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International Journal of Clinical and Experimental Medicine logoLink to International Journal of Clinical and Experimental Medicine
. 2015 Aug 15;8(8):11863–11880.

Association between X-ray repair cross-complementing group 1 gene polymorphisms and glioma risk: a systematic review and meta-analysis based on 22 case-control studies

Jiqiang Li 1,2, Qianxue Chen 1, Baohui Liu 1,2, Jian Yang 1,2, Lingmin Shao 1,2, Tingfeng Wu 1,2
PMCID: PMC4612784  PMID: 26550099

Abstract

Objectives: Glioma is the most common central nervous system tumor. This systematic review and meta-analysis is aimed to systematically assess the association of XRCC1 polymorphisms with the risk of glioma. Methods: Such databases as EMbase, PubMed, The Cochrane Library, the China National Knowledge Infrastructure (CNKI) platforms, VIP and WanFang were searched up to April 2015 to collect case-control studies of association between XRCC1 polymorphisms and glioma. Data were extracted and meta-analysis was conducted by using Stata 12.0 softwares. Results: A total of 22 studies were included in the meta-analysis, including 18503 glioma patients and 24367 controls. The overall data indicated that XRCC1 Arg194Trp (C>T) polymorphism significantly increased glioma risk (allele C versus T: OR=0.72, 95% CI=0.55-0.93, CC versus TT: OR=0.55, 95% CI=0.46-0.67; CC versus CT+TT: OR=0.64, 95% CI=0.45-0.91 and CC+CT vs. TT: OR=0.61, 95% CI=0.51-0.74), especially in Asia ethnicity. XRCC1 Arg280His (G>A) polymorphism has no association with glioma (allele G versus A: OR=1.01, 95% CI=0.83-1.22; GG versus AA: OR=1.07, 95% CI=0.66-1.75; GA versus AA: OR=1.01, 95% CI=0.77-1.32; GG versus GA+AA: OR=1.01, 95% CI=0.84-1.22 and GG+GT versus AA: OR=1.06, 95% CI=0.67-1.69). XRCC1 Arg399Gln (G>A) polymorphism will significantly increase glioma risk in Asian (allele G versus A: OR=0.78, 95% CI= 0.72-0.84; GG versus AA: OR=0.56, 95% CI=0.47-0.66; GA versus AA OR=0.71, 95% CI=0.59-0.84; GG versus GA+AA: OR=0.76, 95% CI=0.68-0.84 and GG+GA vs. AA: OR=0.62, 95% CI=0.53-0.73) but not Caucasian ethnicity. XRCC1 Pro161Leu (C>T), Leu387Leu (G>A), Pro602Thr (C>A), Ser593Arg (C>G) and Glu491Lys (G>A) polymorphisms increased glioma risk in different degrees. Conclusion: This meta-analysis suggested that XRCC1 Arg194Trp and XRCC1 Arg399Gln (G>A) polymorphisms led to susceptibility to glioma in Asian but not Caucasian population. XRCC1 Glu491Lys (G>A), Pro161Leu (C>T), Leu387Leu (G>A), Pro602Thr (C>A), Thr304Ala (A>G) and Ser593Arg (C>G) polymorphisms will increase glioma risk. However, XRCC1 Arg280His (G>A) is irrelevant to the increased or decreased glioma risk.

Keywords: XRCC1, glioma, polymorphism, systematic review, meta-analysis

Introduction

Glioma is the most common central nervous system tumor comprising about 80% of malignant brain tumors. Despite improvements in diagnosis and treatment, the prognosis of glioma remains dismal with a 5-year overall survival rate of less than 30% [1]. The tumor cells often invade adjacent brain tissue, which is largely responsible for the poor outcome of the disease [2]. Glioma has drawn special attention because of its poor prognosis and recurrence. Diagnosis at the early stages and prevention of tumor progression become important strategies to fight glioma, and identification of genetic risk factors such as the single nucleotide polymorphisms (SNPs) is the most effective way to reach this purpose [3,4].

X-ray repair cross complementing gene group 1 (XRCC1), one of the DNA repair genes, which is located on chromosome 19q13.2-13.3 with a length of 33 kilo bases, plays an important role in base excision as a crucial scaffold protein, which brings proteins of the DNA repair complex together [5]. XRCC1 involves 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 polyadenosine diphosphate-ribose polymerase (ADP) at the site of damaged DNA [6]. To date, several XRCC1 SNPs including Arg194Trp (C>T), Arg280His (G>A), Arg399Gln (G>A), Glu491Lys (G>A), Pro161Leu (C>T), Leu387Leu (G>A), Pro602Thr (C>A), Thr304Ala (A>G) and Ser593Arg (C>G) were reseached to find association between XRCC1 gene mutation and glioma risk. XRCC1 Arg194Trp which single-nucleotide C changed to T on exon 6, Arg280His single-nucleotide G changed to A on exon 9, Arg399Gln single-nucleotide G changed to A on exon 10, Glu491Lys single-nucleotide G changed to A on exon 13, Pro161Leu single-nucleotide C changed to T on exon 5, Leu387Leu single-nucleotide G changed to A on exon 10, Pro602Thr single-nucleotide C changed to A on exon 17, Thr304Ala single-nucleotide A changed to G on exon 9 and Ser593Arg single-nucleotide C changed to G on exon 16 are nine XRCC1 genetic polymorphisms of more than 300 validated SNPs in the XRCC1 gene in the dbSNP database. These mutations of the SNPs may affect DNA repair capability by changing interactions between XRCC1-coded proteins and other base excision repair gen e (BER)-coded proteins [7].

Genetic polymorphisms in XRCC1 gene have been reported to be associated with the risk of several types of cancers including lung cancer, prostate cancer and glioma [8,9]. Recently, some scholars hold different viewpoints about whether XRCC1 is associated with the risk of glioma. McKean-Cowdin et al [10] suggested that XRCC1 Arg194Trp is not involved in susceptibility of neurons for developing astrocytomas and glioblastomas (G versus A: OR=0.89, 95% CI=0.70-1.14). However, Liu et al [11] indicated that XRCC1 Arg194Trp is related to the risk of glioma (G versus A: OR=2.66, 95% CI=1.48-4.88). Two review articles [12] including only four literatures, draw a conclusion that the XRCC1 Arg194Trp polymorphism is not a risk factor for glioma. A meta-analysis [13] failed to conclude the association XRCC1 Arg280His and glioma with four case-control studies. As for XRCC1 Arg399Glu polymorphism, Jacobs et al [14], a meta-analysis which concluded no association between XRCC1 Arg399Trp polymorphism and glioma, and another meta-analysis [15] suggested that the XRCC1 Arg399Gln polymorphism may contribute to the susceptibility of glioma. Only one case-control study was performed to research the association between XRCC1 Glu491Lys [16], Pro161Leu [17], Leu387Leu [17], Pro602Thr [17], Thr304Ala [18], Ser593Arg [18] and glioma. Due to the controversial results and insufficient data, we carried out a comprehensive quantitative meta-analysis to achieve a more precise estimation of this association.

Materials and methods

Literature search strategy

We searched such databases as EMbase, PubMed, the Cochrane Library, the China National Knowledge Infrastructure (CNKI) platforms, VIP and WanFang without a language limitation, including all studies published until April 2015, with a combination of the following keywords: XRCC1 or X-ray repair cross-complementing 1, glioma, glioblastoma, brain tumor, variation, polymorphism, SNP, single nucleotide polymorphisms. All searched studies were retrieved and the bibliographies were reviewed for other relevant publications. Review articles and bibliographies of other relevant studies identified were searched manually to identify additional eligible studies. We tried to identify potential relevant studies from the whole reference lists by orderly reviewing title, abstract and full text.

Selection criteria

All the included studies must satisfy the following inclusion criteria: I. Concerning the association of XRCC1 polymorphism and glioma risk; II. Preformed in a case-control study; III. Providing the exact sample sizes of all genotypes in both case groups and control groups; IV. Providing detailed information for genotype extraction or calculation.

Data extraction

Having comprehensively searched the above databases, we reviewed all papers in accordance with the inclusion criteria for further analysis. Two investigators independently reviewed all eligible publications and extracted the data as the follows: first author, publication year, ethnicity (Caucasian or Asian or Mixed), the number of sample size in cases and controls, and the genotyping information. As for the disputes, they were resolved by discussion and consulting a senior reviewer to reach a final decision.

Statistical analysis

The odds ratios (ORs) with 95 % confidence intervals (CIs) of the XRCC1 polymorphisms and glioma risk were estimated for each study. We calculated the combined ORs with 95% CI for allele comparison, co-dominant model comparison, dominant model comparison and recessive model comparison, respectively. Hardy-Weinberg equilibrium (HWE) was evaluated by using chi-square test in control groups for each study. P<0.05 was considered significant departure from HWE. A chi-square-based Q-statistic test was performed to assess heterogeneity across the studies [19]. P<0.05 indicated obvious between-study heterogeneity. If P>0.05, the ORs were pooled according to the fixed-effects model [20]; otherwise, the random-effects model was used [21]. Sensitivity analysis was conducted by removing the single studies, one at a time and recalculating the summary ORs to identify the stability of the models. Publication bias and selection bias of the included studies was assessed by Begg’s funnel plots and Egger’s test [22], Publication bias and selection bias were tested with Stata 12.0 software, using Begg’s funnel plots with Egger’s test. If P<0.05, it revealed the existence of publication bias and selection bias.

Results

Study characteristics

232 relevant studies were retrieved and screened originally. As shown in Figure 1, 197 studies were excluded due to obviously irrelevance or other polymorphisms, and 13 studies were excluded due to review article or insufficient data. Finally, a total of 22 studies [10,11,16-18,23-39] subjected to the final analysis, including 18503 glioma patients and 24367 controls. Among these trials, 14 studies [10,11,24,26-33,35,37,39] researched association between Arg194Trp (C>T) and glioma, including 9 studies [11,26,27,29,31,32,35,37,39] were performed in China and their ethnicity were Asian, 3 [10,30,33] in USA and one [28] in multicountry including Denmark, Finland, Sweden and the UK which ethnicity was Caucasian. Only one study [24] was performed in Brazil and the ethnicity was mixed. 8 studies [26-29,33,35,37,39] researched association between Arg280His (G>A) and glioma, including 6 [26,27,29,35,37,39] were performed in china and their ethnicity were Asian, one study [33] performed in USA and another [28] performed in European multicountry with a Caucasian ethnicity. 18 studies [10,23-39] researched association between Arg399Gln (G>A) and glioma, including 8 studies [26,27,29,31,32,35,37,39] were performed in China and their ethnicity were Asian, 5 studies [10,25,30,33,36] performed in USA, 2 [23,38] in Turkey, one [34] in Spain and one [28] in European multicountry with a Caucasian ethnicity and one study [24] was performed in Brazil with a mixed ethnicity. Another three studies [16-18] performed in China including one [17] researched Pro161Leu (C>T), Leu387Leu (G>A), Pro602Thr (C>A), one [18] researched Thr304Ala (A>G) and Ser593Arg (C>G) and one [16] researched Glu491Lys (G>A). All cases were confirmed by pathologic diagnosis of glioma. Control cases were collected from healthy population or other nontumorous patients in hospital. Four genotyping methods including PCR-PFLP, Taqman, Mass ARRAY and CRS-PCR were adopted to detect these SNPs. 8 studies [10,28-30,33,35,37,39] researching Arg194Trp (C>T), 6 [26,28,33,35,37,39] researching Arg280His (G>A), 13 [10,23,25,26,28,31-36,38,39] researching Arg399Gln (G>A), one study [17] researching Leu387Leu (G>A) and Pro602Thr (C>A) and one [18] researching Thr304Ala (A>G) met the Hardy-Weinberg equilibrium. The detail characteristics including first author (year), country, ethnicity, Genotyping method and HWE of these eligible studies were showed in Table 1.

Figure 1.

Figure 1

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Flow chart for the selection process of the included 22 studies.

Table 1.

Main characteristic of the 22 eligible studies

Study (year) Polymorphisms Country Ethnicity Source of control Case group Control group Genotyping method HWE
Wang 2012 Arg194Trp (C>T) China Asian Hospital CC=376; CT=218; TT=30 CC=355; CT=205; TT=20 PCR-PFLP Yes
Arg280His (G>A) China Asian Hospital GG=506; GA=115; AA=3 GG=473; GA=98; AA=9 PCR-PFLP Yes
Arg399Trp (G>A) China Asian Hospital GG=270; GA=279; AA=75 GG=300; GA=232; AA=48 PCR-PFLP Yes
Rajaraman 2010 Arg194Trp (C>T) USA Caucasian Mixed CC=304; CT=38; TT=0 CC=394; CT=73; TT=1 Taqman Yes
Arg280His (G>A) USA Caucasian Mixed GG=312; GA=28; AA=0 GG=417; GA=48; AA=1 Taqman Yes
Arg399Trp (G>A) USA Caucasian Mixed GG=142; GA=164; AA=44 GG=205; GA=201; AA=72 Taqman Yes
Kiuru 2008 Arg194Trp (C>T) European Caucasian Population CC=626; CT=71; TT=3 CC=1377; CT=177; TT=2 PCR-PFLP Yes
Arg280His (G>A) European Caucasian Population GG=633; GA=67; AA=1 GG=1377; GA=157; AA=4 PCR-PFLP Yes
Arg399Trp (G>A) European Caucasian Population GG=284; GA=324; AA=91 GG=645; GA=728; AA=176 PCR-PFLP Yes
Xu 2013 Arg194Trp (C>T) China Asian Hospital CC=525; CT=301; TT=60 CC=540; CT=311; TT=35 PCR-PFLP Yes
Arg280His (G>A) China Asian Hospital GG=618; GA=177; AA=91 GG=621; GA=178; AA=87 PCR-PFLP Yes
Arg399Trp (G>A) China Asian Hospital GG=451; GA=365; AA=70 GG=469; GA=372; AA=45 PCR-PFLP No
Liu 2009 Arg194Trp (C>T) USA Caucasian Population CC=29; CT=180; TT=1 CC=310; CT=52; TT=3 MassARRAY Yes
Arg399Trp (G>A) USA Caucasian Population GG=149; GA=162; AA=62 GG=169; GA=145; AA=50 MassARRAY No
Hu 2011 Arg194Trp (C>T) China Asian Hospital CC=71; CT=38; TT=18 CC=163; CT=64; TT=22 PCR-PFLP No
Arg280His (G>A) China Asian Hospital GG=72; GA=28; AA=27 GG=153; GA=58; AA=38 PCR-PFLP No
Arg399Trp (G>A) China Asian Hospital GG=58; GA=48; AA=21 GG=145; GA=75; AA=29 PCR-PFLP No
Zhou 2011 Arg194Trp (C>T) China Asian Hospital CC=145; CT=112; TT=14 CC=159; CT=117; TT=13 PCR-PFLP Yes
Arg280His (G>A) China Asian Hospital GG=218; GA=45; AA=8 GG=240; GA=44; AA=5 PCR-PFLP Yes
Arg399Trp (G>A) China Asian Hospital GG=121; GA=113; AA=37 GG=147; GA=118; AA=24 PCR-PFLP Yes
McKean-Cowdin 2009 Arg194Trp (C>T) USA Caucasian Mixed CC=842; CT=117; TT=3 CC=1664; CT=252; TT=6 MassARRAY Yes
Arg399Trp (G>A) USA Caucasian Mixed GG=397; GA=461; AA=145 GG=844; GA=865; AA=262 MassARRAY Yes
Custódio 2011 Arg194Trp (C>T) Brazil Mixed Population CC=15; CT=31; TT=34 CC=67; CT=4; TT=29 PCR-PFLP No
Arg399Trp (G>A) Brazil Mixed Population GG=23; GA=33; AA=24 GG=29; GA=20; AA=51 PCR-PFLP No
Liu 2012 Arg194Trp (C>T) China Asian Hospital CC=294; CT=105; TT=45 CC=334; CT=89; TT=19 MassARRAY No
Pan 2013 Arg194Trp (C>T) China Asian Hospital CC=301; CT=116; TT=27 CC=327; CT=101; TT=15 MassARRAY No
Arg399Trp (G>A) China Asian Hospital GG=226; GA=190; AA=27 GG=244; GA=178; AA=21 MassARRAY Yes
Luo 2013 Arg194Trp (C>T) China Asian Hospital CC=204; CT=63; TT=30 CC=297; CT=96; TT=22 PCR-PFLP No
Arg399Trp (G>A) China Asian Hospital GG=111; GA=134; AA=51 GG=189; GA=181; AA=45 PCR-PFLP Yes
Li 2014 Arg194Trp (C>T) China Asian Hospital CC=183; CT=171; TT=16 CC=175; CT=151; TT=20 PCR-PFLP Yes
Arg280His (G>A) China Asian Hospital GG=302; GA=61; AA=5 GG=251; GA=79; AA=16 PCR-PFLP No
Arg399Trp (G>A) China Asian Hospital GG=142; GA=167; AA=59 GG=176; GA=132; AA=38 PCR-PFLP No
Gao 2014 Arg194Trp (C>T) China Asian Hospital CC=235; CT=73; TT=18 CC=279; CT=84; TT=13 MassARRAY No
Arg280His (G>A) China Asian Hospital GG=250; GA=66; AA=10 GG=313; GA=57; AA=6 MassARRAY Yes
Arg399Trp (G>A) China Asian Hospital GG=126; GA=155; AA=45 GG=178; GA=168; AA=29 MassARRAY Yes
Wang 2004 Arg399Trp (G>A) USA Caucasian Hospital GG=134; GA=138; AA=37 GG=131; GA=162; AA=49 PCR-PFLP Yes
Yosunkaya 2010 Arg399Trp (G>A) Turkey Caucasian Hospital GG=37; GA=67; AA=15 GG=18; GA=71; AA=91 PCR-PFLP Yes
Felini 2007 Arg399Trp (G>A) USA Caucasian Population GG=158; GA=155; AA=53 GG=180; GA=196; AA=51 PCR-PFLP Yes
Cengiz 2008 Arg399Trp (G>A) Turkey Caucasian Population GG=51; GA=73; AA=11 GG=43; GA=41; AA=3 PCR-PFLP Yes
Rodriguez-Hernandez 2014 Arg399Trp (G>A) Spain Caucasian Hospital GG=52; GA=55; AA=8 GG=80; GA=94; AA=26 Taqman Yes
Feng 2014 Pro161Leu (C>T) China Asian Hospital CC=295; CT=265; TT=78 CC=330; CT=279; TT=39 CRS-PCR No
Leu387Leu (G>A) China Asian Hospital GG=301; GA=255; AA=82 GG=326; GA=267; AA=55 CRS-PCR Yes
Pro602Thr (C>A) China Asian Hospital CC=277; CA=271; AA=91 CC=316; CA=283; AA=49 CRS-PCR Yes
Jin 2014 Thr304Ala (A>G) China Asian Hospital AA=282; AG=258; GG=80 AA=321; AG=263; GG=46 PCR-PFLP Yes
Ser593Arg (C>G) China Asian Hospital CC=290; CG=267; GG=63 CC=315; CG=277; GG=38 PCR-PFLP No
Wang 2013 Glu491Lys (G>A) China Asian Population GG=289; GA=270; AA=70 GG=320; GA=280; AA=41 CRS-PCR No
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Test of heterogeneity

Heterogeneity was estimated using the Chi-square-based Z statistic for statistical significance. If P>0.05 indicated little heterogeneity, a fixed effect model was applied to analyze the data; if not, a random effect model was adopted. The heterogeneity between studies was estimated using the I2 statistic. If I2>50%, it indicated that substantial heterogeneity existed. When I2<75%, the heterogeneity between studies could be accepted. Publication bias and selection bias were tested with Stata 12.0 software, using funnel plots with Begg’s test. If P<0.05, it revealed the existence of publication bias and selection bias. Subgroup analysis was performed based on ethnicity, source of control, genotyping method and HWE. When the overall data were divided for subgroup analysis, we observed a loss of heterogeneity in the subgroups in the Arg194Trp (C>T) co-dominant model CC versus TT (I2=35.0%, P=0.095), Arg194Trp (C>T) recessive model CC+CT versus TT (I2=0.0%, P=0.510) and Arg280His (G>A) co-dominant model GA versus AA (I2=21.6%, P=0.258) and fixed effect model was used in these subgroup analysis. Random effect model was used in other genetic polymorphisms subgroup analysis. The details of heterogeneity test were showed in Table 2.

Table 2.

Main results of heterogeneity and publication bias

Polymorphisms I-squared % p value for heterogeneity Model p value for Begg’s Test p value for Egger’s Test Publication bias
Arg194Trp (C>T)
    Allele C vs. T 93.3 <0.001 Random effect 0.324 0.164 No
    CC vs. TT 35.0 0.095 Fixed effect 0.584 0.830 No
    CT vs. TT 55.7 0.006 Random effect 0.049 0.084 No
    CC vs. CT+TT 94.7 <0.001 Random effect 0.661 0.055 No
    CC+CT vs. TT 0.0 0.510 Fixed effect 0.029 0.205 No
Arg280His (G>A)
    Allele G vs. A 70.7 0.001 Random effect >0.999 0.912 No
    GG vs. AA 54.2 0.032 Random effect 0.536 0.450 No
    GA vs. AA 21.6 0.258 Fixed effect 0.536 0.359 No
    GG vs. GA+AA 58.3 0.019 Random effect >0.999 0.989 No
    GG+GA vs. AA 50.7 0.048 Random effect 0.425 0.425 No
Arg399Gln (G>A)
    Allele G vs. A 83.2 <0.001 Random effect 0.324 0.386 No
    GG vs. AA 79.8 <0.001 Random effect 0.584 0.464 No
    GA vs. AA 73.3 <0.001 Random effect 0.049 0.353 No
    GG vs. GA+AA 66.9 <0.001 Random effect 0.661 0.588 No
    GG+GA vs. AA 79.6 <0.001 Random effect 0.029 0.505 No
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Meta-analysis results

The main results of the meta-analysis were showed in Table 3 for the overall data including 18503 glioma cases and 24367 controls. Based on three genetic models, co-dominant, dominant, and recessive and allele comparison, meta-analyses followed by stratified analyses by ethnicity, control sources genotyping methods and Hardy-Weinberg equilibrium were performed to evaluate the associations between Arg194Trp (C>T), Arg280His (G>A) and XRCC1 Arg399Gln (G>A) polymorphisms and glioma risk. In addition, because of only one literature was included respectively, the associations between Glu491Lys (G>A), Pro161Leu (C>T), Leu387Leu (G>A), Pro602Thr (C>A), Thr304Ala (A>G) and Ser593Arg (C>G) polymorphisms and glioma risk were evaluated without subgroup analysis.

Table 3.

Main results of the pooled data from the meta-analysis

Polymorphisms Study group No. of studies No. of cases No. of controls Allele Co-dominant model Dominant model Recessive model
OR (95% CI) p value OR (95% CI) p value OR (95% CI) p value OR (95% CI) p value OR (95% CI) p value
Arg194Trp (C>T) Allele C vs. T CC vs. TT CT vs. TT CC vs. CT+TT CC+CT vs. TT
Total 14 6083 8437 0.72 (0.55, 0.93) 0.014 0.55 (0.46, 0.67) <0.001 0.81 (0.58, 1.13) 0.206 0.64 (0.45, 0.91) 0.014 0.61 (0.51, 0.74) <0.001
Ethnicity
Asian 9 3789 4026 0.82 (0.74, 0.92) 0.001 0.59 (0.48, 0.72) <0.001 0.66 (0.53, 0.81) <0.001 0.86 (0.78, 0.95) 0.003 0.61 (0.50, 0.74) <0.001
Caucasian 4 2214 4311 0.66 (0.23, 1.91) 0.445 0.68 (0.27, 1.70) 0.405 1.22 (0.28, 5.26) 0.794 0.48 (0.12, 1.90) 0.295 0.87 (0.35, 2.14) 0.722
Control source
Hospital 9 3789 4026 0.82 (0.74, 0.92) 0.001 0.59 (0.48, 0.72) <0.001 0.66 (0.53, 0.81) <0.001 0.86 (0.78, 0.95) 0.003 0.61 (0.50, 0.74) <0.001
Population 3 990 2021 0.32 (0.08, 1.36) 0.124 0.21 (0.11, 0.40) <0.001 2.62 (0.29, 23.29) 0.387 0.15 (0.01, 1.90) 0.144 0.57 (0.32, 0.99) 0.044
Genotyping method
PCR-PFLP 8 3355 4421 0.80 (0.66, 0.98) 0.028 0.58 (0.46, 0.73) <0.001 0.85 (0.54, 1.36) 0.499 0.79 (0.61, 1.02) 0.066 0.65 (0.52, 0.81) <0.001
Taqman 1 342 468 1.48 (0.99, 2.22) 0.056 2.32 (0.09, 57.04) 0.608 1.57 (0.06, 39.50) 0.784 1.50 (0.99, 2.28) 0.057 2.20 (0.09, 54.12) 0.630
MassARRAY 5 2386 3548 0.54 (0.28, 1.07) 0.076 0.48 (0.34, 0.68) <0.001 0.70 (0.41, 1.19) 0.187 0.43 (0.17, 1.09) 0.076 0.53 (0.37, 0.74) <0.001
HWE
Total in HWE 8 4365 6412 0.79 (0.53, 1.19) 0.259 0.73 (0.55, 0.95) 0.022 0.85 (0.55, 1.30) 0.445 0.69 (0.40, 1.18) 0.171 0.75 (0.57, 0.98) 0.037
Arg280His (G>A) Allele G vs. A GG vs. AA GA vs. AA GG vs. GA+AA GG+GA vs. AA
Total 8 3643 4730 1.01 (0.83, 1.22) 0.943 1.07 (0.66, 1.75) 0.780 1.01 (0.77, 1.32) 0.967 1.01 (0.84, 1.22) 0.916 1.06 (0.67, 1.69) 0.806
Ethnicity
Asian 6 2602 2726 0.96 (0.75, 1.23) 0.751 1.04 (0.60, 1.78) 0.892 0.99 (0.76, 1.30) 0.954 0.96 (0.76, 1.22) 0.765 1.03 (0.62, 1.72) 0.906
Caucasian 2 1041 2004 1.15 (0.90, 1.47) 0.261 1.96 (0.32, 11.97) 0.466 1.72 (0.28, 10.70) 0.558 1.14 (0.89, 1.48) 0.298 1.94 (0.32, 11.82) 0.474
Control source
Hospital 6 2602 2726 0.96 (0.75, 1.23) 0.751 1.04 (0.60, 1.78) 0.892 0.99 (0.76, 1.30) 0.954 0.96 (0.76, 1.22) 0.765 1.03 (0.62, 1.72) 0.906
Population 1 701 1538 1.10 (0.82, 1.46) 0.537 1.84 (0.21, 16.48) 0.586 1.71 (0.19, 15.56) 0.635 1.09 (0.81, 1.47) 0.578 1.83 (0.20, 16.36) 0.591
Genotyping method
PCR-PFLP 7 3303 3684 0.98 (0.80, 1.21) 0.854 1.06 (0.64, 1.77) 0.821 1.00 (0.76, 1.31) 0.993 0.98 (0.81, 1.20) 0.877 1.05 (0.65, 1.71) 0.840
Taqman 1 340 1026 1.32 (0.82, 2.12) 0.250 2.25 (0.09, 55.31) 0.621 1.76 (0.07,44.74) 0.731 1.31 (0.80, 2.13) 0.278 2.19 (0.09, 54.03) 0.631
HWE
Total in HWE 5 2262 3249 0.95 (0.76, 1.18) 0.638 1.02 (0.44, 2.37) 0.970 1.15 (0.63, 2.11) 0.642 0.94 (0.77, 1.16) 0.576 1.04 (0.45, 2.36) 0.933
Arg399Gln (G>A) Allele G vs. A GG vs. AA GA vs. AA GG vs. GA+AA GG+GA vs. AA
Total 18 6890 9281 0.93 (0.82, 1.05) 0.239 0.85 (0.66, 1.09) 0.201 0.98 (0.79, 1.22) 0.854 0.87 (0.77, 0.98) 0.026 0.92 (0.73, 1.16) 0.463
Ethnicity
Asian 8 3341 3583 0.78 (0.72, 0.84) <0.001 0.56 (0.47, 0.66) <0.001 0.71 (0.59, 0.84) <0.001 0.76 (0.68, 0.84) <0.001 0.62 (0.53, 0.73) <0.001
Caucasian 9 3469 5598 1.08 (0.89, 1.32) 0.413 1.20 (0.81, 1.77) 0.368 1.18 (0.86, 1.63) 0.313 1.03 (0.85, 1.25) 0.772 1.19 (0.84, 1.69) 0.337
Control source
Hospital 11 3884 4305 0.93 (0.76, 1.14) 0.489 0.86 (0.56, 1.32) 0.486 0.95 (0.68, 1.33) 0.764 0.88 (0.72, 1.08) 0.217 0.89 (0.61, 1.32) 0.573
Population 5 1653 2527 0.93 (0.79, 1.10) 0.398 0.85 (0.64, 1.11) 0.232 1.02 (0.66, 1.58) 0.933 0.91 (0.79, 1.05) 0.182 0.95 (0.65, 1.37) 0.769
Genotyping method
PCR-PFLP 13 4606 5825 0.93 (0.78, 1.10) 0.391 0.82 (0.58, 1.16) 0.257 0.95 (0.70, 1.29) 0.744 0.87 (0.73, 1.03) 0.114 0.88 (0.64, 1.22) 0.453
Taqman 3 1468 2649 0.99 (0.85, 1.16) 0.932 1.08 (0.73, 1.62) 0.691 1.17 (0.84, 1.62) 0.359 0.91 (0.80, 1.03) 0.139 1.13 (0.79, 1.63) 0.498
MassARRAY 2 816 807 0.84 (0.72, 0.98) 0.025 0.71 (0.50, 1.01) 0.060 0.88 (0.62, 1.25) 0.464 0.81 (0.67, 0.99) 0.037 0.79 (0.57, 1.10) 0.161
HWE
Total in HWE 13 5056 7336 0.96 (0.83, 1.12) 0.639 0.92 (0.67, 1.26) 0.598 0.97 (0.75, 1.26) 0.841 0.92 (0.79, 1.07) 0.276 0.95 (0.71, 1.26) 0.704
Glu491Lys (G>A) Allele G vs. A GG vs. AA GA vs. AA GG vs. GA+AA GG+GA vs. AA
Total 1 629 641 0.81 (0.69, 0.96) 0.017 0.53 (0.35, 0.80) 0.003 0.57 (0.37, 0.86) 0.008 0.85 (0.68, 1.06) 0.156 0.55 (0.37, 0.82) 0.003
Pro161Leu (C>T) Allele C vs. T CC vs. TT CT vs. TT CC vs. CT+TT CC+CT vs. TT
Total 1 638 648 0.77 (0.65, 0.91) 0.003 0.45 (0.30, 0.68) <0.001 0.48 (0.31, 0.72) 0.001 0.83 (0.67, 1.03) 0.093 0.46 (0.31, 0.69) <0.001
Leu387Leu (G>A) Allele G vs. A GG vs. AA GA vs. AA GG vs. GA+AA GG+GA vs. AA
Total 1 638 648 0.84 (0.71, 0.99) 0.040 0.62 (0.43, 0.90) 0.012 0.64 (0.44, 0.94) 0.022 0.88 (0.71, 1.10) 0.262 0.63 (0.44, 0.90) 0.012
Pro602Thr (C>A) Allele C vs. A CC vs. AA CA vs. AA CC vs. CA+AA CC+CA vs. AA
Total 1 638 648 0.76 (0.64, 0.90) 0.001 0.47 (0.32, 0.69) <0.001 0.52 (0.35, 0.76) 0.001 0.80 (0.65, 1.00) 0.051 0.49 (0.34, 0.71) <0.001
Thr304Ala (A>G) Allele A vs. G AA vs. GG AG vs. GG AA vs. AG+GG AA+AG vs. GG
Total 1 620 630 0.77 (0.65, 0.91) 0.003 0.51 (0.34, 0.75) 0.001 0.56 (0.38, 0.83) 0.005 0.80 (0.64, 1.00) 0.053 0.53 (0.36, 0.78) 0.001
Ser593Arg (C>G) Allele C vs. G CC vs. GG CG vs. GG CC vs. CG+GG CC+CG vs. GG
Total 1 620 630 0.84 (0.71, 1.00) 0.045 0.56 (0.36, 0.86) 0.008 0.58 (0.38, 0.90) 0.015 0.88 (0.70, 1.10) 0.254 0.94 (0.65, 1.36) 0.745
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XRCC1 Arg194Trp (C>T) polymorphism

A total of 14 case-control studies including 6083 glioma cases and 8437 controls were pooled to reach a result. Arg194Trp (C>T) was observed to be associated with increased glioma risk in the allele model, homozygous co-dominant model, dominant model and recessive model (allele C versus T: OR=0.72, 95% CI=0.55-0.93, CC versus TT: OR=0.55, 95% CI=0.46-0.67; CC versus CT+TT: OR=0.64, 95% CI=0.45-0.91 and CC+CT vs. TT: OR=0.61, 95% CI=0.51-0.74). Among the subgroup analysis, Asian ethnicity (allele C versus T: OR=0.82, 95% CI= 0.74-0.92; CC versus TT: OR=0.59, 95% CI=0.48-0.72; CT versus TT: OR=0.66, 95% CI= 0.53-0.81; CC versus CT+TT: OR=0.86, 95% CI=0.78-0.95 and CC+CT vs. TT: OR=0.61, 95% CI=0.50-0.74), hospital-based source of control (allele C versus T: OR=0.82, 95% CI=0.74-0.92; CC versus TT: OR=0.59, 95% CI=0.48-0.72; CT versus TT: OR=0.66, 95% CI= 0.53-0.81; CC versus CT+TT: OR=0.86, 95% CI=0.78-0.95 and CC+CT versus TT: OR=0.61, 95% CI=0.50-0.74), PCR-PFLP genotyping method (allele C versus T: OR=0.80, 95% CI=0.66-0.98; CC versus TT: OR=0.58, 95% CI=0.46-0.73 and CC+CT vs. TT: OR=0.65, 95% CI=0.52-0.81), Hardy-Weinberg equilibrium (CC versus TT: OR=0.73, 95% CI=0.55-0.95 and CC+CT vs. TT: OR=0.75, 95% CI=0.57-0.98) subgroups showed statistical significance for higher risk in glioma cases group. For Caucasian ethnicity (allele C versus T: OR=0.66, 95% CI=0.23-1.91; CC versus TT: OR=0.68, 95% CI=0.27-1.70; CT versus TT: OR=1.22, 95% CI=0.28-5.66; CC versus CT+TT: OR=0.48, 95% CI=0.12-1.90 and CC+CT versus TT: OR=0.87, 95% CI=0.35-2.14), XRCC1 Arg194Trp (C>T) polymorphism was unproved to be associated with glioma risk. The detail data was showed in Figure 2 and Table 3.

Figure 2.

Figure 2

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Forest plots of ORs with 95% CI for the association between XRCC1 Arg194Trp (C>T) polymorphism and glioma risk observed in subgroup analysis by ethnicity (A: CC vs. CT+TT; B: CC+CT vs. TT). The squares and horizontal lines correspond to the study specific OR and 95% CI. The area of the squares reflects the study-specific weight. The diamond represents the pooled OR and 95% CI.

XRCC1 Arg280His (G>A) polymorphism

A total of 8 case-control studies including 3643 glioma cases and 4730 controls were pooled to reach a result. According to our research result, XRCC1 Arg280His (G>A) polymorphism was not associated with increased or decreased glioma risk (allele G versus A: OR=1.01, 95% CI=0.83-1.22; GG versus AA: OR=1.07, 95% CI=0.66-1.75; GA versus AA: OR=1.01, 95% CI=0.77-1.32; GG versus GA+AA: OR=1.01, 95% CI=0.84-1.22 and GG+GT versus AA: OR=1.06, 95% CI=0.67-1.69). Subgroup analysis did not showed any significance between Arg280His (G>A) polymorphism and glioma risk as well. Detail data was showed in Figure 3 and Table 3.

Figure 3.

Figure 3

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Forest plots of ORs with 95% CI for the association between XRCC1 Arg280His (G>A) polymorphism and glioma risk observed in subgroup analysis by ethnicity (A: GG vs. GA+AA; B: GG+GA vs. AA). The squares and horizontal lines correspond to the study specific OR and 95% CI. The area of the squares reflects the study-specific weight. The diamond represents the pooled OR and 95% CI.

XRCC1 Arg399Gln (G>A) polymorphism

A total of 18 case-control studies including 6890 glioma cases and 9281 controls were pooled in this genetic polymorphism analysis. Arg399Gln (G>A) was observed to be associated with increased glioma risk in the dominant model (GG versus GA+AA: OR=0.87, 95% CI=0.77-0.98). Among the subgroup analysis, Asian ethnicity (allele G versus A: OR=0.78, 95% CI=0.72-0.84; GG versus AA: OR=0.56, 95% CI=0.47-0.66; GA versus AA OR=0.71, 95% CI=0.59-0.84; GG versus GA+AA: OR=0.76, 95% CI=0.68-0.84 and GG+GA vs. AA: OR=0.62, 95% CI=0.53-0.73), Mass ARRAY genotyping method (Allele G vs. A: OR=0.84, 95% CI=0.72-0.98; and GG vs. GA+AA: OR=0.81, 95% CI=0.67-0.99) subgroups showed higher risk in glioma cases group significantly. Data showed that Arg399Gln (G>A) was not associated with Caucasian ethnicity (allele G versus A: OR=1.08, 95% CI=0.89-1.32; GG versus AA: OR=1.20, 95% CI=0.81-1.77; GA versus AA: OR=1.18, 95% CI=0.86-1.63; GG versus GA+AA: OR=1.031, 95% CI=0.85-1.25 and GG+GT versus AA: OR=1.19, 95% CI=0.84-1.69). Detail data was showed in Figure 4 and Table 3.

Figure 4.

Figure 4

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Forest plots of ORs with 95% CI for the association between XRCC1 Arg399Gln (G>A) polymorphism and glioma risk observed in subgroup analysis by ethnicity (A: GG vs. GA+AA; B: GG+GA vs. AA). The squares and horizontal lines correspond to the study specific OR and 95% CI. The area of the squares reflects the study-specific weight. The diamond represents the pooled OR and 95% CI.

Other XRCC1 polymorphisms

Only one case-control study with an Asian ethnicity involving 629 glioma cases and 641 controls observed the association between XRCC1 Glu491Lys (G>A) and glioma risk. The result revealed that XRCC1 Glu491Lys (G>A) genetic mutation would increase glioma risk in allele model, co-dominant model and recessive model (allele G versus A: OR=0.81, 95% CI=0.69-0.96; GG versus AA: OR=0.53, 95% CI=0.35-0.80; GA versus AA: OR=0.57, 95% CI=0.37-0.86; GG+GA versus AA: OR=0.55, 95% CI=0.37-0.82). One case-control study with an Asian ethnicity involving 620 glioma cases and 630 controls observed the association between XRCC1 Thr304Ala (A>G) and Ser593Arg (C>G) and glioma risk. Results showed that XRCC1 Thr304Ala (A>G) would increase glioma risk in allele model, co-dominant model and recessive model (allele A versus G: OR=0.77, 95% CI=0.65-0.91; AA versus GG: OR=0.52, 95% CI=0.34-0.75; AG versus GG: OR=0.56, 95% CI=0.38-0.83; AA+AG versus GG: OR=0.53, 95% CI=0.36-0.78) and XRCC1 Ser593Arg (C>G) would increase glioma risk in allele model and co-dominant model (allele C versus G: OR=0.84, 95% CI=0.71-1.00; CC versus GG: OR=0.56, 95% CI=0.36-0.86; CG versus GG: OR=0.58, 95% CI=0.38-0.90). And one case-control study with an Asian ethnicity involving 638 glioma cases and 648 controls observed the association between XRCC1 Pro161Leu (C>T), Leu387Leu (G>A) and Pro602Thr (C>A) and glioma risk. Results showed that glioma risk would be increased by XRCC1 Pro161Leu (C>T) (allele C versus T: OR=0.77, 95% CI=0.65-0.91; CC versus TT: OR=0.45, 95% CI=0.30-0.68; CT versus TT: OR=0.48, 95% CI=0.31-0.72; CC+CT versus TT: OR=0.46, 95% CI=0.31-0.69), Leu387Leu (G>A) (allele G versus A: OR=0.84, 95% CI=0.71-0.99; GG versus AA: OR=0.62, 95% CI=0.43-0.90; GA versus AA: OR=0.64, 95% CI=0.44-0.94; GG+GA versus AA: OR=0.63, 95% CI=0.44-0.90) and Pro602Thr (C>A) (allele C versus A: OR=0.76, 95% CI=0.64-0.90; CC versus AA: OR=0.47, 95% CI=0.32-0.69; CA versus AA: OR=0.52, 95% CI=0.35-0.76; CC+CA versus AA: OR=0.49, 95% CI=0.34-0.71) genetic mutations. Detail data was showed in Table 3.

Sensitivity analysis and publication bias

Sensitivity analysis was preformed repeatedly after removal of each particular study. The overall statistical significance of the results in dominant model was not changed after each removal, suggesting that our results were stability and liability statistically. The sensitivity analysis results were showed in Figure 5. Both Begg’s funnel plot and Egger’s test were performed to assess the publication bias of included studies. The shape of both funnel plots in recessive model seemed asymmetrical, suggesting the absence of publication. The Egger’s test was used to provide statistical evidence of funnel plot asymmetry, and the results showed no significant publication bias. Begg’s funnel plots were showed in Figure 6, and results of Egger’s test were showed in Table 2.

Figure 5.

Figure 5

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Outlier analysis for XRCC1 Arg399Gln (G>A) polymorphism illustrating the influence of each study. Each horizontal line represents specific OR and 95% CI with a deletion of specific study.

Figure 6.

Figure 6

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Funnel plots for XRCC1 Arg194Trp (C>T) and Arg399Gln (G>A) polymorphisms and glioma risk for publication bias test. Each circle represents a separate study for the indicated association. A: XRCC1 Arg194Trp (CC+CT vs. TT); B: Arg399Gln (GG vs. AA+GA).

Discussion

Glioma accounts for about 44% of all primary central nervous system tumors and of these the most common is the highly malignant glioblastoma multiforme [40]. Despite significant improvements in the diagnosis and treatment for patients with glioma, this primary brain tumor remains essentially incurable. Although surgery and radiotherapy substantially improve patient survival, 95% of patients have a mean survival of less than 2 years following diagnosis [41]. Due to the poor prognosis, making early diagnosis may be an effective approach to improve and extend glioma patients’ life by individualized or personalized therapy. Genetic biomarker is useful to early diagnosis. Thus, to find some glioma relevant genetic biomarkers is important to improve the prognosis. Single nucleotide polymorphism is one of the most effective approaches to identify the genetic risks. According to present studies, the common single nucleotide polymorphisms including XRCC, ERCC (The excision repair cross-complementing rodent repair deficiency complementation), VEGFR (vascular endothelial growth factor receptor), MMP (Matrix metallopeptidases), RTEL1 (regulator of telomere elongation helicase 1), TERT (telomerase reverse transcriptase) and NEIL3 (nei endonuclease VIII-like 3) affect the prognosis of glioma.

XRCC1 gene is one of the most common genes which mutation could lead to a variety of tumors and many other diseases. XRCC1 which locates on chromosome 19q13.2 and encodes a 70 KD enzyme involved in the BER pathway and non-bulky adducts oxidative damage, alkylation, methylation and also acts as an alternative route of DNA double-strand break (DSB) nonhomologous end-rejoining [42,43]. There are three most common polymorphisms of XRCC1 including Arg194Trp (C>T) (rs1799782), Arg280His (G>A) (rs25489) and Arg399Gln (G>A) (rs25487) have drawn attention because of their potential associations with the development of glioma. Recently, more genetic mutation of XRCC1 polymorphisms were observed to be associated with increased glioma risk including XRCC1 Glu491Lys (G>A), Pro161Leu (C>T), Leu387Leu (G>A), Pro602Thr (C>A), Thr304Ala (A>G) and Ser593Arg (C>G).

Several previous studies have investigated associations between XRCC1 Arg194Trp (C>T), Arg280His (G>A) and Arg399Gln (G>A) and glioma risk. The Arg194Trp SNP is located in an evolutionary conserved linker region and makes the chances of occurrence of chromosomal breaks highly increased [44]. Based on previous studies, it is still unclear whether XRCC1 Arg194Trp (C>T) polymorphism is associated with the risk of glioma. Several individual studies, such as Kiuru et al [28], Rajaraman et al [33], reported that XRCC1 Arg194Trp polymorphism is not associated with glioma. On the contrary, three individual studies [11,32,37] concluded that XRCC1 Arg194Trp increased the risk of glioma. In addition, a meta-analysis [12] including only four literatures failed to suggest an association of the XRCC1 Arg194Trp polymorphism with glioma risk (allele T versus C: OR=1.01, 95% CI=0.77-1.33), as well as another meta-analysis [45] (allele T versus C: OR=1.00, 95% CI=0.89-1.13). However, a meta-analysis [46] showed that the XRCC1 Arg194Trp polymorphism is a genetic risk factor for glioma, especially in Asian population (CC versus TT+CT: OR=1.92, 95% CI=1.58-2.34). Three meta-analysis [7,13,47] including only 4 literatures researched association between XRCC1 Arg280His (G>A) and glioma risk and draw a consistent conclusion that Arg280His genetic mutation would not increase or decrease risk of glioma as well as our present research including 8 literatures. As for the individual studies about XRCC1 Arg399Gln (G>A), Yosunkaya et al [38] held the notion that XRCC1 Arg399Gln polymorphism would decrease glioma risk. In contrast, most studies [26,29,35,37,39] observed an increased glioma risk about Arg399Gln (G>A) genetic mutation. And Wang et al [25,36] demonstrated that no risk of glioma in individuals with the Arg399Gln (G>A). Jacobs et al [14] did a meta-analysis about Caucasian ethnicity and concluded that no risk of glioma in Caucasians after XRCC1 Arg399Gln mutation. Wei et al [15] concluded that XRCC1 Arg399Gln mutation would lead to a potential risk in Asians but not in Caucasians, and Jiang et al [45] concluded that the mutation increased risk in both Asians and Caucasians. However, Zhu et al [48] reported that XRCC1 Arg399Gln mutation would decrease the risk of glioma in Asians. There was only a single case-control study researched XRCC1 Glu491Lys (G>A), Pro161Leu (C>T), Leu387Leu (G>A), Pro602Thr (C>A), Thr304Ala (A>G) and Ser593Arg (C>G) in glioma respectively. On account of the small sample and contradictory conclusions of previous studies, we performed a systematic review and meta-analysis to assess the association between XRCC1 polymorphism and glioma risk.

To our knowledge, this was the most comprehensive meta-analysis addressing the association between XRCC1 polymorphisms and glioma risk. In this study, a total of 22 studies including 18503 glioma cases and 24367 controls were reviewed. The research object was Asian in 12 studies and was Caucasian in 9 studies and only one study researched a mixed object. In the case group, most patients were about fifty years old and with a tumor grade of WHO III-IV. The source of controls derived from population or hospital. The pooled results showed that XRCC1 Arg194Trp polymorphism will significantly increased glioma risk (allele C versus T: OR=0.72, 95% CI=0.55-0.93, CC versus TT: OR=0.55, 95% CI=0.46-0.67; CC versus CT+TT: OR=0.64, 95% CI=0.45-0.91 and CC+CT vs. TT: OR=0.61, 95% CI=0.51-0.74), especially in Asian ethnicity (allele C versus T: OR=0.82, 95% CI=0.74-0.92; CC versus TT: OR=0.59, 95% CI=0.48-0.72; CT versus TT: OR=0.66, 95% CI=0.53-0.81; CC versus CT+TT: OR=0.86, 95% CI=0.78-0.95 and CC+CT vs. TT: OR=0.61, 95% CI=0.50-0.74). XRCC1 Arg280His (G>A) polymorphism has no association with glioma (allele G versus A: OR=1.01, 95% CI=0.83-1.22; GG versus AA: OR=1.07, 95% CI=0.66-1.75; GA versus AA: OR=1.01, 95% CI=0.77-1.32; GG versus GA+AA: OR=1.01, 95% CI=0.84-1.22 and GG+GT versus AA: OR=1.06, 95% CI=0.67-1.69). XRCC1 Arg399Gln (G>A) polymorphism will significantly increase glioma risk in Asian (allele G versus A: OR=0.78, 95% CI=0.72-0.84; GG versus AA: OR=0.56, 95% CI=0.47-0.66; GA versus AA OR=0.71, 95% CI=0.59-0.84; GG versus GA+AA: OR=0.76, 95% CI=0.68-0.84 and GG+GA vs. AA: OR=0.62, 95% CI=0.53-0.73) but not Caucasian ethnicity. XRCC1 Pro161Leu (C>T), Leu387Leu (G>A), Pro602Thr (C>A), Ser593Arg (C>G) and Glu491Lys (G>A) polymorphisms will increase glioma risk in different degrees.

Similar to other Mata-analysis, our study has some limitations. Firstly, in the present meta-analysis, the primary studies only provided data regarding Caucasians and Asians. Other ethnicities such as African should be investigated in future studies. Secondly, only one literature with Asian ethnicity observed the association between XRCC1 Pro161Leu (C>T), Leu387Leu (G>A), Pro602Thr (C>A), Ser593Arg (C>G) and Glu491Lys (G>A) polymorphisms and glioma risk and the conclusion should be confirmed by more researches in future. Thirdly, as we know, glioma is a multifactor disease, the gene-gene and gene-environment interactions should be taken into account. Fourthly, subgroup analyses based on age, gender, histological types, radiation exposure, smoking, alcohol drinking and other factors have not been performed in the present study since sufficient relevant data were not available in the primary literature. Lastly, only studies published in English or Chinese were retrieved, which lead to publication bias and selection bias. Therefore, the conclusion of present study should be accepted with caution.

In conclusion, our meta-analysis revealed an association between XRCC1 Arg194Trp and XRCC1 Arg399Gln (G>A) polymorphisms and the susceptibility to glioma in Asian but not Caucasian population. XRCC1 Glu491Lys (G>A), Pro161Leu (C>T), Leu387Leu (G>A), Pro602Thr (C>A), Thr304Ala (A>G) and Ser593Arg (C>G) polymorphisms will increase glioma risk. XRCC1 Arg280His (G>A) is irrelevant to the glioma risk. Due to the limitation and the potential confounders, more studies are needed to confirm our conclusion.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 81372683). We thank all our colleagues working in the Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China.

Disclosure of conflict of interest

None.

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