Abstract

Objectives In the present study, we examined available articles from online databases to comprehensively investigate the effect of the XPC (xeroderma pigmentosum complementation group C) rs2228000 polymorphism on the risk of different types of clinical cancer.

Methods We conducted a group of overall and subgroup pooling analyses after retrieving the data from four databases (updated till September 2019). The P-value of association, OR (odds ratios), and 95% CI (confidence interval) were calculated.

Results We selected a total of 71 eligible studies with 26835 cancer cases and 37069 controls from the 1186 retrieved articles. There is an enhanced susceptibility for bladder cancer cases under T vs. C [P=0.004; OR (95% CI) = 1.25 (1.07, 1.45)], TT vs. CC [P=0.001; 1.68 (1.25, 2.26)], CT+TT vs. CC [P=0.016; 1.26 (1.04, 1.53)], and TT vs. CC+ CT [P=0.001; 1.49 (1.18, 1.90)] compared with negative controls. Additionally, there is an increased risk of breast cancer under T vs. C, TT vs. CC and TT vs. CC+ CT (P<0.05, OR > 1). Nevertheless, there is a decreased risk of gastric cancer cases in China under T vs. C [P=0.020; 0.92 (0.85, 0.99)], CT vs. CC [P=0.001, 0.83 (0.73, 0.93)], and CT+TT vs. CC [P=0.003, 0.84 (0.76, 0.94)].

Conclusions The TT genotype of XPC rs2228000 may be linked to an increased risk of bladder and breast cancer, whereas the CT genotype is likely to be associated with reduced susceptibility to gastric cancer in the Chinese population.

Introduction

The human XPC (xeroderma pigmentosum complementation group C) gene is located on chromosome 3p25 and contains 16 exons and 15 introns [1,2]. The human XPC protein with 940 amino acids, encoded by XPC, serves as an essential member within the NER (nucleotide excision repair) pathway [3–5]. The XPC protein is important for the early damage site recognition and DNA repair initiation of NER [3,6,7]. The abnormal expression of the XPC protein was also reportedly linked to the progression of the cancer [3,8].

Within the XPC gene, three common variants, including rs2228000 (C21151T) of exon 8, rs2228001 (A33512C) of exon 15, and poly-AT insertion/deletion polymorphism (PAT−/+) of intron 9, were identified [4,9–11]. XPC rs2228000 results in a substitution of alanine for valine in position 499 (Ala499Val), while rs2228001 leads to a transversion from lysine to glutamine in position 939 (Lys939Gln) [4,9–11]. The present study investigated the potential genetic role of nonsynonymous XPC rs2228000 in the risk of different clinical types of cancer by pooling published studies with inconclusive conclusions.

After retrieving these studies, only three previous meta-analyses with no more than 15 studies in 2008 [12–14] and one meta-analysis with 33 studies in 2013 [15] were performed to assess the genetic association of XPC rs2228000 and the risk of overall cancer. Thus, we enrolled more sample sizes (71 case–control studies) and utilized different analysis strategies for an updated comprehensive evaluation in 2019 through meta-analysis and TSA (trial sequential analysis).

Materials and methods

Case–control study identification

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) was utilized for our pooling analysis. In September 2019, we used a series of search terms (shown in Supplementary Table S1) to retrieve from four databases [PubMed, Embase, CChia National Knowledge Infrastructure (CNKI)) and WOS (Web of Science)] to obtain potentially relevant articles. We also designed a group of criteria for the inclusion/exclusion and eligibility assessment of the article. Inclusion criteria were the following: (1) case/control studies; (2) cancer; (3) XPC rs2228000; and (4) genotypic frequency data within both the case and control groups. Exclusion criteria were the following: (1) review; (2) meeting abstract; (3) case reports or family data; (4) meta-analysis; (5) cell, mice, horse, or other species; (6) other gene, disease or variant; (7) lack of specific data; (8) lack of normal group; (9) not in line with HWE (Hardy–Weinberg equilibrium); and (10) cohort.

Basic information collection

We extracted some basic information, including author name, publication year, country, race, genotypic frequency, cancer type, control source, genotyping assay, and sample size, from the selected eligible case–control studies. The P-value of HWE based on the genotypic distribution in the control group was calculated.

Article quality assessment

We utilized two approaches, including the NOS (Newcastle–Ottawa quality assessment scale) system (Supplementary Table S2) [16,17] and the risk-of-bias score system (Supplementary Table S3) [18,19] for the assessment of article quality. The article with an NOS score > 5 and a risk-of-bias score > 9 was considered to be high quality.

Pooling analysis

We used STATA software (Stata Corporation, U.S.A.) to perform the association test in the overall and subgroup meta-analysis, heterogeneity assessment, Begg’s/Egger’s tests (for the publication bias evaluation) and sensitivity analysis (for data stability assessment) [16,17]. The OR (odds ratio), 95% CI (confidence interval) and P-value in a series of association tests under the five genetic models, including T vs. C (allele), TT vs. CC (homozygote), CT vs. CC (heterozygote), CT+TT vs. CC (dominant), and TT vs. CC+CT (recessive), were obtained. In addition, six factors, including race, country, control source, article quality, genotyping assay, and cancer type, were considered in our subgroup analysis.

The high heterogeneity was considered when the I2 value in the I2 test was larger than 50% and the P-value in the Q statistical test was less than 0.05, which led to the use of the DerSimonian–Laird method of the random-effect model. If not, a Mantel–Haenszel method of a fixed-effect model was used for the relatively low heterogeneity between studies.

False-positive report probability

Targeting the positive findings, we also calculated the false-positive report probability (FPRP) and statistical power, as suggested by Wacholder et al. [20]. During analysis, an FPRP cut-off value of 0.2, a power OR of 1.5, and different prior probability levels (0.25, 0.1, 0.01, 0.001, 0.0001) were established. After assessing the research status regarding the association between XPC rs2228000 and cancer risk and referencing the similar publications [21,22], the FPRP value of the positive results less than 0.2 under the prior probability level of 0.1 indicates a noteworthy outcome.

TSA

We also performed the TSA test to evaluate whether further research was needed, referring to some similar publications [23–26]. For the TSA parameter, a type I error probability of 5%, a statistical test power of 80%, and a low bias-based risk ratio reduction were established. Trial Sequential Analysis Viewer software (http://www.ctu.dk/tsa/) was utilized.

Results

Identification of eligible studies

In total, we obtained 1186 potential eligible articles [PubMed (n=266), Embase [n=687], CNKI (n=28), and WOS (n=205)] and then ruled out another 412 duplicates and 646 improper articles according to our exclusion criteria (detailed information listed in Figure 1). Furthermore, we excluded 64 articles due to the question of ‘lack of specific data or normal group’, ‘not in line with HWE’ or ‘cohort’. Finally, we identified a total of 71 eligible case–control studies from the 64 retrieved articles [1,2,4,10,11,27–85] for pooling analysis. We summarized some basic information in Table 1 and presented the flow chart in Figure 1. All the genotypic distribution of the control group in all studies followed the principle of HWE. Although the NOS scores in all studies were larger than 5 (Supplementary Table S2), the risk-of-bias scores of nine articles (Supplementary Table S3) were less than 9.

Selection process of eligible case–control studies

Figure 1
Selection process of eligible case–control studies

 

Figure 1
Selection process of eligible case–control studies

 

Table 1
Basic information of the studies included in the meta-analysis
First author Year Country Race Cases Cancer type Control Control source Genotyping assay 
    CC CT TT  CC CT TT   
Al-Qadoori 2019 Iraq Asian 37 23 Bladder cancer 31 PB Gene sequencing 
An 2007 U.S.A. Caucasian 445 293 91 HNSCC 454 342 58 HB PCR-RFLP 
Bai 2007 China Asian 184 193 48 LAC 446 456 88 HB TaqMan 
  China Asian 149 149 34 LSCC 446 456 88 HB TaqMan 
  China Asian 31 25 SCLC 446 456 88 HB TaqMan 
Broberg 2005 Sweden Caucasian 35 20 Bladder cancer 92 55 PB MassARRAY 
Chen 2013 China Asian 45 60 26 Cervical cancer 101 118 38 HB PCR-RFLP 
de Verdier 2010 Sweden Caucasian 138 138 35 Bladder cancer 196 124 10 PB PCR-RFLP 
Doherty 2011 U.S.A. Mixed 411 257 49 Endometrial cancer 384 278 61 PB PCR-RFLP/SNaPshot 
Dong 2008 China Asian 141 90 22 GCA 272 282 58 PB PCR-RFLP 
Farnebo 2015 Sweden Caucasian 89 63 17 HNSCC 219 105 20 PB PCR-RFLP 
Figl 2010 Spain/Germany Caucasian 626 477 81 Melanoma 670 516 88 PB TaqMan 
Garcia 2006 Spain Caucasian 583 440 85 Bladder cancer 599 435 75 HB SNP500Cancer 
Guo 2008 China Asian 156 133 38 ESCC 272 282 58 PB PCR-RFLP 
He 2016 China Asian 201 198 51 Breast cancer 228 174 28 PB MassARRAY 
He 2012 China Asian 104 90 16 Pancreatic cancer 106 85 22 PB SNaPshot 
Hu 2005 China Asian 124 171 25 Lung cancer 158 145 19 PB PCR-PIRA 
Hua 2016a China Asian 432 531 178 CRC 429 583 161 PB TaqMan 
Hua 2016b China Asian 457 524 161 Gastric cancer 429 583 161 PB TaqMan 
Huang 2006 U.S.A. Mixed 397 261 31 CRC 403 259 41 HB SNP500Cancer 
Ibarrola 2011 Spain Caucasian 323 227 49 Melanoma 198 158 23 PB/HB MassARRAY 
Jiao 2011 China Asian 127 177 30 GBC 163 146 20 HB PCR-RFLP 
Jorgensen 2007 U.S.A. Caucasian 153 87 13 Breast cancer 157 104 14 PB TaqMan 
Kim 2002 Korea Asian 104 102 12 Lung cancer 77 62 10 PB PCR-RFLP 
Lee 2005 Korea Asian 113 84 13 LSCC 223 179 29 PB PCR-RFLP 
  Korea Asian 79 58 LAC 223 179 29 PB PCR-RFLP 
  Korea Asian 39 28 SCLC 223 179 29 PB PCR-RFLP 
Li 2006 U.S.A. Caucasian 338 214 50 Melanoma 318 248 37 HB PCR-RFLP 
Li 2014 China Asian 92 91 19 Gastric cancer 144 153 30 PB PCR-RFLP 
Li 2010 China Asian 163 248 89 HCC 169 250 88 HB TaqMan 
Liang 2018 China Asian 98 89 18 Pancreatic cancer 116 90 24 HB SNaPshot 
Liu 2016 China Asian 444 351 96 Gastric cancer 424 408 95 HB MassARRAY 
Liu 2012 China Asian 242 294 64 Bladder cancer 272 285 52 PB PCR-RFLP 
Liu 2019 China Asian 178 159 54 Uterine leiomyoma 183 232 78 PB Sequence Detection System 
Long 2010 China Asian 170 156 35 GAA 280 274 62 HB TaqMan 
McWilliams 2008 U.S.A. Mixed 246 182 29 Pancreatic cancer 339 211 32 HB SNPstream or Pyrosequencing 
Monroy 2011 U.S.A. Mixed 92 90 HL 137 71 10 PB MassARRAY 
Na 2012 China Asian 213 124 23 Breast cancer 228 118 14 HB MassARRAY 
Nigam 2019 China Asian 22 22 26 Oral cancer 69 145 83 PB PCR-RFLP 
Ozgoz 2019 Turkey Caucasian 57 38 Breast cancer 67 26 PB MassARRAY 
Pan 2009 U.S.A Caucasian 228 129 26 Esophageal cancer 251 178 21 PB TaqMan 
Paszkowska 2015 Poland Caucasian 443 269 41 CRC 548 563 177 PB MassARRAY/Taqman 
Paszkowska 2013 Poland Caucasian 245 240 34 Melanoma 548 563 177 PB MassARRAY 
Perez 2013 U.S.A. Caucasian 63 115 Breast cancer 21 131 203 PB TaqMan 
Ravegnini 2016 Italy Caucasian 42 34 GIST 90 45 12 PB TaqMan 
Roberts 2011 U.S.A. Mixed 167 100 18 Breast cancer1 317 193 40 PB MassARRAY 
  U.S.A. Mixed 437 273 48 Breast cancer2 793 478 72 PB MassARRAY 
Sak 2006 U.K. Mixed 279 202 57 Bladder cancer 317 210 38 PB/HB TaqMan 
Sakoda 2012 U.S.A. Caucasian 401 299 43 Lung cancer 822 566 87 PB GoldenGate/Taqman 
Sankhwar 2016 India Asian 52 113 69 Bladder cancer 87 112 59 PB PCR-RFLP/gene sequencing 
Santos 2013 Portugal Caucasian 47 55 Thyroid cancer 95 98 19 HB PCR-RFLP 
Shen 2006 U.S.A. Caucasian 96 50 Breast cancer 91 55 PB TaqMan 
Shen 2008 U.S.A. Mixed 614 385 62 Breast cancer 632 417 56 PB Fluorescence polarization 
Shen 2005 China Asian 56 47 13 Lung cancer 50 47 13 PB TaqMan 
Slyskova 2012 Czech Republic Caucasian 36 24 CRC 37 24 PB PCR-RFLP 
Smith 2008 U.S.A. Caucasian 178 116 23 Breast cancer 211 161 29 PB MassARRAY 
  U.S.A. Others 44 Breast cancer 61 14 PB MassARRAY 
Steck 2014 U.S.A. Others 175 51 CRC 276 47 PB MassARRAY 
  U.S.A. Caucasian 177 104 22 CRC 293 207 35 PB MassARRAY 
Tang 2011 China Asian 40 55 14 ALL 80 74 15 PB MassARRAY 
Weiss 2005 U.S.A. Mixed 211 129 31 Endometrial cancer 213 166 41 PB SNaPshot 
Wu 2011a China Asian 172 195 52 CRC 315 406 117 PB PCR-RFLP 
Wu 2011b China Asian 65 86 22 Breast cancer 69 85 16 PB PCR-RFLP 
Yang 2012 China Asian 197 322 99 Breast cancer 235 312 75 PB PCR-RFLP 
Yang 2008 China Asian 52 73 28 NPC 76 79 13 PB PCR-RFLP 
Zhao 2018 China Asian 46 35 Ovarian cancer 127 175 54 PB TaqMan 
Zheng 2016 China Asian 111 108 34 Neuroblastoma 205 250 76 PB TaqMan 
Zhou 2008 China Asian 103 78 27 Ovarian cancer 118 95 18 PB PCR-RFLP 
Zhu 2018 China Asian 64 59 22 Nneuroblastoma 205 250 76 PB TaqMan 
Zhu 2008 China Asian 110 60 18 ESCC 83 88 32 PB PCR-RFLP 
Zhu 2007 U.S.A. Caucasian 323 193 30 Bladder cancer 310 215 24 HB TaqMan 
First author Year Country Race Cases Cancer type Control Control source Genotyping assay 
    CC CT TT  CC CT TT   
Al-Qadoori 2019 Iraq Asian 37 23 Bladder cancer 31 PB Gene sequencing 
An 2007 U.S.A. Caucasian 445 293 91 HNSCC 454 342 58 HB PCR-RFLP 
Bai 2007 China Asian 184 193 48 LAC 446 456 88 HB TaqMan 
  China Asian 149 149 34 LSCC 446 456 88 HB TaqMan 
  China Asian 31 25 SCLC 446 456 88 HB TaqMan 
Broberg 2005 Sweden Caucasian 35 20 Bladder cancer 92 55 PB MassARRAY 
Chen 2013 China Asian 45 60 26 Cervical cancer 101 118 38 HB PCR-RFLP 
de Verdier 2010 Sweden Caucasian 138 138 35 Bladder cancer 196 124 10 PB PCR-RFLP 
Doherty 2011 U.S.A. Mixed 411 257 49 Endometrial cancer 384 278 61 PB PCR-RFLP/SNaPshot 
Dong 2008 China Asian 141 90 22 GCA 272 282 58 PB PCR-RFLP 
Farnebo 2015 Sweden Caucasian 89 63 17 HNSCC 219 105 20 PB PCR-RFLP 
Figl 2010 Spain/Germany Caucasian 626 477 81 Melanoma 670 516 88 PB TaqMan 
Garcia 2006 Spain Caucasian 583 440 85 Bladder cancer 599 435 75 HB SNP500Cancer 
Guo 2008 China Asian 156 133 38 ESCC 272 282 58 PB PCR-RFLP 
He 2016 China Asian 201 198 51 Breast cancer 228 174 28 PB MassARRAY 
He 2012 China Asian 104 90 16 Pancreatic cancer 106 85 22 PB SNaPshot 
Hu 2005 China Asian 124 171 25 Lung cancer 158 145 19 PB PCR-PIRA 
Hua 2016a China Asian 432 531 178 CRC 429 583 161 PB TaqMan 
Hua 2016b China Asian 457 524 161 Gastric cancer 429 583 161 PB TaqMan 
Huang 2006 U.S.A. Mixed 397 261 31 CRC 403 259 41 HB SNP500Cancer 
Ibarrola 2011 Spain Caucasian 323 227 49 Melanoma 198 158 23 PB/HB MassARRAY 
Jiao 2011 China Asian 127 177 30 GBC 163 146 20 HB PCR-RFLP 
Jorgensen 2007 U.S.A. Caucasian 153 87 13 Breast cancer 157 104 14 PB TaqMan 
Kim 2002 Korea Asian 104 102 12 Lung cancer 77 62 10 PB PCR-RFLP 
Lee 2005 Korea Asian 113 84 13 LSCC 223 179 29 PB PCR-RFLP 
  Korea Asian 79 58 LAC 223 179 29 PB PCR-RFLP 
  Korea Asian 39 28 SCLC 223 179 29 PB PCR-RFLP 
Li 2006 U.S.A. Caucasian 338 214 50 Melanoma 318 248 37 HB PCR-RFLP 
Li 2014 China Asian 92 91 19 Gastric cancer 144 153 30 PB PCR-RFLP 
Li 2010 China Asian 163 248 89 HCC 169 250 88 HB TaqMan 
Liang 2018 China Asian 98 89 18 Pancreatic cancer 116 90 24 HB SNaPshot 
Liu 2016 China Asian 444 351 96 Gastric cancer 424 408 95 HB MassARRAY 
Liu 2012 China Asian 242 294 64 Bladder cancer 272 285 52 PB PCR-RFLP 
Liu 2019 China Asian 178 159 54 Uterine leiomyoma 183 232 78 PB Sequence Detection System 
Long 2010 China Asian 170 156 35 GAA 280 274 62 HB TaqMan 
McWilliams 2008 U.S.A. Mixed 246 182 29 Pancreatic cancer 339 211 32 HB SNPstream or Pyrosequencing 
Monroy 2011 U.S.A. Mixed 92 90 HL 137 71 10 PB MassARRAY 
Na 2012 China Asian 213 124 23 Breast cancer 228 118 14 HB MassARRAY 
Nigam 2019 China Asian 22 22 26 Oral cancer 69 145 83 PB PCR-RFLP 
Ozgoz 2019 Turkey Caucasian 57 38 Breast cancer 67 26 PB MassARRAY 
Pan 2009 U.S.A Caucasian 228 129 26 Esophageal cancer 251 178 21 PB TaqMan 
Paszkowska 2015 Poland Caucasian 443 269 41 CRC 548 563 177 PB MassARRAY/Taqman 
Paszkowska 2013 Poland Caucasian 245 240 34 Melanoma 548 563 177 PB MassARRAY 
Perez 2013 U.S.A. Caucasian 63 115 Breast cancer 21 131 203 PB TaqMan 
Ravegnini 2016 Italy Caucasian 42 34 GIST 90 45 12 PB TaqMan 
Roberts 2011 U.S.A. Mixed 167 100 18 Breast cancer1 317 193 40 PB MassARRAY 
  U.S.A. Mixed 437 273 48 Breast cancer2 793 478 72 PB MassARRAY 
Sak 2006 U.K. Mixed 279 202 57 Bladder cancer 317 210 38 PB/HB TaqMan 
Sakoda 2012 U.S.A. Caucasian 401 299 43 Lung cancer 822 566 87 PB GoldenGate/Taqman 
Sankhwar 2016 India Asian 52 113 69 Bladder cancer 87 112 59 PB PCR-RFLP/gene sequencing 
Santos 2013 Portugal Caucasian 47 55 Thyroid cancer 95 98 19 HB PCR-RFLP 
Shen 2006 U.S.A. Caucasian 96 50 Breast cancer 91 55 PB TaqMan 
Shen 2008 U.S.A. Mixed 614 385 62 Breast cancer 632 417 56 PB Fluorescence polarization 
Shen 2005 China Asian 56 47 13 Lung cancer 50 47 13 PB TaqMan 
Slyskova 2012 Czech Republic Caucasian 36 24 CRC 37 24 PB PCR-RFLP 
Smith 2008 U.S.A. Caucasian 178 116 23 Breast cancer 211 161 29 PB MassARRAY 
  U.S.A. Others 44 Breast cancer 61 14 PB MassARRAY 
Steck 2014 U.S.A. Others 175 51 CRC 276 47 PB MassARRAY 
  U.S.A. Caucasian 177 104 22 CRC 293 207 35 PB MassARRAY 
Tang 2011 China Asian 40 55 14 ALL 80 74 15 PB MassARRAY 
Weiss 2005 U.S.A. Mixed 211 129 31 Endometrial cancer 213 166 41 PB SNaPshot 
Wu 2011a China Asian 172 195 52 CRC 315 406 117 PB PCR-RFLP 
Wu 2011b China Asian 65 86 22 Breast cancer 69 85 16 PB PCR-RFLP 
Yang 2012 China Asian 197 322 99 Breast cancer 235 312 75 PB PCR-RFLP 
Yang 2008 China Asian 52 73 28 NPC 76 79 13 PB PCR-RFLP 
Zhao 2018 China Asian 46 35 Ovarian cancer 127 175 54 PB TaqMan 
Zheng 2016 China Asian 111 108 34 Neuroblastoma 205 250 76 PB TaqMan 
Zhou 2008 China Asian 103 78 27 Ovarian cancer 118 95 18 PB PCR-RFLP 
Zhu 2018 China Asian 64 59 22 Nneuroblastoma 205 250 76 PB TaqMan 
Zhu 2008 China Asian 110 60 18 ESCC 83 88 32 PB PCR-RFLP 
Zhu 2007 U.S.A. Caucasian 323 193 30 Bladder cancer 310 215 24 HB TaqMan 

Abbreviations: ALL, acute lymphoblastic leukemia; CRC, colorectal cancer; ESCC, esophageal squamous cell carcinoma; GAA, gastric antrum adenocarcinoma; GBC, primary gallbladder adenocarcinoma; GCA, gastric cardiac adenocarcinoma; GIST, gastrointestinal stromal tumour; HB, hospital-based; HCC, hepatocellular carcinoma; HL, Hodgkin lymphoma; HNSCC, head and neck squamous cell carcinoma; LAC, lung adenocarcinoma; LSCC, lung squamous cell carcinoma; NPC, nasopharyngeal cancer; PB, population-based; PCR, polymerase chain reaction; PIRA, primer-introduced restriction analysis; RFLP, restriction fragment length polymorphism; SCLC, Small cell lung carcinoma; SNP, single nucleotide polymorphism.

1 Premenopausal.

2 Postmenopausal.

Overall meta-analysis

As shown in Table 2, our overall meta-analysis included a total of 71 studies with 26835 cases and 37069 controls. We observed high between-study heterogeneity (Table 2, all I2 > 50%, Pheterogeneity<0.001) and thus utilized the random-effect model for the pooling analysis. After pooling the different studies together, we only detected an increased risk of overall cancers under the TT vs. CC+CT model [Table 2, Passociation=0.023, OR = 1.11, 95% CI = (1.01, 1.22)] but not other models (all Passociation>0.05). These results indicated that XPC rs2228000 does not seem to be statistically associated with susceptibility to cancer.

Table 2
Meta-analysis of XPC rs2228000 and overall cancer risk
Genetic model Sample size Association Heterogeneity Publication bias 
 Study Case/control Passociation OR (95% CI) I2 Pheterogeneity PBegg PEgger 
T vs. C 71 26835/37069 0.218 1.03 (0.98,1.09) 72.2% <0.001 0.079 0.031 
TT vs. CC 71 26835/37069 0.090 1.10 (0.99,1.23) 64.6% <0.001 0.124 0.065 
CT vs. CC 71 26835/37069 0.588 0.98 (0.93,1.04) 59.1% <0.001 0.093 0.046 
CT+TT vs. CC 71 26835/37069 0.793 1.01 (0.95,1.07) 68.0% <0.001 0.069 0.023 
TT vs. CC+ CT 71 26835/37069 0.023 1.11 (1.01,1.22) 54.1% <0.001 0.493 0.230 
Genetic model Sample size Association Heterogeneity Publication bias 
 Study Case/control Passociation OR (95% CI) I2 Pheterogeneity PBegg PEgger 
T vs. C 71 26835/37069 0.218 1.03 (0.98,1.09) 72.2% <0.001 0.079 0.031 
TT vs. CC 71 26835/37069 0.090 1.10 (0.99,1.23) 64.6% <0.001 0.124 0.065 
CT vs. CC 71 26835/37069 0.588 0.98 (0.93,1.04) 59.1% <0.001 0.093 0.046 
CT+TT vs. CC 71 26835/37069 0.793 1.01 (0.95,1.07) 68.0% <0.001 0.069 0.023 
TT vs. CC+ CT 71 26835/37069 0.023 1.11 (1.01,1.22) 54.1% <0.001 0.493 0.230 

Abbreviations: Passociation, P-value in the association test; Pheterogeneity, P-value in the heterogeneity test; PBegg, P-value in Begg’s test; PEgger, P-value in Egger’s test.

Subgroup analysis

Next, we performed a series of subgroup analyses by the factors of race, control source, country, article quality, and genotyping assay. As shown in Table 3, a total of 38 studies (12118 cases/18124 controls) were included for the subgroup analysis of ‘Asian’, while 22 studies with 9371 cases and 12338 controls were included for the ‘Caucasian’ subgroup. We did not observe a significant difference between cancer cases and negative controls under the most genetic models (Table 3, Passociation>0.05), only apart from the Asian subgroup under the TT vs. CC+CT model [Passociation=0.005, OR = 1.13, 95% CI = (1.04, 1.23)]. Within the subgroup analysis by the factor or control source (PB/HB (population/hospital-based)), an increased risk of cancer was only detected in the ‘HB’ subgroup under TT vs. CC [Table 3, Passociation=0.010, OR = 1.17, 95% CI = (1.04,1.32)] and TT vs. CC+CT [Passociation=0.006, OR = 1.18, 95% CI = (1.05, 1.32)] models but not others (Passociation>0.05). Similarly, we observed negative results in the majority of the subgroup analyses by country, article quality and genotyping assay (Supplementary Table S4). As examples, we presented the forest plots of the subgroup analysis data by the factor of race (Figure 2), control source (Supplementary Figure S1), country (Supplementary Figure S2), article quality (Supplementary Figure S3), and genotyping assay (Supplementary Figure S4) under the T vs. C model.

Subgroup analysis data by the factor of race under the T vs. C model

Figure 2
Subgroup analysis data by the factor of race under the T vs. C model

 

Figure 2
Subgroup analysis data by the factor of race under the T vs. C model

 

Table 3
Subgroup analysis data by the factors of race and control source
Genetic model Subgroup Sample size Association 
  Study Case/Control Passociation OR (95% CI) 
T vs. C Asian 38 12118/18124 0.360 1.03 (0.97, 1.10) 
 Caucasian 22 9371/12338 0.572 1.03 (0.92, 1.16) 
 PB 52 17758/25317 0.447 1.03 (0.96, 1.10) 
 HB 17 7940/10808 0.109 1.04 (0.99, 1.09) 
TT vs. CC Asian 38 12118/18124 0.091 1.11 (0.98, 1.25) 
 Caucasian 22 9371/12338 0.329 1.15 (0.87, 1.53) 
 PB 52 17758/25317 0.397 1.07 (0.92, 1.23) 
 HB 17 7940/10808 0.010 1.17 (1.04, 1.32) 
CT vs. CC Asian 38 12118/18124 0.554 0.98 (0.90, 1.06) 
 Caucasian 22 9371/12338 0.483 0.96 (0.86, 1.07) 
 PB 52 17758/25317 0.612 0.98 (0.91, 1.06) 
 HB 17 7940/10808 0.857 0.99 (0.92, 1.07) 
CT+TT vs. CC Asian 38 12118/18124 0.948 1.00 (0.92, 1.09) 
 Caucasian 22 9371/12338 0.905 0.99 (0.88, 1.12) 
 PB 52 17758/25317 0.965 1.00 (0.92, 1.09) 
 HB 17 7940/10808 0.581 1.02 (0.95, 1.09) 
TT vs. CC+ CT Asian 38 12118/18124 0.005 1.13 (1.04, 1.23) 
 Caucasian 22 9371/12338 0.293 1.14 (0.89, 1.45) 
 PB 52 17758/25317 0.219 1.08 (0.96, 1.21) 
 HB 17 7940/10808 0.006 1.18 (1.05, 1.32) 
Genetic model Subgroup Sample size Association 
  Study Case/Control Passociation OR (95% CI) 
T vs. C Asian 38 12118/18124 0.360 1.03 (0.97, 1.10) 
 Caucasian 22 9371/12338 0.572 1.03 (0.92, 1.16) 
 PB 52 17758/25317 0.447 1.03 (0.96, 1.10) 
 HB 17 7940/10808 0.109 1.04 (0.99, 1.09) 
TT vs. CC Asian 38 12118/18124 0.091 1.11 (0.98, 1.25) 
 Caucasian 22 9371/12338 0.329 1.15 (0.87, 1.53) 
 PB 52 17758/25317 0.397 1.07 (0.92, 1.23) 
 HB 17 7940/10808 0.010 1.17 (1.04, 1.32) 
CT vs. CC Asian 38 12118/18124 0.554 0.98 (0.90, 1.06) 
 Caucasian 22 9371/12338 0.483 0.96 (0.86, 1.07) 
 PB 52 17758/25317 0.612 0.98 (0.91, 1.06) 
 HB 17 7940/10808 0.857 0.99 (0.92, 1.07) 
CT+TT vs. CC Asian 38 12118/18124 0.948 1.00 (0.92, 1.09) 
 Caucasian 22 9371/12338 0.905 0.99 (0.88, 1.12) 
 PB 52 17758/25317 0.965 1.00 (0.92, 1.09) 
 HB 17 7940/10808 0.581 1.02 (0.95, 1.09) 
TT vs. CC+ CT Asian 38 12118/18124 0.005 1.13 (1.04, 1.23) 
 Caucasian 22 9371/12338 0.293 1.14 (0.89, 1.45) 
 PB 52 17758/25317 0.219 1.08 (0.96, 1.21) 
 HB 17 7940/10808 0.006 1.18 (1.05, 1.32) 

Abbreviations: PB, population-based; Passociation, P-value in the association test.

Additionally, we performed a subgroup analysis using the specific cancer type. As shown in Table 4, in the subgroup of ‘bladder cancer’ with 3460 cases and 3613 controls, enhanced susceptibility was detected in bladder cancer cases under T vs. C [Table 4, Passociation=0.004, OR = 1.25, 95% CI = (1.07, 1.45)], TT vs. CC [Passociation=0.001, OR = 1.68, 95% CI = (1.25, 2.26)], CT+TT vs. CC [Passociation=0.016, OR = 1.26, 95% CI = (1.04, 1.53)], TT vs. CC+ CT [Passociation= 0.001, OR = 1.49, 95% CI = (1.18, 1.90)] compared with the negative controls. Additionally, there is an increased risk of breast cancer under T vs. C [Table 4, Passociation=0.018, OR = 1.11, 95% CI = (1.02, 1.21)], TT vs. CC [Passociation=0.003, OR = 1.33, 95% CI = (1.10, 1.60)], and TT vs. CC+ CT [Passociation= 0.001, OR = 1.29, 95% CI = (1.12, 1.48)]. Nevertheless, we observed a decreased risk of gastric cancer in the Chinese population under T vs. C [Table 4, Passociation=0.020, OR = 0.92, 95% CI = (0.85, 0.99)], CT vs. CC [Passociation=0.001, OR = 0.83, 95% CI = (0.73, 0.93)], CT+TT vs. CC [Passociation=0.003, OR = 0.84, 95% CI = (0.76, 0.94)]. The relevant forest plots under different genetic models are presented in Figure 3 (T vs. C), Supplementary Figure S5 (TT vs. CC), Supplementary Figure S6 (CT vs. CC), Supplementary Figure S7 (CT+TT vs. CC), and Supplementary Figure S8 (TT vs. CC+ CT).

Subgroup analysis data by the factor of cancer type under the T vs. C model

Figure 3
Subgroup analysis data by the factor of cancer type under the T vs. C model

 

Figure 3
Subgroup analysis data by the factor of cancer type under the T vs. C model

 

Table 4
Subgroup analysis data by the factors of specific cancer type
Genetic model Subgroup Sample size Association 
  Study Case/Control Passociation OR (95% CI) 
T vs. C Bladder cancer 3460/3613 0.004 1.25 (1.07, 1.45) 
 Lung cancer 10 2642/6319 0.222 1.05 (0.97, 1.13) 
 Gastric cancer 2849/3655 0.020 0.92 (0.85, 0.99) 
 Melanoma 2904/3544 0.250 0.92 (0.81, 1.06) 
 Esophageal cancer 898/1265 0.210 0.83 (0.62, 1.11) 
 Breast cancer 13 4762/5937 0.018 1.11 (1.02, 1.21) 
 Pancreatic cancer 872/1025 0.380 1.07 (0.92, 1.23) 
 CRC 3602/4924 0.776 0.97 (0.76, 1.23) 
TT vs. CC Bladder cancer 3460/3613 0.001 1.68 (1.25, 2.26) 
 Lung cancer 10 2642/6319 0.252 1.11 (0.93, 1.34) 
 Gastric cancer 2849/3655 0.361 0.93 (0.78, 1.09) 
 Melanoma 2904/3544 0.697 0.90 (0.55, 1.50) 
 Esophageal cancer 898/1265 0.724 0.89 (0.46, 1.71) 
 Breast cancer 13 4762/5937 0.003 1.33 (1.10, 1.60) 
 Pancreatic cancer 872/1025 0.952 0.99 (0.69, 1.41) 
 CRC 3602/4924 0.588 0.87 (0.52, 1.45) 
CT vs. CC Bladder cancer 3460/3613 0.069 1.17 (0.99, 1.39) 
 Lung cancer 10 2642/6319 0.368 1.05 (0.95, 1.16) 
 Gastric cancer 2849/3655 0.001 0.83 (0.73, 0.93) 
 Melanoma 2904/3544 0.157 0.93 (0.83, 1.03) 
 Esophageal cancer 898/1265 0.013 0.73 (0.57, 0.94) 
 Breast cancer 13 4762/5937 0.418 1.04 (0.94, 1.16) 
 Pancreatic cancer 872/1025 0.128 1.16 (0.96, 1.40) 
 CRC 3602/4924 0.405 0.91 (0.73, 1.13) 
CT+TT vs. CC Bladder cancer 3460/3613 0.016 1.26 (1.04, 1.53) 
 Lung cancer 10 2642/6319 0.282 1.05 (0.96, 1.16) 
 Gastric cancer 2849/3655 0.003 0.84 (0.76, 0.94) 
 Melanoma 2904/3544 0.088 0.92 (0.83, 1.01) 
 Esophageal cancer 898/1265 0.065 0.74 (0.54, 1.02) 
 Breast cancer 13 4762/5937 0.175 1.08 (0.97, 1.21) 
 Pancreatic cancer 872/1025 0.182 1.13 (0.94, 1.36) 
 CRC 3602/4924 0.563 0.93 (0.71, 1.20) 
TT vs. CC+ CT Bladder cancer 3460/3613 0.001 1.49 (1.18, 1.90) 
 Lung cancer 10 2642/6319 0.293 1.10 (0.92, 1.32) 
 Gastric cancer 2849/3655 0.834 1.02 (0.87, 1.19) 
 Melanoma 2904/3544 0.826 0.94 (0.56, 1.59) 
 Esophageal cancer 898/1265 0.889 1.04 (0.61, 1.78) 
 Breast cancer 13 4762/5937 0.001 1.29 (1.12, 1.48) 
 Pancreatic cancer 872/1025 0.669 0.94 (0.66, 1.31) 
 CRC 3602/4924 0.682 0.91 (0.58, 1.43) 
Genetic model Subgroup Sample size Association 
  Study Case/Control Passociation OR (95% CI) 
T vs. C Bladder cancer 3460/3613 0.004 1.25 (1.07, 1.45) 
 Lung cancer 10 2642/6319 0.222 1.05 (0.97, 1.13) 
 Gastric cancer 2849/3655 0.020 0.92 (0.85, 0.99) 
 Melanoma 2904/3544 0.250 0.92 (0.81, 1.06) 
 Esophageal cancer 898/1265 0.210 0.83 (0.62, 1.11) 
 Breast cancer 13 4762/5937 0.018 1.11 (1.02, 1.21) 
 Pancreatic cancer 872/1025 0.380 1.07 (0.92, 1.23) 
 CRC 3602/4924 0.776 0.97 (0.76, 1.23) 
TT vs. CC Bladder cancer 3460/3613 0.001 1.68 (1.25, 2.26) 
 Lung cancer 10 2642/6319 0.252 1.11 (0.93, 1.34) 
 Gastric cancer 2849/3655 0.361 0.93 (0.78, 1.09) 
 Melanoma 2904/3544 0.697 0.90 (0.55, 1.50) 
 Esophageal cancer 898/1265 0.724 0.89 (0.46, 1.71) 
 Breast cancer 13 4762/5937 0.003 1.33 (1.10, 1.60) 
 Pancreatic cancer 872/1025 0.952 0.99 (0.69, 1.41) 
 CRC 3602/4924 0.588 0.87 (0.52, 1.45) 
CT vs. CC Bladder cancer 3460/3613 0.069 1.17 (0.99, 1.39) 
 Lung cancer 10 2642/6319 0.368 1.05 (0.95, 1.16) 
 Gastric cancer 2849/3655 0.001 0.83 (0.73, 0.93) 
 Melanoma 2904/3544 0.157 0.93 (0.83, 1.03) 
 Esophageal cancer 898/1265 0.013 0.73 (0.57, 0.94) 
 Breast cancer 13 4762/5937 0.418 1.04 (0.94, 1.16) 
 Pancreatic cancer 872/1025 0.128 1.16 (0.96, 1.40) 
 CRC 3602/4924 0.405 0.91 (0.73, 1.13) 
CT+TT vs. CC Bladder cancer 3460/3613 0.016 1.26 (1.04, 1.53) 
 Lung cancer 10 2642/6319 0.282 1.05 (0.96, 1.16) 
 Gastric cancer 2849/3655 0.003 0.84 (0.76, 0.94) 
 Melanoma 2904/3544 0.088 0.92 (0.83, 1.01) 
 Esophageal cancer 898/1265 0.065 0.74 (0.54, 1.02) 
 Breast cancer 13 4762/5937 0.175 1.08 (0.97, 1.21) 
 Pancreatic cancer 872/1025 0.182 1.13 (0.94, 1.36) 
 CRC 3602/4924 0.563 0.93 (0.71, 1.20) 
TT vs. CC+ CT Bladder cancer 3460/3613 0.001 1.49 (1.18, 1.90) 
 Lung cancer 10 2642/6319 0.293 1.10 (0.92, 1.32) 
 Gastric cancer 2849/3655 0.834 1.02 (0.87, 1.19) 
 Melanoma 2904/3544 0.826 0.94 (0.56, 1.59) 
 Esophageal cancer 898/1265 0.889 1.04 (0.61, 1.78) 
 Breast cancer 13 4762/5937 0.001 1.29 (1.12, 1.48) 
 Pancreatic cancer 872/1025 0.669 0.94 (0.66, 1.31) 
 CRC 3602/4924 0.682 0.91 (0.58, 1.43) 

Abbreviations: CRC, colorectal cancer; Passociation, P-value in the association test.

Moreover, we performed subgroup analysis data for different system cancers. As shown in Supplementary Table S5 and Figure S9 (forest plot data under the allelic model), we observed the same result in the subgroup of ‘urinary system cancer’ as the subgroup of ‘bladder cancer’. There is a reduced cancer risk in the subgroup of ‘reproductive system cancer’ under the models of CT vs. CC [Passociation=0.006, OR = 0.81, 95% CI = (0.70, 0.94)] and CT+TT vs. CC [Passociation=0.041, OR = 0.82, 95% CI = (0.68, 0.99)] and an increased risk in the subgroup of ‘head and neck cancer’ under the TT vs. CC+CT [Passociation=0.024, OR = 1.58, 95% CI = (1.06, 2.34)]. However, no positive association was observed in other subgroups (Supplementary Table S5, Passociation>0.05).

The above results indicated that the TT genotype of XPC rs2228000 seems to be related to a high risk of bladder and breast cancer, whereas the CT genotype is more likely to be associated with reduced susceptibility to gastric cancer in the Chinese population.

Publication bias/sensitivity

As shown in Table 2, we did not observe a notable publication bias among these comparisons, in that all the PBegg>0.05, PEgger>0.05 apart from the PEgger=0.031 (T vs. C), PEgger=0.046 (CT vs. CC), PEgger=0.023 (CT+TT vs. CC). Figure 4A presents the publication bias plot of Egger’s test under the T vs. C model. In addition, as shown in Figure 4B (allelic model data as example), we also observed relatively stable pooling data through the performance of sensitivity analyses.

Egger’s test plot and the sensitivity analysis data under the T vs. C model

Figure 4
Egger’s test plot and the sensitivity analysis data under the T vs. C model

(A) Egger’s test; (B) sensitivity analysis data.

Figure 4
Egger’s test plot and the sensitivity analysis data under the T vs. C model

(A) Egger’s test; (B) sensitivity analysis data.

FPRP/TSA

An FPRP test was conducted to confirm the above positive findings for bladder, breast, and gastric cancers. The FPRP values of positive results at different prior probability levels are shown in Supplementary Table S6. We found that at a prior probability of 0.1 with an OR of 1.5, all the FPRP values were less than 0.2 (Supplementary Table S6, FPRP = 0.028, T vs. C; FPRP = 0.023, TT vs. CC; FPRP = 0.155, CT+TT vs. CC; FPRP = 0.022, TT vs. CC+ CT), indicating a noteworthy association between XPC rs2228000 and the risk of bladder cancer. Similar true positive associations were observed for breast and gastric cancer (Supplementary Table S6, all FPRP < 0.02) at a prior probability of 0.1.

In addition, we also performed the TSA test to assess the robustness of our significant findings. As shown in the TSA data of breast cancer under the TT vs. CC+CT model (Figure 5) and gastric cancer under the CT+TT vs. CC models (Supplementary Figure S10), we found that the cumulative number of participants (Z-curve) met the TSA monitoring boundary and required information size. With regard to the bladder cancer under the TT vs. CC+CT model (Supplementary Figure S11), the cumulative Z-curve crossed with the TSA monitoring boundary, even though it did not reach the required information size. These data therefore indicated the robustness of our conclusions.

TSA for the association between XPC rs2228000 and the risk of breast cancer under the TT vs. CC+CT model

Figure 5
TSA for the association between XPC rs2228000 and the risk of breast cancer under the TT vs. CC+CT model

 

Figure 5
TSA for the association between XPC rs2228000 and the risk of breast cancer under the TT vs. CC+CT model

 

Discussion

There is a controversial conclusion regarding the genetic impacts of the XPC rs2228000 SNP in the risk of clinical cancer diseases in different publications. For example, XPC rs2228000 was reportedly related to susceptibility to bladder cancer cases in Iraq [27], Sweden [30], or India [67] but not the U.S.A. [85] or Spain [35]. Likewise, XPC rs2228000 was also significantly associated with the risk of breast cancer in a Chinese population [37,78] but not Caucasians or African-Americans in the U.S.A. [72]. Although several meta-analyses of XPC rs2228000 and certain specific cancer types exist [86–92], differences in study enrolment, data extraction, analysis strategy, and result descriptions were observed. We thus conducted a meta-analysis and TSA for a comprehensive assessment regarding the genetic influence of the XPC rs2228000 in the risk of various types of cancer, including bladder cancer, lung cancer, gastric cancer, melanoma, esophageal cancer, breast cancer, pancreatic cancer, and colorectal cancer.

Only three prior meta-analyses with fewer than 15 studies in 2008 [12–14] and one meta-analysis with 33 articles in 2013 [15] were reported to detect the genetic association between XPC rs2228000 and overall cancer risk. In our study, we retrieved four databases (updated till September 2019) to include the potential publication for the pooling analysis. After employing our strict screen strategy, we finally included 64 eligible articles, which contained 71 case–control studies, for the overall meta-analysis and the following subgroup analyses by the factors of race, country, control source, article quality, genotyping assay, and cancer type. Five genetic models, including allelic, homozygotic, heterozygotic, dominant, and recessive models, were utilized. We excluded the improper studies according to the strict requirement of full genotype frequency data and the HWE principle. For instance, there are a total of 33 articles with 14877 cases and 17888 controls [1,28,30–32,36,39,42,44,45,47,48,50,54–56,60,64–66,68–70,72,75–79,82,93–95] for the prior meta-analysis of He et al. in 2013 [15]. In this study, we excluded two articles regarding bladder cancer [95] and cutaneous melanoma [94] because the genotype distribution in the control group is not in line with the HWE, and we added 32 other published articles [4,10,11,27,29,33–35,37,38,40,41,43,46,49,51,52,57–59,61–63,67,71,73,74,80,81,83–85]. Our pooling data from eight case–control studies showed the genetic correlation between XPC rs2228000 and increased risk of bladder cancer under the allelic, homozygotic, heterozygotic, dominant, and recessive models, which is partly consistent with the positive data of He et al. (2013) [15] under the homozygotic and recessive models from four case–control studies. A similar result was obtained for breast cancer, even though four new case–control studies were added, compared with the pooling results of He et al. (2013) [15]. Moreover, we provided assessment evidence regarding the potential impact of XPC rs2228000 on the reduced susceptibility to gastric cancer in the Chinese population. Nevertheless, we did not detect a significant association between XPC rs2228000 and other types of cancer, such as lung cancer, melanoma, pancreatic cancer, or colorectal cancer.

In our study, we performed the FPRP test with a prior probability of 0.1 and an FPRP threshold of 0.2 to check whether the positive findings of breast, bladder, and gastric cancers are noteworthy, considering the potential presence of ‘false positives’. After the FPRP estimation, the genetic association between XPC rs2228000 and the risk of bladder, breast, and gastric cancers risk remain significant at the prior probability level of 0.1. Furthermore, we observed the robustness of our conclusions through the performance of TSA test and sensitivity analyses and the absence of large publication bias by Begg’s/Egger’s test.

Despite these findings, some limitations to this research may still influence the statistical power of analyses of certain types of cancer. Although more than 70 case–control studies were enrolled in the overall meta-analysis, small sample sizes were still included in some subgroup analyses. For example, only two case–control studies [56,74] were included for the subgroup of ‘blood system cancer’, while only two studies [81,83] were enrolled for ‘nervous system cancer’. Therefore, we still cannot rule out the possible genetic role played by XPC rs2228000 in the risk of cancers of the blood or nervous systems. A similar uncertainty also exists in the subgroup analysis of ‘lung cancer’, ‘melanoma’, ‘esophageal cancer’, ‘pancreatic cancer’ and ‘CRC’.

We observed clear between-study heterogeneity, even if articles with low quality are removed. Regarding the available sample size, more factors, such as gender, age, environmental exposure, drinking/smoking status, tumor situations, characteristics, antiepileptic agents, or drug resistance, should be adjusted in future pooling analyses. It would be valuable to carry out an integrated analysis to evaluate the combined role of more XPC polymorphic loci (e.g., rs2228001, PAT−/+) in susceptibility to different types of cancer based on the available evidence.

Conclusions

In general, the TT genotype of XPC rs2228000 may be linked to an increased risk of bladder and breast cancers, whereas the CT genotype is more likely to be associated with a reduced susceptibility to gastric cancer in the Chinese population. Considering the limitations of our study, we need to analyze more publications to verify the genetic impact of XPC rs2228000 in other types of cancer.

Acknowledgments

The authors are grateful to American Journal Experts for providing English language editing.

Author Contribution

Y.D., Z.S., and J.Z. conducted the database search and study screening. Y.D., Z.S., and W.G. summarized the evidence, performed the pooling analysis, FPRP, and TSA tests. Y.D. and J.Z. wrote the manuscript. All the authors reviewed and approved the final version.

Competing Interests

The authors declare that there are no competing interests associated with the manuscript.

Funding

This work was in part supported by the National Natural Science Fund Grant [grant numbers 81471761, 81501568]; the Tianjin Science and Technology Support Plan Key Projects [grant number 15ZCZDSY00890]; and the Tianjin Medical University Cancer Hospital Project [grant number 1711].

Abbreviations

     
  • CI

    confidence interval

  •  
  • CNKI

    China National Knowledge Infrastructure

  •  
  • FPRP

    false-positive report probability

  •  
  • HB

    hospital-based

  •  
  • HWE

    Hardy–Weinberg equilibrium

  •  
  • NER

    nucleotide excision repair

  •  
  • NOS

    Newcastle–Ottawa quality assessment Scale

  •  
  • OR

    odds ratio

  •  
  • PAT−/+

    poly-AT insertion/deletion polymorphism

  •  
  • TSA

    trial sequential analysis

  •  
  • WOS

    Web of Science

  •  
  • XPC

    xeroderma pigmentosum complementation group C

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