JD, Thompson LH et al. Requirement for the Xrcc1 DNA base excision repair gene during early mouse development. Dev Biol 1999; 208: 513–529.

  11. 11

    Shen MR, Zdzienicka MZ, Mohrenweiser H, Thompson LH, Thelen MP . Mutations in hamster single-strand break repair gene XRCC1 causing defective DNA repair. Nucleic Acids Res 1998; 26: 1032–1037.

  12. 12

    Masson M, Niedergang C, Schreiber V, Muller S, Menissier-de Murcia J, de Murcia G . XRCC1 is specifically associated with poly(ADP-ribose) polymerase and negatively regulates its activity following DNA damage. Mol Cell Biol 1998; 18: 3563–3571.

  13. 13

    Abdel-Rahman SZ, El-Zein RA . The 399Gln polymorphism in the DNA repair gene XRCC1 modulates the genotoxic response induced in human lymphocytes by the tobacco-specific nitrosamine NNK. Cancer Lett 2000; 159: 63–71.

  14. 14

    Lunn RM, Langlois RG, Hsieh LL, Thompson CL, Bell DA . XRCC1 polymorphisms: effects on aflatoxin B1-DNA adducts and glycophorin A variant frequency. Cancer Res 1999; 59: 2557–2561.

  15. 15

    Duell EJ, Wiencke JK, Cheng TJ, Varkonyi A, Zuo ZF, Ashok TD et al. Polymorphisms in the DNA repair genes XRCC1 and ERCC2 and biomarkers of DNA damage in human blood mononuclear cells. Carcinogenesis 2000; 21: 965–971.

  16. 16

    Fan J, Otterlei M, Wong HK, Tomkinson AE, Wilson III DM . XRCC1 co-localizes and physically interacts with PCNA. Nucleic Acids Res 2004; 32: 2193–2201.

  17. 17

    van Gils CH, Bostick RM, Stern MC, Taylor JA . Differences in base excision repair capacity may modulate the effect of dietary antioxidant intake on prostate cancer risk: an example of polymorphisms in the XRCC1 gene. Cancer Epidemiol Biomarkers Prev 2002; 11: 1279–1284.

  18. 18

    Rybicki BA, Conti DV, Moreira A, Cicek M, Casey G, Witte JS . DNA repair gene XRCC1 and XPD polymorphisms and risk of prostate cancer. Cancer Epidemiol Biomarkers Prev 2004; 13: 23–29.

  19. 19

    Hirata H, Hinoda Y, Tanaka Y, Okayama N, Suehiro Y, Kawamoto K et al. Polymorphisms of DNA repair genes are risk factors for prostate cancer. Eur J Cancer 2007; 43: 231–237.

  20. 20

    Ritchey JD, Huang WY, Chokkalingam AP, Gao YT, Deng J, Levine P et al. Genetic variants of DNA repair genes and prostate cancer: a population-based study. Cancer Epidemiol Biomarkers Prev 2005; 14: 1703–1709.

  21. 21

    Chen L, Ambrosone CB, Lee J, Sellers TA, Pow-Sang J, Park JY . Association between polymorphisms in the DNA repair genes XRCC1 and APE1, and the risk of prostate cancer in white and black Americans. J Urol 2006; 175: 108–112; discussion 112.

  22. 22

    Kuasne H, Rodrigues IS, Losi-Guembarovski R, Reis MB, Fuganti PE, Gregorio EP et al. Base excision repair genes XRCC1 and APEX1 and the risk for prostate cancer. Mol Biol Rep 2011; 38: 1585–1591.

  23. 23

    Agalliu I, Kwon EM, Salinas CA, Koopmeiners JS, Ostrander EA, Stanford JL . Genetic variation in DNA repair genes and prostate cancer risk: results from a population-based study. Cancer Causes Control 2010; 21: 289–300.

  24. 24

    Dhillon VS, Yeoh E, Fenech M . DNA repair gene polymorphisms and prostate cancer risk in South Australia-results of a pilot study. Urol Oncol 2009; e-pub ahead of print 12 November 2011; doi:10.1016/j.urolonc.2009.08.013.

  25. 25

    Zhang J, Dhakal IB, Greene G, Lang NP, Kadlubar FF . Polymorphisms in hOGG1 and XRCC1 and risk of prostate cancer: effects modified by plasma antioxidants. Urology 2010; 75: 779–785.

  26. 26

    Lau J, Ioannidis JP, Schmid CH . Quantitative synthesis in systematic reviews. Ann Intern Med 1997; 127: 820–826.

  27. 27

    DerSimonian R, Laird N . Meta-analysis in clinical trials. Control Clin Trials 1986; 7: 177–188.

  28. 28

    Mantel N, Haenszel W . Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst 1959; 22: 719–748.

  29. 29

    Higgins JP, Thompson SG . Controlling the risk of spurious findings from meta-regression. Stat Med 2004; 23: 1663–1682.

  30. 30

    Knapp G, Hartung J . Improved tests for a random effects meta-regression with a single covariate. Stat Med 2003; 22: 2693–2710.

  31. 31

    Harbord RM, Higgins JP . Meta-regression in Stata. Stata J 2008; 8: 493–519.

  32. 32

    Barba M, Yang L, Schunemann HJ, Sperati F, Grioni S, Stranges S et al. Urinary estrogen metabolites and prostate cancer: a case-control study and meta-analysis. J Exp Clin Cancer Res 2009; 28: 135.

  33. 33

    Shao N, Xu B, Mi YY, Hua LX . IL-10 polymorphisms and prostate cancer risk: a meta-analysis. Prostate Cancer Prostatic Dis 2011; 14: 129–135.

  34. 34

    Xu B, Tong N, Chen SQ, Hua LX, Wang ZJ, Zhang ZD et al. FGFR4 Gly388Arg polymorphism contributes to prostate cancer development and progression: a meta-analysis of 2618 cases and 2305 controls. BMC Cancer 2011; 11: 84.

  35. 35

    Lao T, Gu W, Huang Q . A meta-analysis on XRCC1 R399Q and R194W polymorphisms, smoking and bladder cancer risk. Mutagenesis 2008; 23: 523–532.

  36. 36

    Huang Y, Li L, Yu L . XRCC1 Arg399Gln, Arg194Trp and Arg280His polymorphisms in breast cancer risk: a meta-analysis. Mutagenesis 2009; 24: 331–339.

  37. 37

    Geng J, Zhang Q, Zhu C, Wang J, Chen L . XRCC1 genetic polymorphism Arg399Gln and prostate cancer risk: a meta-analysis. Urology 2009; 74: 648–653.

  38. 38

    Hulley HB, Cummings SR, Browner WS, Grady DG, Newman TB . Designing Clinical Research, 3rd edn. Lippincott Williams & Wikins: Philadelphia, 2007.

  39. 39

    Anderson MJ, Braak CT . Permutation tests for multi-factorial analysis of variance. J Stat Comput Simulation 2003; 73: 85–113.

  40. 40

    Gonzalea L, Manly BF . Analysis of variance by randomization with small data sets. Environmetrics 1998; 9: 53–65.

  41. 41

    Viechtbauer W . Conducting Meta-Analysis in R with the metafor Package. J Stat Software 2010; 36: 1–48.

  42. 42

    Wang J, Zhao Y, Jiang J, Gajalakshmi V, Kuriki K, Nakamura S et al. Polymorphisms in DNA repair genes XRCC1, XRCC3 and XPD, and colorectal cancer risk: a case-control study in an Indian population. J Cancer Res Clin Oncol 2010; 136: 1517–1525.

  43. 43

    Li M, Yin Z, Guan P, Li X, Cui Z, Zhang J et al. XRCC1 polymorphisms, cooking oil fume and lung cancer in Chinese women nonsmokers. Lung Cancer 2008; 62: 145–151.

  44. 44

    Kang SY, Lee KG, Lee W, Shim JY, Ji SI, Chung KW et al. Polymorphisms in the DNA repair gene XRCC1 associated with basal cell carcinoma and squamous cell carcinoma of the skin in a Korean population. Cancer Sci 2007; 98: 716–720.

  45. 45

    Casse C, Hu YC, Ahrendt SA . The XRCC1 codon 399 Gln allele is associated with adenine to guanine p53 mutations in non-small cell lung cancer. Mutat Res 2003; 528: 19–27.

  46. 46

    Curtin K, Samowitz WS, Wolff RK, Ulrich CM, Caan BJ, Potter JD et al. Assessing tumor mutations to gain insight into base excision repair sequence polymorphisms and smoking in colon cancer. Cancer Epidemiol Biomarkers Prev 2009; 18: 3384–3388.

  47. 47

    Weng Z, Lu Y, Weng H, Morimoto K . Effects of the XRCC1 gene-environment interactions on DNA damage in healthy Japanese workers. Environ Mol Mutagen 2008; 49: 708–719.

  48. 48

    Huang M, Dinney CP, Lin X, Lin J, Grossman HB, Wu X . High-order interactions among genetic variants in DNA base excision repair pathway genes and smoking in bladder cancer susceptibility. Cancer Epidemiol Biomarkers Prev 2007; 16: 84–91.

Download references


This work benefited from the helpful comments of two anonymous reviewers.

Author information

Correspondence to Z Xu.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Additional information

Supplementary Information accompanies the paper on the Prostate Cancer and Prostatic Diseases website

Supplementary information

Rights and permissions

Reprints and Permissions

About this article


  • XRCC1
  • polymorphism
  • meta-analysis

Further reading