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. 2021 Apr 30;14:117. doi: 10.1186/s12920-021-00965-4

The association between XRCC3 rs1799794 polymorphism and cancer risk: a meta-analysis of 34 case–control studies

Weiqing Liu 1, Shumin Ma 2, Lei Liang 2, Zhiyong Kou 2, Hongbin Zhang 2, Jun Yang 2,✉
PMCID: PMC8086287  PMID: 33931047

Abstract

Background

Studies on the XRCC3 rs1799794 polymorphism show that this polymorphism is involved in a variety of cancers, but its specific relationships or effects are not consistent. The purpose of this meta-analysis was to investigate the association between rs1799794 polymorphism and susceptibility to cancer.

Methods

PubMed, Embase, the Cochrane Library, Web of Science, and Scopus were searched for eligible studies through June 11, 2019. All analyses were performed with Stata 14.0. Subgroup analyses were performed by cancer type, ethnicity, source of control, and detection method. A total of 37 studies with 23,537 cases and 30,649 controls were included in this meta-analysis.

Results

XRCC3 rs1799794 increased cancer risk in the dominant model and heterozygous model (GG + AG vs. AA: odds ratio [OR] = 1.04, 95% confidence interval [CI] = 1.00–1.08, P = 0.051; AG vs. AA: OR = 1.05, 95% CI = 1.00–1.01, P = 0.015). The existence of rs1799794 increased the risk of breast cancer and thyroid cancer, but reduced the risk of ovarian cancer. In addition, rs1799794 increased the risk of cancer in the Caucasian population.

Conclusion

This meta-analysis confirms that XRCC3 rs1799794 is related to cancer risk, especially increased risk for breast cancer and thyroid cancer and reduced risk for ovarian cancer. However, well-designed large-scale studies are required to further evaluate the results.

Keywords: Rs1799794, Polymorphism, Cancer, Risk, Meta-analysis

Background

Cancer is the leading cause of death worldwide, and the number of patients with cancer is increasing [1]. The occurrence of cancer is related to many factors, including environmental, lifestyle, genetic and other factors. Among them, gene mutation is a kind of genetic factor, which has a great influence on cancer risk [2]. The mutation in BRCA1 and BRCA2 is related to the increase risk of breast cancer [3]. XPF rs2276466 polymorphism is related to neurogenic cancer [4].

X-ray repair cross-complementing group 3 (XRCC3), functions in the homologous recombination (HR) repair of DNA crosslinks [5] and double-strand breaks [6]. Based on the function of XRCC3, XRCC3 gene mutations are related to the occurrence and development of many diseases. For example, XRCC3 241Thr/Met genotype promotes left ventricular hypertrophy by inhibiting DNA damage repair [7]. Mutations in the XRCC3 gene affect mitochondrial DNA integrity [8]. XRCC3 rs861539 polymorphism is associated with poor prognosis of breast cancer patients [9]. The mutation sites that have been studied more about the relationship between XRCC3 gene and cancer are rs861539, rs1799794 and rs1799796 [10]. However, results remain fairly conflicting rather than conclusive. A number of meta-analyses have investigated the relationship between rs861539 and susceptibility to various cancers [11–33]. However, there have been few meta-studies on rs1799794 and susceptibility to cancer [28, 30, 31, 33, 34]. Therefore, we conducted this meta-analysis to analyze the relationship between rs1799794 and susceptibility to cancer on the basis of more data.

Methods

Search strategies

We comprehensively searched five databases (PubMed, Embase, the Cochrane Library, Web of Science, and Scopus) for research published as of June 11, 2019, using relevant MeSH terms and entry terms. The keywords of XRCC3 included X-ray repair cross complementing 3, rs1799794, 4541A/G, XRCC3. The MeSH term and entry terms of polymorphism were genetic polymorphism [MeSH terms]; polymorphisms, genetic; genetic polymorphisms; genetic polymorphism; polymorphism (genetics); polymorphisms (genetics); polymorphism, single nucleotide; nucleotide polymorphism, single; nucleotide polymorphisms, single; polymorphisms, single nucleotide; single nucleotide polymorphisms; polymorphisms; polymorphism; variant; mutation; single nucleotide polymorphism; SNP. The MeSH term and entry terms of cancer were neoplasm [MeSH terms], neoplasms, neoplasia, neoplasias, neoplasm, tumors, tumor, cancer, cancers, carcinoma, carcinogenesis, tumour. Furthermore, we refined the search results of related studies by looking at the list of references included in each article.

Selection criteria

Relevant studies were included in accordance with the inclusion criteria and exclusion criteria, which were similar to those described in the previous study (PMID: 30867406). Original case–control study focused on the relationship between rs1799794 and cancer risk with the frequency of XRCC3 rs1799794 mutant genotypes were included. While conference abstracts or reports, reviews or meta-analyses, republished articles, and studies with insufficient data were excluded.

Data extraction and quality assessment

The following data from each selected article were collected: the surname of the first author, the publication year, country, ethnicity, cancer types, and methods of genotyping XRCC3 rs1799794 polymorphism. The quality of eligible case–control studies was estimated using the Newcastle–Ottawa Scale [35].

Statistical analysis

The relationship between XRCC3 rs1799794 polymorphisms and cancer risk were evaluated using odds ratios (ORs) and 95% confidence intervals (CI) under five genetic models (G vs. A, GG vs. AA, GG + GA vs. AA, GG vs. GA + AA, GA vs. AA).as previous study. If P < 0.05 or the 95% CI did not include 1, the result was considered statistically significant. Cochran’s Q with chi-square (with PQ) and the Higgins I2 test were used to determine heterogeneity in between-study variability. When PQ < 0.1 or I2 > 25% indicated significant heterogeneity [36–38], we analyzed the data using a random effects model [39]. If the opposite held, a fixed effects model was chosen. We also performed subgroup analyses and a sensitivity analysis to explore sources of heterogeneity. Subgroup analyses stratified studies by cancer type (ovarian cancer, acute lymphoblastic leukemia, breast cancer, thyroid cancer, bladder cancer, lung cancer, other), ethnicity (Arabian, Asian, Caucasian, mixed), sample size (< 100, > 100), the publication year (≤ 2010, > 2010), detection method (PCR–RFLP, sequencing, TaqMan, PCR, ND, other), and source of control (HB, PB, mixed, nested). We assessed publication bias using funnel plots and Egger’s test (P < 0.05). Statistical calculations were performed with Stata 14.0.

Results

Literature search and study characteristics

Finally, 3,467 potentially relevant published works were identified (997 in PubMed, 27 in the Cochrane library, 855 in Embase, 696 in Scopus, and 889 in Web of Science). Of these, duplicates (1959) and works not related to cancer and rs1799794 polymorphism (1451) were excluded. Then 23 of these studies were excluded after reviewing full texts. The remaining 37 works (43 studies) were included in this meta-analysis [10, 40–75]. Because two studies in Auranen et al. [10] were duplicated in Quaye et al. [62], we only extracted data from these studies from Auranen et al. [10] to avoid duplication; thus, one article included four studies [66], and three articles included two studies each [10, 68, 70]. The flow chart of the literature selection process is shown in Fig. 1.

Fig. 1.

Fig. 1

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Flow chart of study selection

There were a total of 23,537 cases and 30,649 controls in these 37 works, and 3 were conducted among Arabians [40, 48, 55], 14 among Asians [41, 42, 45–47, 49, 50, 53, 54, 56, 58, 59, 66, 67], and 24 among Caucasians [10, 43, 44, 51, 52, 57, 60–62, 64, 66, 69–75]; 2 were conducted among mixed populations [63, 65]. In addition, in terms of cancer type, ovarian cancer (n = 4) [10, 40, 62], acute lymphoblastic leukemia (n = 3) [41, 52, 57], breast cancer (n = 13) [44, 48, 49, 55, 61, 66, 68, 72, 74], thyroid cancer (n = 4) [42, 46, 47, 67], bladder cancer (n = 4) [45, 63, 65, 69], lung cancer (n = 3) [53, 59, 71], and other cancer (hepatocellular cancer, leiomyoma, nasopharyngeal carcinoma, osteosarcoma, oral cancer, glioma, head and neck cancer, myeloma, endometrial cancer, colorectal adenoma, melanoma skin cancer) [43, 50, 51, 54, 56, 58, 60, 64, 70, 73, 75] were studied. The basic information of each study is presented in Table 1. And we took sensitivity analysis for studies that do not conform to HWE.

Table 1.

Characteristics of the individual studies included in the meta-analysis

Author Year Country Ethnicity Cancer type Genotyping method Control Cases/control Cases Control HWE NOS
aa ag gg aa ag gg
Mackawy 2019 Egypt Arabian Ovarian carcinoma PCR–RFLP HB 50/20 14 20 16 4 6 10 0.128 6
Pei 2018 China Asian Acute lymphoblastic leukemia PCR–RFLP PB 266/266 55 144 67 53 150 63 0.035 7
Al Zoubi 2017 Italy Caucasian Breast cancer Sequencing HB 23/16 8 14 1 11 5 0 0.459 7
De Mattia 2017 Italy Caucasian Hepatocellular cancer TaqMan HB 192/192 128 52 12 137 49 5 0.806 7
Sarwar 2017 Pakistan Asian Thyroid cancer ARMS-PCR HB 456/400 289 90 77 297 65 38  < 0.001 7
Yan 2016 China Asian Yhyroid carcinoma PCR HB 276/403 116 127 33 202 161 40 0.345 8
Zhu 2016 China Asian Bladder cancer TaqMan HB 184/260 72 53 59 69 142 49 0.111 7
Ali 2016 Saudi Arabia Arabian Breast cancer PCR–RFLP HB 143/145 102 40 1 93 28 24  < 0.001 7
Chang 2015 China Asian Leiomyoma PCR–RFLP HB 166/474 35 91 40 93 268 113 0.004 7
Chen 2015 China Asian Lung cancer PCR–RFLP HB 358/716 70 202 86 147 395 177 0.007 7
Liu 2015 China Asian Nasopharyngeal carcinoma PCR–RFLP HB 176/880 33 99 44 179 489 212 0.001 7
Su 2015 China Asian Breast cancer PCR–RFLP HB 1232/1232 239 696 297 254 668 310 0.002 8
Al Zoubi 2015 Jordan Arabian Breast cancer Sequencing HB 46/31 16 28 2 21 9 1 0.976 7
Yuan 2015 China Asian Papillary thyroid cancer PCR HB 183/367 77 84 22 184 147 36 0.406 6
Goričar 2015 Slovenia Caucasian Acute lymphoblastic leukemia TaqMan PB 121/184 89 117  ≥ 0.050 8
Goričar 2015 Slovenia Caucasian Osteosarcoma TaqMan PB 79/373 47 247 ND 7
Smolkova 2014 Germany Caucasian Acute lymphoblastic leukaemia TaqMan HB 460/547 286 155 19 340 183 24 0.921 7
TSAI 2014 China Asian Oral cancer PCR–RFLP HB 788/956 155 438 195 195 532 229  < 0.001 7
He 2013 China Asian Lung cancer PCR HB 507/661 180 230 97 184 313 164 0.181 7
Zhao 2013 China Asian Glioma TaqMan HB 384/384 83 201 100 95 181 108 0.271 7
Gresner 2012 Poland Caucasian Head and neck cancer PCR–RFLP PB 81/100 45 31 5 59 34 7 0.497 8
VRAL 2011 Belgium Caucasian Breast cancer PCR–RFLP or SnapShot technique HB 343/172 220 108 15 117 52 3 0.304 5
Quaye 2009 mixed Caucasian Ovarian cancer TaqMan PB 1461/2307 940 484 37 1505 713 89 0.691 8
Andrew 2009 USA Mixed Bladder cancer PCR–RFLP PB 342/559 190 333 ND 7
Hayden 2007 Germany, Italy, Spain, Ireland,France, Czech Republic and the Irish Caucasian Myeloma TaqMan Mixed 302/257 189 100 13 153 91 13 0.911 8
Ni 2006 China Asian Thyroid carcinoma PCR–RFLP HB 191/201 66 81 44 62 94 45 0.411 7
Garcıa-Closas 2006 Poland Caucasian Breast cancer ND PB 1920/2218 1210 632 78 1386 736 96 0.891 8
Garcıa-Closas 2006 USA Caucasian Breast cancer ND PB 1564/1264 980 521 63 837 357 52 0.079 8
Wu 2006 USA Mixed Bladder cancer PCR–RFLP HB 599/595 402 185 12 398 185 12 0.072 8
Paul Pharoah ICRC-Thai 2006 Thailand Asian Breast cancer ND PB 465/389 153 217 95 135 182 72 0.441 8
Paul Pharoah SEARCH 2006 UK Caucasian Breast cancer ND PB 2790/3642 1808 889 93 2388 1113 141 0.427 8
Paul Pharoah Sheffield 2006 UK Caucasian Breast cancer ND HB 1185/1159 781 369 35 755 353 51 0.238 8
Paul Pharoah USRTS 2006 USA Caucasian Breast cancer ND Nested 718/1049 458 224 36 650 356 43 0.509 8
Auranen1 2005 US FROC Caucasian Ovarian cancer TaqMan PB 325/417 204 112 9 267 133 17 0.932 8
Auranen2 2005 UK RMH/YOV Caucasian Ovarian cancer TaqMan PB 301/1808 194 95 12 1196 535 77 0.083 8
Matullo 2005 Italy Caucasian Bladder cancer PCR–RFLP HB 316/315 207 98 11 201 102 12 0.833 7
Han 2004 USA Caucasian Breast cancer TaqMan PB 991/1291 630 322 39 865 372 54 0.084 8
Han 2004 USA Caucasian Endometrial cancer TaqMan PB 220/663 140 73 7 438 200 25 0.716 8
Jacobsen 2004 Denmark Caucasian Lung cancer PCR Nested 256/269 111 116 29 108 127 34 0.724 8
Tranah 2004 USA (NHS) Caucasian Colorectal adenoma TaqMan Nested 556/557 256 212 58 250 222 54 0.65 8
Tranah 2004 USA (HPFS) Caucasian Colorectal adenoma TaqMan Nested 376/725 180 155 31 329 303 73 0.793 8
Kuschel 2002 UK Caucasian Breast cancer TaqMan PB 1828/1808 1176 581 71 1196 535 77 0.083 8
Winsey 2000 UK Caucasian Melanoma skin cancer PCR-SSP HB 125/211 5 47 73 8 80 122 0.245 7
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Meta-analysis and subgroup analyses

The value of I2 in the five genetic models was greater than 25%, and PQ < 0.10, so pooled ORs for the five genetic models were calculated with a random effects model. There was no obvious correlation between rs1799794 and cancer risk (PZ > 0.05; Table 2).

Table 2.

The results of the meta-analyses under different genetic models for all studies

Genetic model Number I2 (%) PH OR (95% CI) PZ
G VS A 40 47.50 0.001 1.02(0.98–1.07) 0.377
GG VS AA 40 30.20 0.039 0.98(0.89–1.08) 0.713
GG + GA VS AA 43 40.0 0.004 1.04(0.98–1.09) 0.207
GG VS GA + AA 40 34.10 0.02 0.98(0.90–1.07) 0.696
GA VS AA 40 39.40 0.006 1.04(0.99–1.11) 0.134
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Subgroup analyses were then performed based on cancer type, ethnicity, detection method, the publication year, source of control, and sample size to investigate sources of heterogeneity (Table 3). In the subgroup analysis based on cancer type, a significantly increased risk for thyroid cancer was observed in the five models (G vs. A: OR = 1.27, 95% CI = 1.01–1.61, I2 = 71.2%; GG + AG vs. AA: OR = 1.36, 95% CI = 1.15–1.61, I2 = 55.4%; GG vs. AA + AG: OR = 1.38, 95% CI = 1.09–1.75, I2 = 29.8%; GG vs. AA: OR = 1.50, 95% CI = 1.17–1.93, I2 = 45.7%; AG vs. AA: OR = 1.27, 95% CI = 1.05–1.53, I2 = 33.2%), a significantly increased risk for breast cancer was found in the heterozygous model (OR = 1.08, 95% CI = 1.02–1.13, I2 = 42.3%), and a decreased risk for ovarian cancer was found in the recessive model and homozygous model (GG vs. AA + AG: OR = 0.69, 95% CI = 0.51–0.93, I2 = 0.0%; GG vs. AA: OR = 0.71, 95% CI = 0.53–0.96, I2 = 0.0%).

Table 3.

Results of meta-analysis for polymorphisms in different subgroups and cancer susceptibility

Comparison Subgroup Number I2 PH PZ OR (95% CI)
G VS A Ethnicity
 Arabian 3 84.9% 0.001 0.752 0.86 (0.33–2.23)
 Asian 14 64.8% P < 0.001 0.255 1.05 (0.96–1.15)
 Caucasian 22 0.0% 0.661 0.502 1.01 (0.98–1.05)
 Mixed 1 NA NA 0.940 0.99 (0.80–1.23)
Cancer type
 Ovarian cancer 4 0.0% 0.547 0.848 0.99 (0.90–1.09)
 Acute lymphoblastic leukemia 2 0.0% 0.887 0.979 1.00 (0.85–1.18)
 Breast cancer 13 58.6% 0.004 0.494 1.03 (0.95–1.10)
 Thyroid cancer 4 71.2% 0.015 0.043 1.27 (1.01–1.61)
 Bladder cancer 3 0.0% 0.921 0.815 0.98 (0.85–1.13)
 lung cancer 3 60.1% 0.082 0.166 0.88 (0.74–1.05)
 Others 11 0.0% 0.902 0.822 1.01 (0.91–1.08)
Method
 PCR–RFLP 12 22.3% 0.225 0.657 0.99 (0.93–1.05)
 Sequencing 2 0.0% 0.828 0.004 2.60 (1.37–4.94)
 TaqMan 13 0.0% 0.886 0.475 1.02 (0.97–1.07)
 PCR 4 82.4% 0.001 0.913 1.02 (0.78–1.33)
 ND 6 14.6% 0.321 0.663 1.01 (0.96–1.06)
 Others 3 68.3% 0.043 0.089 1.32 (0.96–1.82)
Source of control
 HB 23 66.0% P < 0.001 0.445 1.03 (0.95–1.13)
 PB 12 0.0% 0.892 0.135 1.03 (0.99–1.08)
 Mixed 1 NA NA 0.442 0.89 (0.67–1.19)
 Nested 4 0.0% 0.874 0.294 0.95 (0.86–1.05)
Sample size
  < 100 3 77.1% 0.013 0.419 1.54 (0.54–4.43)
  > 100 37 43.7% 0.003 0.424 1.02 (0.98–1.07)
Year
  ≤ 2010 20 0.0% 0.910 0.700 1.01 (0.97–1.04)
  > 2010 20 69.5% 0.000 0.272 1.06 (0.96–1.17)
GG + AG VS AA Ethnicity
 Arabian 3 79.8% 0.007 0.739 1.21 (0.39–3.76)
 Asian 14 64.4% P < 0.001 0.547 1.04 (0.91–1.20)
 Caucasian 24 0.6% 0.453 0.119 1.03 (0.99–1.08)
 Mixed 2 0.0% 0.620 0.765 1.03 (0.85–1.24)
Cancer type
 Ovarian cancer 4 0.0% 0.887 0.439 1.05 (0.93–1.17)
 Acute lymphoblastic leukemia 3 24.4% 0.267 0.397 0.90 (0.75–1.12)
 Breast cancer 13 47.0% 0.031 0.037 1.06 (0.98–1.15)
 Thyroid cancer 4 55.4% 0.081 0.033 1.36 (1.15–1.61)
 Bladder cancer 4 59.1% 0.062 0.370 0.89 (0.70–1.14)
 Lung cancer 3 51.2% 0.129 0.207 0.85 (0.66–1.09)
 Others 12 0.0% 0.910 0.597 1.03 (0.93–1.13)
Method
 PCR–RFLP 13 0.0% 0.965 0.840 1.01 (0.92–1.11)
 Sequencing 2 0.0% 0.956 0.001 4.00 (1.82–8.80)
 TaqMan 15 29.2% 0.137 0.269 1.04 (0.97–1.10)
 PCR 4 81.0% 0.001 0.862 1.03 (0.72–1.48)
 ND 6 28.0% 0.225 0.360 1.03 (0.97–1.09)
 Others 3 16.0% 0.304 0.051 1.45 (1.15–1.82)
Source of control
 HB 23 58.4% P < 0.001 0.397 1.05 (0.94–1.18)
 PB 15 0.0% 0.656 0.015 1.06 (1.01–1.12)
 Mixed 1 NA NA 0.461 0.88 (0.63–1.24)
 Nested 4 0.0% 0.979 0.190 0.92 (0.82–1.04)
Sample size
  < 100 3 65.8% 0.054 0.179 2.23 (0.69–7.21)
  > 100 40 32.9% 0.025 0.234 1.03 (0.98–1.09)
Year
  ≤ 2010 21 0.0% 0.815 0.166 1.03 (0.99–1.08)
  > 2010 22 62.0% 0.000 0.322 1.07 (0.94–1.22)
GG VS AA + AG Ethnicity
 Arabian 3 73.9% 0.022 0.218 0.28 (0.04–2.13)
 Asian 14 52.7% 0.011 0.253 1.08 (0.95–1.23)
 Caucasian 22 0.0% 0.806 0.056 0.91 (0.82–1.00)
 Mixed 1 NA NA 0.987 0.99 (0.44–2.23)
Cancer type
 Ovarian cancer 4 0.0% 0.678 0.014 0.69 (0.51–0.93)
 Acute lymphoblastic leukemia 2 0.0% 0.698 0.818 1.04 (0.75–1.45)
 Breast cancer 13 35.7% 0.097 0.101 0.92 (0.83–1.02)
 Thyroid cancer 4 29.8% 0.234 0.007 1.38 (1.09–1.75)
 Bladder cancer 3 52.3% 0.123 0.303 1.35 (0.76–2.37)
 Lung cancer 3 5.5% 0.347 0.062 0.83 (0.69–1.01)
 Others 11 0.0% 0.893 0.993 1.00 (0.88–1.13)
Method
 PCR–RFLP 12 18.3% 0.265 0.421 0.96 (0.86–1.06)
 Sequencing 2 0.0% 0.818 0.621 1.63 (0.23–11.46)
 TaqMan 13 41.3% 0.059 0.462 0.95 (0.84–1.08)
 PCR 4 44.2% 0.146 0.211 0.88 (0.71–1.08)
 ND 6 8.8% 0.360 0.363 0.94 (0.81–1.08)
 Others 3 60.9% 0.078 0.121 1.54 (0.89–2.64)
Source of control
 HB 23 55.0% 0.010 0.614 1.04 (0.90–1.20)
 PB 12 0.0% 0.862 0.111 0.91 (0.81–1.02)
 Mixed 1 NA NA 0.674 0.84 (0.38–1.02)
 Nested 4 0.0% 0.536 0.967 1.00 (0.80–1.24)
Sample size
  < 100 3 0.0% 0.537 0.339 0.64 (0.26–1.59)
  > 100 37 36.9% 0.014 0.766 0.99 (0.90–1.07)
Year
  ≤ 2010 20 0.0% 0.928 0.068 0.94 (0.83–1.01)
  > 2010 20 58.0% 0.001 0.374 1.08 (0.92–1.27)
GG VS AA Ethnicity
 Arabian 3 75.4% 0.017 0.338 0.33 (0.04–3.15)
 Asian 14 47.8% 0.024 0.279 1.08 (0.93–1.26)
 Caucasian 22 0.0% 0.812 0.083 0.91 (0.82–1.01)
 Mixed 1 NA NA 0.981 0.99 (0.44–2.23)
Cancer type
 Ovarian cancer 4 0.0% 0.705 0.028 0.71 (0.53–0.96)
 Acute lymphoblastic leukemia 2 0.0% 0.836 0.961 0.99 (0.67–1.47)
 Breast cancer 13 37.7% 0.082 0.311 0.94 (0.85–1.05)
 Thyroid cancer 4 45.7% 0.137 0.001 1.50 (1.17–1.93)
 Bladder cancer 3 0.0% 0.860 0.773 1.06 (0.72–1.55)
 Lung cancer 3 53.1% 0.119 0.019 0.79 (0.56–1.11)
 Others 11 0.0% 0.884 0.798 1.02 (0.88–1.19)
Method
 PCR–RFLP 12 10.7% 0.340 0.591 0.96 (0.85–1.10)
 Sequencing 2 0.0% 0.837 0.264 3.09 (0.43–22.45)
 TaqMan 13 0.0% 0.701 0.297 0.93 (0.81–1.07)
 PCR 4 73.8% 0.010 0.937 0.98 (0.61–1.58)
 ND 6 2.7% 0.399 0.436 0.94 (0.82–1.09)
 Others 3 0.0% 0.409 P < 0.001 1.97 (1.36–2.87)
Source of control
 HB 23 52.8% 0.002 0.628 1.04 (0.88–1.24)
 PB 12 0.0% 0.911 0.185 0.92 (0.82–1.04)
 Mixed 1 NA NA 0.604 0.81 (0.36–1.80)
 Nested 4 0.0% 0.553 0.737 0.96 (0.76–1.21)
Sample size
  < 100 3 18.0% 0.295 0.796 0.87 (0.31–2.48)
  > 100 37 32.5 0.031 0.733 0.98 (0.89–1.08)
Year
  ≤ 2010 20 0.0% 0.961 0.070 0.91 (0.82–1.01)
  > 2010 20 55.2% 0.002 0.356 1.06 (0.96–1.17)
AG VS AA Ethnicity
 Arabian 3 54.9% 0.109 0.174 1.76 (0.78–3.95)
 Asian 14 65.7% P < 0.001 0.906 1.01 (0.86–1.18)
 Caucasian 22 0.0% 0.631 0.023 1.05 (1.01–1.10)
 Mixed 1 NA NA 0.937 0.99 (0.77–1.27)
Cancer type
 Ovarian cancer 4 0.0% 0.998 0.145 1.09 (0.97–1.22)
 Acute lymphoblastic leukemia 2 0.0% 0.747 0.893 0.98 (0.78–1.24)
 Breast cancer 13 42.3% 0.054 0.006 1.08 (1.02–1.13)
 Thyroid cancer 4 33.2% 0.213 0.012 1.27 (1.05–1.53)
 Bladder cancer 3 87.1% P < 0.001 0.038 0.71 (0.41–1.23)
 Lung cancer 3 26.7% 0.255 0.132 0.87 (0.73–1.04)
 Others 11 0.0% 0.935 0.710 1.02 (0.92–1.13)
Method
 PCR–RFLP 12 0.0% 0.981 0.590 1.03 (0.93–1.14)
 Sequencing 2 0.0% 0.946 0.001 4.00 (1.79–8.94)
 TaqMan 13 57.1% 0.006 0.696 1.02 (0.92–1.14)
 PCR 4 72.9% 0.011 0.780 1.05 (0.76–1.44)
 ND 6 35.1% 0.173 0.205 1.04 (0.98–1.11)
 Others 3 0.0% 0.577 0.089 1.25 (0.97–1.63)
Source of control
 HB 23 56.0% 0.001 0.421 1.05 (0.93–1.18)
 pb 12 0.0% 0.803 0.002 1.09 (1.03–1.15)
 MIXED 1 NA NA 0.518 0.89 (0.62–1.27)
 Nested 4 0.0% 0.989 0.160 0.91 (0.80–1.04)
Sample size
  < 100 3 31.6% 0.232 0.003 2.82 (1.42–5.57)
  > 100 37 32.9% 0.029 0.153 1.04 (0.99–1.10)
Year
  ≤ 2010 20 0.0% 0.667 0.047 1.05 (1.00–1.10)
  > 2010 20 60.8% 0.000 0.278 1.08 (0.94–1.25)
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In the subgroup analysis based on ethnicity, rs1799794 was associated with increased cancer risk in the Caucasian population according to the heterozygous model (AG vs. AA: OR = 1.05, 95% CI = 1.01–1.10, I2 = 0.0%). In the subgroup analysis based on source of control, we found a significantly increased risk for PB (population based) in the dominant model and heterozygous model (GG + AG vs. AA: OR = 1.06, 95% CI = 1.01–1.12, I2 = 0.0%; AG vs. AA: OR = 1.09, 95% CI = 1.03–1.15, I2 = 0.0%). In the subgroup analysis based on detection method, sequencing was associated with a significantly increased cancer risk in the allele model, dominant model, and heterozygous model (G vs. A: OR = 2.60, 95% CI = 1.37–4.94, I2 = 0.0%; GG + AG vs. AA: OR = 4.00, 95% CI = 1.82–8.80, I2 = 0.0%; AG vs. AA: OR = 4.00, 95% CI = 1.79–8.94, I2 = 0.0%). In the subgroup analysis based on sample size, AG carriers were 2.82 times more likely to develop cancer than AA carriers (95% CI = 1.42–5.57, PZ = 0.003). In the subgroup analysis based on the publication year, studies published before 2010 showed that AG carriers were 1.05 times more likely to develop cancer than AA carriers (95% CI = 1.00–1.10, PZ = 0.047).

Publication bias

The shape of the funnel plots (Fig. 2) and Egger’s test (allele: P = 0.108, dominant: P = 0.177, recessive: P = 0.240, homozygous: P = 0.132, heterozygous: P = 0.177) showed no publication bias.

Fig. 2.

Fig. 2

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Funnel plots for the test of publication bias for the five genetic models

Sensitivity analysis

Eight studies [41, 42, 48–50, 53, 54, 56] had PHWE < 0.05, but for two studies [51, 63] PHWE was not available. We compared the combined results before and after excluding these 10 studies and there were slight changes in the results. When the subgroup analysis was performed according to cancer type, there were no significant associations between rs1799794 polymorphism and increased risk for thyroid cancer in the recessive model, homozygous model, or heterozygous model (GG vs. AA + AG: OR = 1.16, 95% CI = 0.87–1.55, I2 = 0.0%; GG vs. AA: OR = 1.24, 95% CI = 0.90–1.69, I2 = 0.0%; AG vs. AA: OR = 1.22, 95% CI = 0.98–1.51, I2 = 49.4%), and rs3116496 was related to a decreased risk for lung cancer in the five models (A vs. G: OR = 0.80, 95% CI = 0.70–0.92, I2 = 18.1%; GG + AG vs. AA: OR = 0.76, 95% CI = 0.62–0.93, I2 = 4.9%; GG vs. AA + AG: OR = 0.75, 95% CI = 0.59–0.96, I2 = 0.0%; GG vs. AA: OR = 0.65, 95% CI = 0.49–0.87, I2 = 0.0%; AG vs. AA: OR = 0.80, 95% CI = 0.64–0.99, I2 = 0.0%); no changes were observed for the other cancers. No significant changes were found in the subgroup analyses by ethnicity and source of control.

Discussion

Our study shows that XRCC3 rs1799794 is irrelevant to cancer risk. In addition, the risk for thyroid cancer and breast cancer increase significantly in patients with rs1799794, and Caucasian populations are more likely to develop these cancers while having a decreased risk for ovarian cancer. We excluded articles that did not conform to HWE and reanalyzed the data. Compared to the previous results, rs3116496 was related to a decreased risk for lung cancer in the five models, although the other results were not much changed (data not shown).

Moderate heterogeneity was found in this meta-analysis. First, we used random models when significant heterogeneity. Second, we performed subgroup analyses to explore sources of heterogeneity. As shown in Table 3, in the subgroup analysis based on ethnicity, heterogeneity increased in Arabian/Asian populations but was 0% in Caucasian populations, which suggests that ethnicity may be a factor in heterogeneity. Furthermore, we analyzed studies stratified by cancer type, detection method, source of control, and sample size. Ethnicity, cancer type, source of control, and sample size may be the source of inter-research heterogeneity. In addition, a sensitivity analysis suggested that the current findings were reliable.

To date, five meta-analyses of the impact of rs1799794 on cancer risk have been performed [28, 30, 31, 33, 34] on rs1799794 and susceptibility to pan-cancer [28], breast cancer [30, 34], bladder cancer [33], and ovarian cancer [31]. To the best of our knowledge, ours is currently the most comprehensive meta-analysis of correlations between rs1799794 polymorphisms and cancer. There are many differences between the results of this study and previous studies. According to Qiu et al.’s research on rs1799794 and susceptibility to breast cancer, which included four studies in three papers, rs1799794 was associated with a statistically significant increase in cancer risk in the dominant model (GG + AG vs. AA: OR = 1.09, 95% CI = 1.01–1.17, PH = 0.15), whereas our results showed an increased risk for breast cancer in AG carriers, different from the protective effect found previously [48]. In addition, our study found that the G allele might be a dominant gene and found an increased risk for thyroid cancer.

Our study included a large number of samples and conducted a stratified analysis, which played an important role in the reliability of the research results. At the same time, there are problems that cannot be ignored: the presence of heterogeneity that may due to ethnicity, source of control, status, or cancer type; the lack of relevant data published in other languages and evaluation of the interaction between cancer-related factors.

Conclusion

In conclusion, this meta-analysis found no association between XRCC3 rs1799794 and cancer risk, but XRCC3 rs1799794 was associated with breast cancer and thyroid cancer as well as with Caucasian populations. In addition, detection method, source of control, and sample size played a role in heterogeneity and in the results. Well-designed large-scale studies are required to further evaluate the results.

Acknowledgements

Not applicable

Abbreviations

SNP

Single nucleotide polymorphism

XRCC3

X-ray repair cross-complementing group 3

HR

Homologous recombination

OR

Odds ratio

CI

Confidence interval

Authors' contributions

WQL: Drafting of manuscript/Analysis and interpretation of data. SMM, LL, ZYK, HBZ: Acquisition of data/Analysis and interpretation of data/Critical revision. JY: Study conception and design/Analysis and interpretation of data/Critical revision. All authors have read and approved the final manuscript.

Funding

This work was supported by the Applied Basic Foundation of Yunnan Province (No. 202001AT070009), Yunnan Health Training Project of High Level Talents (No. D-2019032). The funding bodies had no role in the research design, the conduct of the study, or the decision to publish the results.

Availability of data and materials

All data generated or analyzed during this study are included in this manuscript.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

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

Data Availability Statement

All data generated or analyzed during this study are included in this manuscript.

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