Skip to main content
Download PDF

Association between XRCC1polymorphism 399 G->A and glioma among Caucasians: a systematic review and meta-analysis

BMC Medical Genetics volume 13, Article number: 97 (2012) Cite this article

Abstract

Background

The x-ray cross complementing group 1 gene (XRCC1) is crucial to proper repair of DNA damage such as single-strand DNA breaks. A non-synonymous polymorphism in XRCC1, 399 G → A, has been shown to reduce effectiveness of such DNA repair and has been associated with the risk of certain cancers. The known risk for glioma from high dose ionizing radiation makes associations between this polymorphism and glioma of particular interest.

Methods

A systematic literature review and meta-analysis was conducted to explore the association between XRCC1 399 G → A and glioma. Subgroup analyses by grade, gender, genotyping method, country in which study was conducted, and study size were conducted when data were available and validity of the results were assessed by influence analyses and exploration of potential publication bias.

Results

Six studies were eligible for meta-analysis including data on 2,362 Caucasian glioma cases and 3,085 Caucasian controls. Pooled analysis yielded a significant association between the variant of interest and risk of glioma (OR = 1.17, 95% CI: 1.05-1.30) which was found to be disproportionately driven by a single study. Exclusion of this study, in an influence analysis, produced no statistically significant evidence of association with glioma (OR = 1.10, 95% CI: 0.98-1.23), and no evidence of publication bias.

Conclusions

This meta-analysis does not suggest a major role of the XRCC1 399 G → A polymorphism in influencing risk of glioma among Caucasians. Future studies should report data separately for glioma subtypes to permit stratified analyses for Grade III and Grade IV glioma and examine other polymorphisms in this gene.

Peer Review reports

Background

Malignant gliomas account for approximately 70% of adult malignant primary brain tumors in the United States and are associated with median survival of only 12 to 15 months among patients with glioblastoma, the most common type of glioma [1, 2]. Research on the etiology of glioma over the past several decades has yielded few consistent findings; the only established environmental risk factor is exposure to therapeutic or high-dose ionizing radiation [3–5]. Studies of genetic susceptibility to glioma have been similarly inconsistent, although three recent genome-wide association studies (GWAS) have replicated findings at six susceptibility loci in TERT, CCDC26, CDKN2A-CDKN2B, RTEL1, PHLDB1, and EGFR[6–8]. Despite this progress, the majority of the risk of malignant gliomas remains unexplained.

Given the established link between ionizing radiation and glioma, it has been hypothesized that genetic variants of the DNA repair pathway may affect susceptibility to the disease. Specifically, x-ray cross complementing group 1 (XRCC1) is an important component of the base excision repair system, which is the predominant DNA repair pathway for small base lesions resulting from oxidation and alkylation damage [9]. The 33 kb XRCC1 gene, located on chromosome 19q13.2-13.3, encodes a scaffolding protein which coordinates numerous protein-protein interactions, including with DNA ligase III and DNA polymerase at the site of damage [10, 11].

Although over 300 single nucleotide polymorphisms (SNPs) have been identified in XRCC1 (http://www.ncbi.nlm.nih.gov/projects/SNP), the most extensively studied has been rs25487 in codon 399 of exon 10, a non-synonymous G → A polymorphism changing arginine to glutamine. The frequency of the putative risk allele A has been shown to be 0.366, 0.274, and 0.111 in European American, east Asian, and African HapMap populations, respectively [12]. It has been suggested that this amino acid change results in deficient DNA repair, rendering carriers of the variant allele more susceptible to damage by environmental carcinogens [13, 14]. The 399 G → A polymorphism has been shown to be associated with risk for numerous cancers including lung and prostate cancer among Asians, as well as breast cancer in both Asian and Caucasian populations [15–20]. As studies of the association of 399 G → A with the risk of glioma have been inconsistent, a systematic review and meta-analysis of such studies was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [21]. The aim was to elucidate the role of the polymorphism in glioma and to explore sources of heterogeneity among the identified studies.

Methods

Search strategy

A systematic literature search of the PubMed, EMBASE and Scopus databases was performed on October 25th, 2011 using search terminology [(XRCC1 OR x-ray cross complementing group 1) AND (glioma OR brain tumor)]. This was supplemented by a query of the HuGE Literature Finder based on a search of (glioma OR brain tumor) for gene XRCC1. Reference lists of identified studies were reviewed in order to identify additional relevant articles.

Study selection

Included studies were required to meet all of the following conditions: 1) study employed a case–control design of human subjects; 2) outcome was microscopically-confirmed malignant glioma; and 3) data were presented on the genotype counts of cases and controls for the 399 G → A polymorphism in XRCC1. No language restriction was implemented, and corresponding authors were contacted in an attempt to obtain unreported genotype counts if studies were otherwise eligible. Genotype counts for cases and controls for the study by Liu et al. [22] were provided.

Data extraction

The following data were extracted from each eligible article: last name of first author, date of publication, country in which study was performed, ethnicity of subjects, genotype counts of cases and controls, genotyping method and quality control, glioma subtype (grade III and/or grade IV), and age and gender composition of subjects. There was no blinding to the names of authors, journals, institutions, or funding sources.

Statistical analysis

Deviation from Hardy-Weinberg equilibrium in control subjects was tested using a chi-squared goodness-of-fit test (P < 0.05 was considered significant). Assuming a fixed effects model, the inverse variance method was used to estimate separate genotype-specific summary odds ratios (OR) and 95% confidence intervals for the association between 399 G → A and risk of glioma. A fixed effects model was used due to the exhaustive nature of the literature review and a detailed exploration of possible sources of heterogeneity, as opposed to a random effects model which is not a solution for heterogeneity and assumes that identified studies are a subset of a larger pool of studies. Three pooled ORs were calculated: AA versus GG (OR1), AG versus GG (OR2), and AA versus AG (OR3). The resulting ORs were used to determine the most appropriate genetic model using a previously described approach [23]:

  1. a)

    If OR1 = OR3 ≠ 1 and OR2 = 1, a recessive model is suggested

  2. b)

    If OR1 = OR2 ≠ 1 and OR3 = 1, a dominant model is suggested

  3. c)

    If OR2 = 1/OR3 ≠ 1 and OR1 = 1, an over dominant model is suggested

  4. d)

    If OR1 > OR2 > 1 and OR1 > OR3 > 1 (or OR1 < OR2 < 1 and OR1 < OR3 < 1), a co-dominant model is suggested

After determination of the most appropriate genetic model, heterogeneity across studies was assessed using the I 2 statistic [24]. To explore potential sources of heterogeneity, subgroup analyses were conducted according to gender, glioma subtype (Grade III vs. Grade IV), country (US vs. other), study size (<500 vs. ≥500), and genotyping method (PCR-RFLP vs. other). Influence analyses were also conducted, in which pooled estimates were calculated after omission of one study at a time to identify studies excessively influencing the summary estimate. Finally, publication bias was qualitatively assessed through use of a funnel plot of the log(standard error of the effect estimates) versus the effect estimates. Analyses were conducted using Review Manager Version 5.1.4 (Cochrane Collaboration, Oxford, UK) and R Version 2.13.1.

Results

Literature search and study selection

The initial database search yielded 25 articles, and four additional articles were retrieved from a search of the reference lists of the originally identified articles. Screening of the titles and abstracts of these 29 articles for study eligibility led to the initial exclusion of 18 articles which did not meet eligibility requirements, leaving 11 full articles for review [22, 25–34]. Of these articles, four were excluded due to not reporting on a case control study [28], providing duplicate data [31, 33], and reporting the incorrect phenotype [29]. This systematic review resulted in the identification of seven eligible studies (Figure 1).

Figure 1
figure 1

Flow diagram of systematic literature search.

Full size image

Of the seven studies retained for review, three were conducted in the United States [22, 27, 32], two in Turkey [25, 34], one in Finland [30], and one in Brazil [26]. All studies reported on a mix of Grade III and Grade IV gliomas among exclusively Caucasian populations, based on DNA extracted from collected blood samples. Genotyping was conducted using restriction fragment length polymorphism PCR (PCR-RFLP) for all studies except Rajaraman et al. [32] which employed real-time TaqMan PCR, and Liu et al. [22] which used an array based approach. All control groups were in Hardy Weinberg equilibrium with the exception of that assembled by Custódio et al. [26] which deviated considerably (P < 0.001) and was consequently excluded from the meta-analysis. Thus, six studies [22, 25, 27, 30, 32, 34] were included in the final meta-analysis with data on 2,362 unique cases and 3,085 controls (Table 1).

Table 1 Characteristics and genotype data extracted from eligible studies
Full size table

Association between XRCC1399 G → A and Glioma

For the association between variants in 399 G → A and glioma, genotype-specific odds ratios OR1, OR2, and OR3 were 1.33 (95% CI 1.12-1.58, P = 0.001), 1.14 (95% CI 1.01-1.28, P = 0.03), and 1.16 (95% CI 0.98-1.37, P = 0.09), respectively. As these genotype-specific odds ratios are most suggestive of a dominant model, genotypes AG and AA were collapsed and the studies were meta-analyzed using a dominant model (AA + AG versus GG).

Results of the overall meta-analysis are presented in Figure 2. The pooled odds ratio among Caucasians for the association between 399 G → A AG + AA genotypes, as compared to GG genotype, and risk of glioma was statistically significant (OR = 1.17, 95% CI 1.05-1.30, P = 0.006), with indication of high among-study heterogeneity (I 2 = 87%). Data were available to explore potential sources of this heterogeneity through stratification by gender, glioma subtype (Grade III vs. Grade IV), study country (US vs. other), genotyping method (PCR-RFLP vs. other), and study size (n < 500 vs. n ≥ 500), and results of these analyses are presented in Table 2. A statistically significant association was observed among females (OR = 2.27, 95% CI 1.45-3.57, P < 0.001), but this stratification was only feasible for one study [22] based on only 158 female cases. Furthermore, although significant associations were detected for studies with small sample size (OR = 3.09, 95% CI 2.06-4.65, P < 0.001), studies conducted outside of the US (OR = 1.22, 95% CI 1.05-1.42, P = 0.009), and studies employing PCR-RFLP genotyping (OR = 1.16, 95% CI 1.01-1.32, p = 0.03), it was clear that these associations were being driven by results of the study by Yosunkaya et al. [34]. An influence analysis demonstrated that there was no longer a statistically significant association between the XRCC1 399 G → A polymorphism and glioma after removing this study from the meta-analysis (OR = 1.10, 95% CI 0.98-1.23, P = 0.10), and there were no significant associations detected upon repeating previous subgroup analyses except for that among females as described above (Table 3). A funnel plot of the remaining studies did not indicate any evidence of publication bias (available upon request).

Figure 2
figure 2

Forest plot of GG vs. AG+AA genotypes and association with glioma.

Full size image
Table 2 Results of overall and stratified analyses for the association of XRCC1 399 G → A and risk of glioma, all eligible studies
Full size table
Table 3 Results of overall and stratified analyses for the association of XRCC1 399 G → A and risk of glioma, Yosunkaya et al.[34]excluded
Full size table

Discussion

Studies of the XRCC1 protein have demonstrated that it plays an important role in repairing single strand DNA breaks by participating in many processes including detecting such breaks and stabilizing proteins involved in the base excision repair system [35]. Because of the crucial role the protein plays, the effect of DNA variation in the coding sequence has been extensively explored in cancer and many other diseases. Studies have shown that the specific G → A substitution at codon 399 can induce conformational changes including the loss of α-helical structures that may be critical for effective protein-protein interactions [36], and that such variation corresponds with significantly reduced repair rates of irradiation-induced DNA damage [37].

Given the association of 399 G → A polymorphisms with other cancers [29] and the established risk of brain tumor development induced by ionizing radiation [3–5], these variants have been explored in numerous studies for association with glioma yet have yielded conflicting results. Despite the biologic plausibility of an association between XRCC1 399 G → A and risk of glioma, this meta-analysis does not provide evidence of such an association. The initial meta-analysis demonstrated a significant association of the polymorphism with glioma among Caucasians (OR = 1.17, 95% CI 1.05-1.30, P = 0.006) with high corresponding among-study heterogeneity (I 2 = 87%). However, an influence analysis conducted by sequentially removing each study and recalculating the association and heterogeneity demonstrated that the study by Yosunkaya et al. [34], the smallest contributor to the meta-analysis (Figure 2), was disproportionately driving the apparent association and among-study heterogeneity. While the reported methodology of this study did not raise specific concerns, the observed odds ratio employing a dominant model of 7.09 (95% CI 3.83-13.11) is many times higher than has been observed for the association of this polymorphism and any cancer, and is almost certainly a statistical artifact of the small study sample. After exclusion of this study, there was not a statistically significant association demonstrated between the 399 G → A polymorphism and glioma (OR = 1.10, 95% CI 0.98-1.23, P = 0.10) among Caucasians, reduced heterogeneity (I 2 = 7%), and there were no notable associations in stratified analyses.

While it is not possible to rule out the potential for bias in the identified studies, the five studies on which the final meta-analysis was based were deemed to be free of any major sources of bias which would compromise their validity. All studies were sufficiently clear in their description of recruitment methodology and participant characteristics, with the exception of reporting participant age in three studies [22, 27, 30]. All of the studies but one [25] discussed implementation of quality control methods during genotyping, such as use of control samples and replicates to assess concordance. However, only one study [27] reported blinding laboratory personnel to the case/control status of the samples. All studies reported matching controls to cases on age and gender, and all studies except one [25] reported matching on race/ethnicity or geographical location.

Conclusions

Although there was no indication of systematic bias among the final eligible studies as described above, this meta-analysis may have been limited by the small sample sizes of some of the included studies, as well as the lack of data for some planned subgroup analyses. In particular, despite research indicating that the genetic landscape of Grade III versus Grade IV gliomas may differ considerably [38], only two of the included studies in this meta-analysis reported associations for the subtypes separately [22, 30]. This limited our ability to explore associations with the XRCC1 399 G → A polymorphism that may be pertinent to one subtype but not the other. Future investigators should report genotype counts for glioma subtypes separately to permit stratified analyses. Despite these potential limitations, the current literature suggests no statistically significant association between the XRCC1 399 G → A polymorphism and glioma.

References

  1. CBTRUS: CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2004-2008. Source. 2010, Hinsdale: Central Brain Tumor Registry of the United States, Accessed at: http://www.cbtrus.org

    Google Scholar 

  2. Wen PY, Kesari S: Malignant gliomas in adults. N Engl J Med. 2008, 359: 492-507. 10.1056/NEJMra0708126.

    Article  CAS  PubMed  Google Scholar 

  3. Bondy ML, Scheurer ME, Malmer B, Barnholtz-Sloan JS, Davis FG, Il’yasova D, Kruchko C, McCarthy BJ, Rajaraman P, Schwartzbaum JA, Sadetzki S, Schlehofer B, Tihan T, Wiemels JL, Wrensch M, Buffler PA: Brain Tumor Epidemiology Consortium: Brain tumor epidemiology: consensus from the Brain Tumor Epidemiology Consortium. Cancer. 2008, 113 (7): 1953-1968. 10.1002/cncr.23741.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Schwartzbaum JA, Fisher JL, Aldape KD, Wrensch M: Epidemiology and molecular pathology of glioma. Nat Clin Pract Neurol. 2006, 2: 494-503.

    Article  PubMed  Google Scholar 

  5. Ostrom QT, Barnholtz-Sloan JS: Current state of our knowledge on brain tumor epidemiology. Curr Neurol Neurosci Rep. 2011, 11: 329-335. 10.1007/s11910-011-0189-8.

    Article  PubMed  Google Scholar 

  6. Wrensch M, Jenkins RB, Chang JS, Yeh RF, Xiao Y, Decker PA, Ballman KV, Berger M, Buckner JC, Chang S, Giannini C, Halder C, Kollmeyer TM, Kosel ML, LaChance DH, McCoy L, O’Neill BP, Patoka J, Pico AR, Prados M, Quesenberry C, Rice T, Rynearson AL, Smirnov I, Tihan T, Wiemels J, Yang P, Wiencke JK: Variants in the CDKN2B and RTEL1 regions are associated with high-grade glioma susceptibility. Nat Genet. 2009, 41: 905-908. 10.1038/ng.408.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Sanson M, Hosking FJ, Shete S, Zelenika D, Dobbins SE, Ma Y, Enciso-Mora V, Idbaih A, Delattre JY, Hoang-Xuan K, Marie Y, Boisselier B, Carpentier C, Wang XW, Di Stefano AL, Labusserière M, Gousias K, Schramm J, Boland A, Lechner D, Gut I, Armstrong G, Liu Y, Yu R, Lau C, Di Bernardo MC, Robertston LB, Muir K, Hepworth S, Swerdlow A, et al: Chromosome 7p11.2 (EGFR) variation influences glioma risk. Hum Mol Genet. 2011, 20: 2897-2904. 10.1093/hmg/ddr192.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Shete S, Hosking FJ, Robertson LB, Dobbins SE, Sanson M, Malmer B, Simon M, Marie Y, Boisselier B, Delattre JY, Hoang-Xuan K, El Hallani S, Idbaih A, Zelenika D, Andersson U, Henriksson R, Bergenheim AT, Feychting M, Lönn S, Ahlbom A, Schramm J, Linnebank M, Hemminki K, Kumar R, Hepworth SJ, Price A, Armstrong G, Liu Y, Gu X, Yu R, et al: Genome-wide association study identifies five susceptibility loci for glioma. Nat Genet. 2009, 41: 899-904. 10.1038/ng.407.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Almeida KH, Sobol RW: A unified view of base excision repair: lesion-dependent protein complexes regulated by post-translational modification. DNA Repair (Amst). 2007, 6: 695-711. 10.1016/j.dnarep.2007.01.009.

    Article  CAS  Google Scholar 

  10. Vidal AE, Boiteux S, Hickson ID, Radicella JP: XRCC1 coordinates the initial and late stages of DNA a basic site repair through protein-protein interactions. EMBO J. 2001, 20: 6530-6539. 10.1093/emboj/20.22.6530.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Kubota Y, Nash RA, Klungland A, Schär P, Barnes DE, Lindahl T: Reconstitution of DNA base excision-repair with purified human proteins: interaction between DNA polymerase beta and the XRCC1 protein. EMBO J. 1996, 15: 6662-6670.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Frazer KA, Ballinger DG, Cox DR, Hinds DA, Stuve LL, Gibbs RA, Belmont JW, Boudreau A, Hardenbol P, Leal SM, Pasternak S, Wheeler DA, Willis TD, Yu F, Yang H, Zeng C, Gao Y, Hu H, Hu W, Li C, Lin W, Liu S, Pan H, Tang X, Wang J, Wang W, Yu J, Zhang B, Zhang Q, International HapMap Consortium, et al: A second generation human haplotype map of over 3.1 million SNPs. Nature. 2007, 449: 851-861. 10.1038/nature06258.

    Article  CAS  PubMed  Google Scholar 

  13. 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.

    CAS  PubMed  Google Scholar 

  14. Duell EJ, Wiencke JK, Cheng TJ, Varkonyi A, Zuo ZF, Ashok TDS, Mark EJ, Wain JC, Christiani DC, Kelsey KT: Polymorphisms in the DNA repair genes XRCC1 and ERCC2 and biomarkers of DNA damage in human blood mononuclear cells. Carcinogenesis. 2000, 21: 965-971. 10.1093/carcin/21.5.965.

    Article  CAS  PubMed  Google Scholar 

  15. Kiyohara C, Takayama K, Nakanishi Y: Association of genetic polymorphisms in the base excision repair pathway with lung cancer risk: a meta-analysis. Lung Cancer. 2006, 54: 267-283. 10.1016/j.lungcan.2006.08.009.

    Article  PubMed  Google Scholar 

  16. Saadat M, Ansari-Lari M: Polymorphism of XRCC1 (at codon 399) and susceptibility to breast cancer, a meta-analysis of the literatures. Breast Cancer Res Treat. 2009, 115: 137-144. 10.1007/s10549-008-0051-0.

    Article  CAS  PubMed  Google Scholar 

  17. 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. 10.1016/j.urology.2009.02.046.

    Article  PubMed  Google Scholar 

  18. Li H, Ha TC, Tai BC: XRCC1 gene polymorphisms and breast cancer risk in different populations: a meta-analysis. Breast. 2009, 18: 183-191. 10.1016/j.breast.2009.03.008.

    Article  CAS  PubMed  Google Scholar 

  19. Huang Y, Li L, Yu L: XRCC1 Arg399Gln, Arg194Trp and Arg280His polymorphisms in breast cancer risk: a meta-analysis. Mutagenesis. 2009, 24: 331-339. 10.1093/mutage/gep013.

    Article  CAS  PubMed  Google Scholar 

  20. Wei B, Zhou Y, Xu Z, Ruan J, Zhu M, Jin K, Zhou D, Hu Q, Wang Q, Wang Z, Yan Z: XRCC1 Arg399Gln and Arg194Trp polymorphisms in prostate cancer risk: a meta-analysis. Prostate Cancer Prostatic Dis. 2011, 14: 225-231. 10.1038/pcan.2011.26.

    Article  CAS  PubMed  Google Scholar 

  21. Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group: Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009, 6: e1000097-10.1371/journal.pmed.1000097.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Liu Y, Scheurer ME, El-Zein R, Cao Y, Do KA, Gilbert M, Aldape KD, Wei Q, Etzel C, Bondy ML: Association and interactions between DNA repair gene polymorphisms and adult glioma. Cancer Epidemiol Biomarkers Prev. 2009, 18: 204-214. 10.1158/1055-9965.EPI-08-0632.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Thakkinstian A, McElduff P, D’Este C, Duffy D, Attia J: A method for meta-analysis of molecular association studies. Stat Med. 2005, 24: 1291-1306. 10.1002/sim.2010.

    Article  PubMed  Google Scholar 

  24. Higgins JPT, Thompson SG, Deeks JJ, Altman DG: Measuring inconsistency in meta-analyses. BMJ. 2003, 327: 557-560. 10.1136/bmj.327.7414.557.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Cengiz SL, Acar H, Inan Z, Yavuz S, Baysefer A: Deoxy-ribonucleic acid repair genes XRCC1 and XPD polymorphisms and brain tumor risk. Neurosciences (Riyadh). 2008, 13: 227-232.

    Google Scholar 

  26. Custódio AC, Almeida LO, Pinto GR, Santos MJ, Almeida JRW, Clara CA, Rey JA, Casartelli C: Analysis of the polymorphisms XRCC1Arg194Trp and XRCC1Arg399Gln in gliomas. Genet Mol Res. 2011, 10: 1120-1129. 10.4238/vol10-2gmr1125.

    Article  PubMed  Google Scholar 

  27. Felini MJ, Olshan AF, Schroeder JC, North KE, Carozza SE, Kelsey KT, Liu M, Rice T, Wiencke JK, Wrensch MR: DNA repair polymorphisms XRCC1 and MGMT and risk of adult gliomas. Neuroepidemiology. 2007, 29: 55-58. 10.1159/000108919.

    Article  PubMed  Google Scholar 

  28. Goode EL, Ulrich CM, Potter JD: Polymorphisms in DNA repair genes and associations with cancer risk. Cancer Epidemiol Biomarkers Prev. 2002, 11: 1513-1530.

    CAS  PubMed  Google Scholar 

  29. Hu Z, Ma H, Chen F, Wei Q, Shen H: XRCC1 polymorphisms and cancer risk: a meta-analysis of 38 case–control studies. Cancer Epidemiol Biomarkers Prev. 2005, 14: 1810-1818. 10.1158/1055-9965.EPI-04-0793.

    Article  CAS  PubMed  Google Scholar 

  30. Kiuru A, Lindholm C, Heinävaara S, Ilus T, Jokinen P, Haapasalo H, Salminen T, Christensen HC, Feychting M, Johansen C, Lönn S, Malmer B, Schoemaker MJ, Swerdlow AJ, Auvinen A: XRCC1 and XRCC3 variants and risk of glioma and meningioma. J Neurooncol. 2008, 88: 135-142. 10.1007/s11060-008-9556-y.

    Article  CAS  PubMed  Google Scholar 

  31. McKean-Cowdin R, Barnholtz-Sloan J, Inskip PD, Ruder AM, Butler MA, Rajaraman P, Razavi P, Patoka J, Wiencke JK, Bondy ML, Wrensch M: Associations between polymorphisms in DNA repair genes and glioblastoma. Cancer Epidemiol Biomarkers Prev. 2009, 18: 1118-1126. 10.1158/1055-9965.EPI-08-1078.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Rajaraman P, Hutchinson A, Wichner S, Black PM, Fine HA, Loeffler JS, Selker RG, Shapiro WR, Rothman N, Linet MS, Inskip PD: DNA repair gene polymorphisms and risk of adult meningioma, glioma, and acoustic neuroma. Neuro Oncol. 2010, 12: 37-48. 10.1093/neuonc/nop012.

    Article  CAS  PubMed  Google Scholar 

  33. Wang LE, Bondy ML, Shen H, El-Zein R, Aldape K, Cao Y, Pudavalli V, Levin VA, Yung WK, Wei Q: Polymorphisms of DNA repair genes and risk of glioma. Cancer Res. 2004, 64: 5560-5563. 10.1158/0008-5472.CAN-03-2181.

    Article  CAS  PubMed  Google Scholar 

  34. Yosunkaya E, Kucukyuruk B, Onaran I, Gurel CB, Uzan M, Kanigur-Sultuybek G: Glioma risk associates with polymorphisms of DNA repair genes, XRCC1 and PARP1. Br J Neurosurg. 2010, 24: 561-565. 10.3109/02688697.2010.489655.

    Article  PubMed  Google Scholar 

  35. Thompson LH, West MG: XRCC1 keeps DNA from getting stranded. Mutat Res. 2000, 459: 1-18. 10.1016/S0921-8777(99)00058-0.

    Article  CAS  PubMed  Google Scholar 

  36. Monaco R, Rosal R, Dolan M, Pincus MR, Brandt-Rauf PW: Conformational effects of a common codon 399 polymorphism on the BRCT1 domain of the XRCC1 protein. Protein J. 2007, 26: 541-546. 10.1007/s10930-007-9095-y.

    Article  CAS  PubMed  Google Scholar 

  37. Vodicka P, Stetina R, Polakova V, Tulupova E, Naccarati A, Vodickova L, Kumar R, Hanova M, Pardini B, Slyskova J, Musak L, De Palma G, Soucek P, Hemminki K: Association of DNA repair polymorphisms with DNA repair functional outcomes in healthy human subjects. Carcinogenesis. 2007, 28: 657-664.

    Article  CAS  PubMed  Google Scholar 

  38. Jenkins RB, Wrensch MR, Johnson D, Fridley BL, Decker PA, Xiao Y, Kollmeyer TM, Rynearson AL, Fink S, Rice T, McCoy LS, Halder C, Kosel ML, Giannini C, Tihan T, O’Neill BP, Lachance DH, Yang P, Wiemels J, Wiencke JK: Distinct germ line polymorphisms underlie glioma morphologic heterogeneity. Cancer Genet. 2011, 204: 13-18. 10.1016/j.cancergencyto.2010.10.002.

    Article  PubMed  PubMed Central  Google Scholar 

Pre-publication history

Download references

Acknowledgments

The authors would like to thank Andrew T. DeWan for his involvement in revising the manuscript for important intellectual content and Yanhong Liu for providing unpublished XRCC1 Arg399Gln genotype counts for glioma cases and controls.

Author information

Authors and Affiliations

  1. Yale School of Public Health, New Haven, CT, USA

    Daniel I Jacobs

  2. Center for Perinatal, Pediatric and Environmental Epidemiology, Yale School of Public Health, New Haven, CT, USA

    Michael B Bracken

Authors
  1. Daniel I Jacobs
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael B Bracken
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael B Bracken.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

Both authors conceived and designed the study, performed the statistical analysis and interpretation, drafted the manuscript, revised for important intellectual content, and read and approved the final manuscript.

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Authors’ original file for figure 2

Rights and permissions

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Jacobs, D.I., Bracken, M.B. Association between XRCC1polymorphism 399 G->A and glioma among Caucasians: a systematic review and meta-analysis. BMC Med Genet 13, 97 (2012). https://doi.org/10.1186/1471-2350-13-97

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/1471-2350-13-97

Keywords

BMC Medical Genetics

ISSN: 1471-2350

Contact us