Abstract

Background: Previous case–control studies have suggested that apurinic/apyrimidinic endonuclease 1 (APE1) rs1760944 T>G polymorphism may be associated with cancer risk. Here, we carried out an updated meta-analysis to focus on the correlation between APE1 rs1760944 T>G locus and the risk of cancer.

Methods: We used the crude odds ratios (ORs) with their 95% confidence intervals (CIs) to evaluate the possible relationship between the APE1 rs1760944 T>G polymorphism and cancer risk. Heterogeneity, publication bias and sensitivity analysis were also harnessed to check the potential bias of the present study.

Results: Twenty-three independent studies involving 10166 cancer cases and 11598 controls were eligible for this pooled analysis. We found that APE1 rs1760944 T>G polymorphism decreased the risk of cancer in four genetic models (G vs. T: OR, 0.87; 95% CI, 0.83–0.92; P<0.001; GG vs. TT: OR, 0.77; 95% CI, 0.69–0.86; P<0.001; GG/TG vs. TT: OR, 0.83; 95% CI, 0.77–0.89, P<0.001 and GG vs. TT/TG: OR, 0.85; 95% CI, 0.80–0.92, P<0.001). Results of subgroup analyses also demonstrated that this single-nucleotide polymorphism (SNP) modified the risk among lung cancer, breast cancer, osteosarcoma, and Asians. Evidence of publication bias was found in the present study. When we treated the publication bias with ‘trim-and-fill’ method, the adjusted ORs and CIs were not significantly changed.

Conclusion: In conclusion, current evidence highlights that the APE1 rs1760944 T>G polymorphism is a protective factor for cancer susceptibility. In the future, case–control studies with detailed risk factors are needed to confirm or refute our findings.

Introduction

The incidence and mortality of cancer is increasing worldwide [1–3]. It was estimated that approximately 18.1 million new cancer patients were diagnosed and more than half of them died worldwide during 2018 [1]. The etiology of cancer is complicated. Previous epidemiological studies have indicated that consumption of red meat, fried and salted meat, tobacco smoking and alcohol abuse, diabetes mellitus, obesity, non-alcoholic fatty liver disease, oxidative stress, chronic infection and inflammation can contribute to the development of cancer [4–6]. However, these potential risk factors could not fully explain the etiology of cancer. It is reported that the hereditary factor may influence the susceptibility of cancer [7,8].

Apurinic/apyrimidinic endonuclease 1 (APE1) is a multifunctional protein which plays an important role in the pathway of base excision repair (BER). APE1 plays a pivotal role in tumor cells involving DNA damage response and regulating transcription factor activation [9]. The observed roles of APE1 protein allude to its potential effect on inflammation, growth, migration and angiogenesis [9–11]. In addition, APE1 may be also implicated in regulating cell cycle, oxidative stress and apoptosis [12]. Recently, some investigations reported that the expression level of APE1 was up-regulated in a number of cancers [13–17]. In addition, glioma cell with higher APE1 expression level was also associated with shorter time to tumor progression after chemo/radiotherapy [18,19]. As well, previous studies reported that the decreased APE1 activity might retard cell growth of ovarian cancer [20] and pancreatic cancer [21].

APE1 gene is approximately 3 kb in length and is located on chromosome 14q11.2 [22]. A number of variants in APE1 gene are established (https://www.ncbi.nlm.nih.gov/snp/?term=APE1). APE1 rs1760944 (−656T>G) is a promoter locus and has been widely explored. Some functional studies indicated that the APE1 rs1760944 T>G single-nucleotide polymorphism (SNP) might decrease APE1 mRNA and protein expression levels [23,24]. Many case–control studies were conducted to identify the potential association of APE1 rs1760944 T>G polymorphism with the development of cancer. Individuals with APE1 rs1760944 GG variant might reduce 46% glioblastoma risk than those who carried APE1 rs1760944 TT variant [25]. The relationship between APE1 rs1760944 T>G polymorphism and a decreased susceptibility of lung cancer was also found by Lu et al. [24]. A previous study reported that gastric cancer cases carried APE1 rs1760944 GT/GG variants might have a better survival than others with APE1 rs1760944 TT genotype [26]. But the results were conflicting. Two meta-analyses suggested that this SNP was correlated with a decreased susceptibility of cancer in Asian populations and lung cancer [27,28]. Recently, many investigations focused on the association between APE1 rs1760944 T>G polymorphism and the risk of other cancers. The findings were more confusing. The aim of the present study was to carry out a meta-analysis to evaluate whether this SNP was associated with the risk of cancer.

Materials and methods

Literature search parameters

PubMed and Embase databases were exhaustively searched for relevant publications which studied the relationship of APE1 rs1760944 T>G locus with the risk of cancer from the inception up to 17 March 2019. The search strategy was: (polymorphism OR SNP) and (apurinic/apyrimidinic endonuclease 1 or APE1 or APE-1) and (cancer OR carcinoma). In the current study, publications written in English or Chinese were eligible. Moreover, the references of the included studies, comments, meta-analyses and reviews were manually retrospected to recruit the potential literatures.

Inclusion criterion

For eligibility, publications were required to meet the following inclusion criteria: (1) case–control studies investigating the relationship between the APE1 rs1760944 T>G locus and the risk of cancer; (2) the diagnosis of cases was confirmed by pathological examination; (3) the frequencies of alleles or genotypes were presented; (4) the paper was written in English or Chinese.

Exclusion criteria

Studies were excluded based on the major exclusion criteria: (1) not case–control design; (2) studies did not provide genotyping data on APE1 rs1760944 T>G polymorphism; and (3) meta-analyses/reviews, comments and letters focusing on the relationships between the APE1 rs1760944 T>G locus and cancer risk.

Data extraction

Two authors (Guowen Ding and Yu Chen) reviewed each eligible study independently. They extracted the following terms from case–control studies, including the first author name, publishing year, country where the study was carried out, ethnicity, the source of control, cancer type, numbers of included cases and controls in each case–control study, genotyping data, the method of polymerase chain reaction, statistical method and evidence of Hardy–Weinberg equilibrium (HWE) evaluation in control group. If the extracted data had any dispute, authors settled these issues following a detailed discussion among all reviewers.

Statistical analysis

HWE in controls was assessed by an online Pearson’s χ2 test (http://ihg.gsf.de/cgi-bin/hw/hwa1.pl). We calculated crude odds ratios (ORs) and 95% confidence intervals (CIs) to evaluate the correlation of APE1 rs1760944 T>G polymorphism and cancer risk. The following four genetic models were used, including homozygote model (GG vs. TT), dominant model (GG/TG vs. TT), recessive model (GG vs. TT/TG) and allele model (G vs. T). Cochran’s Q-statistic and I2 test were used to check the heterogeneity among the included studies. The random-effect model was harnessed when I2 > 50% or P<0.10 [29]; otherwise, a fixed-effect model was used [30]. Subgroup analyses were performed to explore the heterogeneity source among the studies. Ethnicity, the source of control and cancer type was considered as the potential source of heterogeneity. Begg’s funnel plots and Egger’s linear regression test were used to detect the potential bias in this meta-analysis. Since significant bias was identified in the present study, non-parametric ‘trim-and-fill’ method was used to evaluate the stability of the observed results. Sensitivity analysis was conducted by one-way method, which deleted each study one by one and re-calculated the pooled ORs and CIs. All statistical analyses were conducted by using STATA 12.0 (Stata Corporation, TX, U.S.A.). A P-value (two-sided) <0.05 was defined as statistically significant.

Results

Characteristics of eligible case–control studies

Figure 1 shows the selection process of the eligible publications. A total of 343 papers were collected. According to the major inclusion criteria, there were 20 papers (including 23 independent case–control studies) focusing on the relationship of APE1 rs1760944 T>G polymorphism with cancer risk [23–25,31–47]. Among them, five investigated lung cancer [24,31–33], three investigated colorectal cancer [34–36], three investigated breast cancer [37–39], three investigated cervical cancer [40,41], two investigated osteosarcoma [23], two investigated nasopharyngeal carcinoma [42,43] and five investigated other cancers (bladder cancer [44], glioblastoma [25], renal cell carcinoma [45], prostate cancer [46] and ovarian cancer [47]). Additionally, twenty-one had Asian and two had Caucasian ethnicities. In all included studies, χ2 test was used to calculate the pooled ORs and CIs. The detailed characteristics of the included case–control studies are shown in Table 1. The number of each genotype and HWE are presented in Table 2.

Flow diagram of included and excluded processes

Figure 1
Flow diagram of included and excluded processes
Figure 1
Flow diagram of included and excluded processes
Table 1
Characteristics of all included studies in the meta-analysis
Author Year Country Ethnicity The type of cancer Genotyping method Source of control Sample size (case/control) Statistical methods 
Berndt et al. 2007 U.S.A. Caucasians Advanced colorectal adenoma Taqman PB 767/720 χ2 test 
Lu et al. 2009 China Asians Lung cancer Illumina PB 500/517 χ2 test, SPSS 15.0 
Lo et al. 2009 China Asians Lung cancer MassARRAY HB 730/730 χ2 test, SAS 
Lu et al. 2009 China Asians Lung cancer Illumina HB 572/547 χ2 test, SPSS 15.0 
Wang et al. 2010 China Asians Bladder cancer PCR-RFLP HB 234/253 χ2 test, SAS 
Zhou et al. 2011 China Asians Glioblastoma MALDI-TOF HB 766/824 χ2 test, SPSS 15.0 
Li et al. 2011 China Asians Lung cancer PCR-CTPP HB 455/443 χ2 test, SPSS 16.0 
Cao et al. 2011 China Asians Renal cell carcinoma TaqMan HB 612/632 χ2 test, t test, SAS 
Wang et al. 2013 China Asians Cervical cancer PCR-RFLP HB 306/306 χ2 test, t test, SAS 
Jing et al. 2013 China Asians Prostate cancer PCR-RFLP HB 198/156 χ2 test, SPSS 16.0 
Kang et al. 2013 China Asians Breast cancer TaqMan HB 500/799 χ2 test, SAS 
Pan et al. 2013 China Asians Lung cancer PCR-LDR HB 819/803 χ2 test, Open-source R software 
Zhang et al. 2013 China Asians Ovarian cancer DNA sequence HB 124/141 χ2 test, SPSS 16.0 
Li et al. 2013 China Asians Nasopharyngeal carcinoma PCR-CTPP HB 231/300 χ2 test, SPSS 16.0 
Zhang et al. 2014 China Asians Colorectal cancer PCR-CTPP HB 247/300 χ2 test, SPSS 19.0 
Luo et al. 2014 China Asians Breast cancer PCR-CTPP HB 194/245 χ2 test, SPSS 16.0 
Mashayekhi et al. 2015 Iran Caucasians Breast cancer T-ARMS-PCR HB 150/150 χ2 test, Medcalc software 12.1 
Lai et al. 2016 China Asians Colorectal cancer High resolution melting assay HB 727/736 χ2 test, SAS9.2 
Meng et al. 2017 China Asians Cervical cancer TaqMan HB 571/657 χ2 test 
Meng et al. 2017 China Asians Cervical cancer TaqMan HB 608/1165 χ2 test 
Xiao et al. 2017 China Asians Osteosarcoma TaqMan HB 172/256 χ2 test, SPSS 22.0, GraphPad Prism 6.0 
Xiao et al. 2017 China Asians Osteosarcoma TaqMan HB 206/360 χ2 test, SPSS 22.0, GraphPad Prism 6.0 
Lu et al. 2017 China Asians Nasopharyngeal carcinoma MassARRAY HB 477/558 χ2 test, SPSS 17.0 
Author Year Country Ethnicity The type of cancer Genotyping method Source of control Sample size (case/control) Statistical methods 
Berndt et al. 2007 U.S.A. Caucasians Advanced colorectal adenoma Taqman PB 767/720 χ2 test 
Lu et al. 2009 China Asians Lung cancer Illumina PB 500/517 χ2 test, SPSS 15.0 
Lo et al. 2009 China Asians Lung cancer MassARRAY HB 730/730 χ2 test, SAS 
Lu et al. 2009 China Asians Lung cancer Illumina HB 572/547 χ2 test, SPSS 15.0 
Wang et al. 2010 China Asians Bladder cancer PCR-RFLP HB 234/253 χ2 test, SAS 
Zhou et al. 2011 China Asians Glioblastoma MALDI-TOF HB 766/824 χ2 test, SPSS 15.0 
Li et al. 2011 China Asians Lung cancer PCR-CTPP HB 455/443 χ2 test, SPSS 16.0 
Cao et al. 2011 China Asians Renal cell carcinoma TaqMan HB 612/632 χ2 test, t test, SAS 
Wang et al. 2013 China Asians Cervical cancer PCR-RFLP HB 306/306 χ2 test, t test, SAS 
Jing et al. 2013 China Asians Prostate cancer PCR-RFLP HB 198/156 χ2 test, SPSS 16.0 
Kang et al. 2013 China Asians Breast cancer TaqMan HB 500/799 χ2 test, SAS 
Pan et al. 2013 China Asians Lung cancer PCR-LDR HB 819/803 χ2 test, Open-source R software 
Zhang et al. 2013 China Asians Ovarian cancer DNA sequence HB 124/141 χ2 test, SPSS 16.0 
Li et al. 2013 China Asians Nasopharyngeal carcinoma PCR-CTPP HB 231/300 χ2 test, SPSS 16.0 
Zhang et al. 2014 China Asians Colorectal cancer PCR-CTPP HB 247/300 χ2 test, SPSS 19.0 
Luo et al. 2014 China Asians Breast cancer PCR-CTPP HB 194/245 χ2 test, SPSS 16.0 
Mashayekhi et al. 2015 Iran Caucasians Breast cancer T-ARMS-PCR HB 150/150 χ2 test, Medcalc software 12.1 
Lai et al. 2016 China Asians Colorectal cancer High resolution melting assay HB 727/736 χ2 test, SAS9.2 
Meng et al. 2017 China Asians Cervical cancer TaqMan HB 571/657 χ2 test 
Meng et al. 2017 China Asians Cervical cancer TaqMan HB 608/1165 χ2 test 
Xiao et al. 2017 China Asians Osteosarcoma TaqMan HB 172/256 χ2 test, SPSS 22.0, GraphPad Prism 6.0 
Xiao et al. 2017 China Asians Osteosarcoma TaqMan HB 206/360 χ2 test, SPSS 22.0, GraphPad Prism 6.0 
Lu et al. 2017 China Asians Nasopharyngeal carcinoma MassARRAY HB 477/558 χ2 test, SPSS 17.0 

Abbreviations: MALDI-TOF MS, matrix-assisted laser desorption/ionization time of flight mass spectrometry; PCR-CTPP, polymerase chain reaction with confronting two-pair primers; PCR-LDR, polymerase chain reaction-ligase detection reaction; PCR-RFLP: polymerase chain reaction-restriction fragment length polymorphism; T-ARMS-PCR, tetra-primer amplification refractory mutation system-polymerase chain reaction.

Table 2
Distribution of APE1 rs1760944 T>G polymorphism genotype and allele among cases and controls
Author Year case control case control HWE 
  TT TG GG TT TG GG  
Berndt et al. 2007 106 310 244 114 317 243 798 522 803 545 Yes 
Lu et al. 2009 184 241 75 170 238 109 391 609 456 578 Yes 
Lo et al. 2009 271 332 122 234 341 153 576 874 647 809 Yes 
Lu et al. 2009 199 288 85 149 293 105 458 686 503 591 Yes 
Wang et al. 2010 92 108 34 77 124 52 176 292 228 278 Yes 
Zhou et al. 2011 233 392 125 237 424 155 642 858 734 898 Yes 
Li et al. 2011 162 227 66 143 206 94 359 551 394 492 Yes 
Cao et al. 2011 170 307 135 191 307 134 577 647 575 689 Yes 
Wang et al. 2013 121 139 46 92 154 60 231 381 274 338 Yes 
Jing et al. 2013 78 93 27 47 76 33 147 249 142 170 Yes 
Kang et al. 2013 180 207 78 248 381 170 363 567 721 877 Yes 
Pan et al. 2013 114 384 321 98 369 336 1026 612 1041 565 Yes 
Zhang et al. 2013 48 52 24 46 65 30 100 148 125 157 Yes 
Li et al. 2013 71 126 34 94 143 63 194 268 269 331 Yes 
Zhang et al. 2014 93 102 52 93 140 67 206 288 274 326 Yes 
Luo et al. 2014 64 86 44 70 128 47 174 214 222 268 Yes 
Mashayekhi et al. 2015 58 80 12 41 102 104 196 116 184 No 
Lai et al. 2016 217 368 136 211 380 140 640 802 660 802 Yes 
Meng et al. 2017 182 285 104 211 324 122 493 649 568 746 Yes 
Meng et al. 2017 199 298 111 386 564 215 520 696 994 1336 Yes 
Xiao et al. 2017 80 70 22 86 121 49 114 230 219 293 Yes 
Xiao et al. 2017 83 93 30 108 178 74 153 259 326 394 Yes 
Lu et al. 2017 189 GT/GG = 288  179 GT/GG = 379      Yes 
Author Year case control case control HWE 
  TT TG GG TT TG GG  
Berndt et al. 2007 106 310 244 114 317 243 798 522 803 545 Yes 
Lu et al. 2009 184 241 75 170 238 109 391 609 456 578 Yes 
Lo et al. 2009 271 332 122 234 341 153 576 874 647 809 Yes 
Lu et al. 2009 199 288 85 149 293 105 458 686 503 591 Yes 
Wang et al. 2010 92 108 34 77 124 52 176 292 228 278 Yes 
Zhou et al. 2011 233 392 125 237 424 155 642 858 734 898 Yes 
Li et al. 2011 162 227 66 143 206 94 359 551 394 492 Yes 
Cao et al. 2011 170 307 135 191 307 134 577 647 575 689 Yes 
Wang et al. 2013 121 139 46 92 154 60 231 381 274 338 Yes 
Jing et al. 2013 78 93 27 47 76 33 147 249 142 170 Yes 
Kang et al. 2013 180 207 78 248 381 170 363 567 721 877 Yes 
Pan et al. 2013 114 384 321 98 369 336 1026 612 1041 565 Yes 
Zhang et al. 2013 48 52 24 46 65 30 100 148 125 157 Yes 
Li et al. 2013 71 126 34 94 143 63 194 268 269 331 Yes 
Zhang et al. 2014 93 102 52 93 140 67 206 288 274 326 Yes 
Luo et al. 2014 64 86 44 70 128 47 174 214 222 268 Yes 
Mashayekhi et al. 2015 58 80 12 41 102 104 196 116 184 No 
Lai et al. 2016 217 368 136 211 380 140 640 802 660 802 Yes 
Meng et al. 2017 182 285 104 211 324 122 493 649 568 746 Yes 
Meng et al. 2017 199 298 111 386 564 215 520 696 994 1336 Yes 
Xiao et al. 2017 80 70 22 86 121 49 114 230 219 293 Yes 
Xiao et al. 2017 83 93 30 108 178 74 153 259 326 394 Yes 
Lu et al. 2017 189 GT/GG = 288  179 GT/GG = 379      Yes 

Quantitative synthesis

A total of 23 independent case–control studies with 10166 cancer cases and 11598 controls were included to explore the potential correlation of APE1 rs1760944 T>G polymorphism with the susceptibility of cancer [23–25,31–47]. We found that APE1 rs1760944 T>G polymorphism conferred statistical evidence of the relationship between APE1 rs1760944 T>G locus and a decreased risk of cancer (G vs. T: OR, 0.87; 95% CI, 0.83–0.92 P<0.001; GG vs. TT: OR, 0.77; 95% CI, 0.69–0.86; P<0.001; GG/TG vs. TT: OR, 0.83; 95% CI, 0.77–0.89, P<0.001 and GG vs. TT/TG: OR, 0.85; 95% CI, 0.80–0.92, P<0.001; Table 3).

Table 3
Results of the meta-analysis from different genetic models
 Number of cases/controls G vs. T GG vs. TT GG/TG vs. TT GG vs. TT/TG 
  OR (95% CI) P I2 P (Q-test) OR (95% CI) P I2 P (Q-test) OR(95% CI) P I2 P (Q-test) OR (95% CI) P I2 P (Q-test) 
Total 9997/11537 0.87 (0.83–0.92) <0.001 43.9% 0.015 0.77 (0.69–0.86) <0.001 39.4% 0.031 0.83 (0.77–0.89) <0.001 35.8% 0.046 0.85 (0.800.92) <0.001 26.8% 0.121 
HWE                  
  Yes 9847/11387 0.87 (0.82–0.92) <0.001 46.4% 0.011 0.77 (0.69–0.85) <0.001 41.1% 0.026 0.83 (0.77–0.90) <0.001 35.1% 0.054 0.85 (0.79–0.91) <0.001 24.4% 0.151 
  No  0.84 (0.601.17) 0.310 1.21 (0.443.34) 0.710 0.60 (0.37–0.97) 0.038 1.78 (0.684.64) 0.241 
Ethnicity                  
  Caucasians 810/824 1.00 (0.871.15) 0.994 20.0% 0.264 1.09 (0.811.48) 0.573 0.0% 0.832 0.82 (0.471.45) 0.502 75.1% 0.045 1.07 (0.861.33) 0.539 11.5% 0.288 
  Asians 9187/10713 0.86 (0.82–0.91) <0.001 42.4% 0.024 0.75 (0.67–0.84) <0.001 36.3% 0.054 0.82 (0.76–0.89) <0.001 32.9% 0.073 0.83 (0.78–0.90) <0.001 17.7% 0.234 
Cancer type                  
  Colorectal cancer 1628/1705 0.97 (0.881.07) 0.607 0.0% 0.396 0.96 (0.791.17) 0.682 0.0% 0.510 0.93 (0.801.09) 0.388 15.3% 0.307 1.00 (0.861.17) 0.983 0.0% 0.873 
  Lung cancer 3071/3038 0.83 (0.78–0.90) <0.001 0.0% 0.717 0.68 (0.59–0.79) <0.001 0.0% 0.702 0.80 (0.72–0.90) <0.001 0.0% 0.795 0.77 (0.68–0.87) <0.001 10.8% 0.344 
  Cervical cancer 1485/2128 0.93 (0.791.09) 0.361 61.4% 0.075 0.87 (0.651.17) 0.367 51.5% 0.127 0.90 (0.711.15) 0.416 62.3% 0.071 0.93 (0.781.11) 0.433 0.0% 0.438 
  Breast cancer 809/1194 0.83 (0.73–0.95) 0.005 4.3% 0.352 0.75 (0.57–0.98) 0.034 37.6% 0.202 0.71 (0.59–0.86) <0.001 0.0% 0.635 1.04 (0.651.67) 0.870 62.1% 0.072 
  Nasopharyngeal cancer 708/858 0.89 (0.701.14) 0.355 0.71 (0.431.20) 0.204 0.84 (0.591.18) 0.316 58.5% 0.120 0.65 (0.411.03) 0.064 
  Osteosarcoma 378/616 0.69 (0.57–0.83) <0.001 0.0% 0.701 0.51 (0.35–0.75) 0.001 0.0% 0.823 0.61 (0.47–0.80) <0.001 0.0% 0.748 0.64 (0.45–0.91) 0.014 0.0% 0.867 
  Others 1918/1998 0.87 (0.751.02) 0.080 58.0% 0.049 0.77 (0.571.03) 0.083 54.7% 0.065 0.89 (0.781.02) 0.109 47.8% 0.105 0.87 (0.741.02) 0.079 22.3% 0.273 
Source of control                  
  Population-based 1160/1191 0.92 (0.731.17) 0.506 75.6% 0.043 0.83 (0.501.40) 0.494 78.5% 0.031 0.93 (0.771.13) 0.485 28.9% 0.236 0.84 (0.541.31) 0.448 80.5% 0.024 
  Hospital-based 8837/10346 0.87 (0.820.92) <0.001 41.3% 0.028 0.76 (0.68–0.85) <0.001 35.5% 0.059 0.82 (0.750.88) <0.001 36.7% 0.048 0.85 (0.790.92) <0.001 18.3% 0.226 
 Number of cases/controls G vs. T GG vs. TT GG/TG vs. TT GG vs. TT/TG 
  OR (95% CI) P I2 P (Q-test) OR (95% CI) P I2 P (Q-test) OR(95% CI) P I2 P (Q-test) OR (95% CI) P I2 P (Q-test) 
Total 9997/11537 0.87 (0.83–0.92) <0.001 43.9% 0.015 0.77 (0.69–0.86) <0.001 39.4% 0.031 0.83 (0.77–0.89) <0.001 35.8% 0.046 0.85 (0.800.92) <0.001 26.8% 0.121 
HWE                  
  Yes 9847/11387 0.87 (0.82–0.92) <0.001 46.4% 0.011 0.77 (0.69–0.85) <0.001 41.1% 0.026 0.83 (0.77–0.90) <0.001 35.1% 0.054 0.85 (0.79–0.91) <0.001 24.4% 0.151 
  No  0.84 (0.601.17) 0.310 1.21 (0.443.34) 0.710 0.60 (0.37–0.97) 0.038 1.78 (0.684.64) 0.241 
Ethnicity                  
  Caucasians 810/824 1.00 (0.871.15) 0.994 20.0% 0.264 1.09 (0.811.48) 0.573 0.0% 0.832 0.82 (0.471.45) 0.502 75.1% 0.045 1.07 (0.861.33) 0.539 11.5% 0.288 
  Asians 9187/10713 0.86 (0.82–0.91) <0.001 42.4% 0.024 0.75 (0.67–0.84) <0.001 36.3% 0.054 0.82 (0.76–0.89) <0.001 32.9% 0.073 0.83 (0.78–0.90) <0.001 17.7% 0.234 
Cancer type                  
  Colorectal cancer 1628/1705 0.97 (0.881.07) 0.607 0.0% 0.396 0.96 (0.791.17) 0.682 0.0% 0.510 0.93 (0.801.09) 0.388 15.3% 0.307 1.00 (0.861.17) 0.983 0.0% 0.873 
  Lung cancer 3071/3038 0.83 (0.78–0.90) <0.001 0.0% 0.717 0.68 (0.59–0.79) <0.001 0.0% 0.702 0.80 (0.72–0.90) <0.001 0.0% 0.795 0.77 (0.68–0.87) <0.001 10.8% 0.344 
  Cervical cancer 1485/2128 0.93 (0.791.09) 0.361 61.4% 0.075 0.87 (0.651.17) 0.367 51.5% 0.127 0.90 (0.711.15) 0.416 62.3% 0.071 0.93 (0.781.11) 0.433 0.0% 0.438 
  Breast cancer 809/1194 0.83 (0.73–0.95) 0.005 4.3% 0.352 0.75 (0.57–0.98) 0.034 37.6% 0.202 0.71 (0.59–0.86) <0.001 0.0% 0.635 1.04 (0.651.67) 0.870 62.1% 0.072 
  Nasopharyngeal cancer 708/858 0.89 (0.701.14) 0.355 0.71 (0.431.20) 0.204 0.84 (0.591.18) 0.316 58.5% 0.120 0.65 (0.411.03) 0.064 
  Osteosarcoma 378/616 0.69 (0.57–0.83) <0.001 0.0% 0.701 0.51 (0.35–0.75) 0.001 0.0% 0.823 0.61 (0.47–0.80) <0.001 0.0% 0.748 0.64 (0.45–0.91) 0.014 0.0% 0.867 
  Others 1918/1998 0.87 (0.751.02) 0.080 58.0% 0.049 0.77 (0.571.03) 0.083 54.7% 0.065 0.89 (0.781.02) 0.109 47.8% 0.105 0.87 (0.741.02) 0.079 22.3% 0.273 
Source of control                  
  Population-based 1160/1191 0.92 (0.731.17) 0.506 75.6% 0.043 0.83 (0.501.40) 0.494 78.5% 0.031 0.93 (0.771.13) 0.485 28.9% 0.236 0.84 (0.541.31) 0.448 80.5% 0.024 
  Hospital-based 8837/10346 0.87 (0.820.92) <0.001 41.3% 0.028 0.76 (0.68–0.85) <0.001 35.5% 0.059 0.82 (0.750.88) <0.001 36.7% 0.048 0.85 (0.790.92) <0.001 18.3% 0.226 

Bold values are statistically significant (P< 0.05).

When we conducted subgroup analyses according to the different populations, the findings indicated that APE1 rs1760944 T>G polymorphism might be a protective factor for the development of cancer in Asian population (G vs. T: OR, 0.86; 95% CI, 0.82–0.91 P<0.001; GG vs. TT: OR, 0.75; 95% CI, 0.67–0.84; P<0.001; GG/TG vs. TT: OR, 0.82; 95% CI, 0.76–0.89, P<0.001 and GG vs. TT/TG: OR, 0.83; 95% CI, 0.78–0.90, P<0.001; Figure 2).

Meta-analysis for the association of cancer risk with the APE1 rs1760944 T>G polymorphism (random-effect, allele comparing model)

Figure 2
Meta-analysis for the association of cancer risk with the APE1 rs1760944 T>G polymorphism (random-effect, allele comparing model)
Figure 2
Meta-analysis for the association of cancer risk with the APE1 rs1760944 T>G polymorphism (random-effect, allele comparing model)

When we conducted subgroup analyses according to cancer type, the results suggested that APE1 rs1760944 T>G polymorphism decreased the risk of lung cancer (G vs. T: OR, 0.83; 95% CI, 0.78–0.90, P<0.001; GG vs. TT: OR, 0.68; 95% CI, 0.59–0.79; P<0.001; GG/TG vs. TT: OR, 0.80; 95% CI, 0.72–0.90, P<0.001 and GG vs. TT/TG: OR, 0.77; 95% CI, 0.68–0.87, P<0.001), breast cancer (G vs. T: OR, 0.83; 95% CI, 0.73–0.95, P=0.005; GG vs. TT: OR, 0.75; 95% CI, 0.57–0.98; P=0.034 and GG/TG vs. TT: OR, 0.71; 95% CI, 0.59–0.86, P<0.001), and osteosarcoma (G vs. T: OR, 0.69; 95% CI, 0.57–0.83 P<0.001; GG vs. TT: OR, 0.51; 95% CI, 0.35–0.75; P=0.001; GG/TG vs. TT: OR, 0.61; 95% CI, 0.47–0.80, P<0.001 and GG vs. TT/TG: OR, 0.64; 95% CI, 0.45–0.91, P=0.014).

Publication bias and non-parametric ‘trim-and-fill’ method

In the present study, Begg’s and Egger’s tests were used to assess the potential bias among the eligible studies. Evidence of bias was found in the present study (G vs. T: Begg’s test P=0.055, Egger’s test P=0.013; GG vs. TT: Begg’s test P=0.037, Egger’s test P=0.080; GG/TG vs. TT: Begg’s test P=0.051, Egger’s test P=0.016; GG vs. TT/TG: Begg’s test P=0.055, Egger’s test P=0.174; Figure 3).

For APE1 rs1760944 T>G polymorphism, Begg’s funnel plot analysis for publication bias (allele comparing model)

Figure 3
For APE1 rs1760944 T>G polymorphism, Begg’s funnel plot analysis for publication bias (allele comparing model)
Figure 3
For APE1 rs1760944 T>G polymorphism, Begg’s funnel plot analysis for publication bias (allele comparing model)

Since bias was found, we used non-parametric ‘trim-and-fill’ method to evaluate the stability of results. When we treated the publication bias, the adjusted ORs and CIs were not significantly changed (Figure 4).

For APE1 rs1760944 T>G polymorphism, filled funnel plot of meta-analysis (allele comparing model)

Figure 4
For APE1 rs1760944 T>G polymorphism, filled funnel plot of meta-analysis (allele comparing model)
Figure 4
For APE1 rs1760944 T>G polymorphism, filled funnel plot of meta-analysis (allele comparing model)

Sensitivity analysis

In this meta-analysis, sensitivity analysis was conducted by one-way method, which deleted an individual case–control study one by one and re-calculated the pooled ORs and CIs. No single case–control study significantly influenced the final decision (Figure 5).

Sensitivity analysis of the influence in G vs. T genetic model (random-effects estimates)

Figure 5
Sensitivity analysis of the influence in G vs. T genetic model (random-effects estimates)
Figure 5
Sensitivity analysis of the influence in G vs. T genetic model (random-effects estimates)

Heterogeneity

We found significant heterogeneity in all genetic models. Considering the potential factors for heterogeneity, subgroup analysis was conducted to identify its major source. In this meta-analysis, Asians, cervical cancer and population-based studies contribute to the major sources of heterogeneity.

Discussion

The APE1 rs1760944 T>G has been frequently investigated due to its potential role in the development of cancer; however, the results are conflicting. To shed light on this issue, we performed an extensive meta-analysis. The results highlighted that APE1 rs1760944 T>G polymorphism decreased the risk of cancer. Results of subgroup analyses demonstrated that this SNP still significantly modified the risk among lung cancer, breast cancer, osteosarcoma patients and Asians.

Rs1760944 T>G is a promoter SNP in the APE1 gene and may affect the binding of transcription factors. Since 2007, a number of case–control studies were performed to assess the potential relationship of APE1 rs1760944 T>G polymorphism with the risk of cancer, but the observations were controversial. Several investigations suggested that APE1 rs1760944 T>G SNP decreased the susceptibility of cancer [23,24,31,32,37,38,40,42,44,46]. However, other case–control studies suggest null correlation between the APE1 rs1760944 T>G SNP and cancer risk [25,33–36,39,41,43,45,47]. How can we obtain an extensive evaluation of the relationship between APE1 rs1760944 T>G locus and the risk of cancer with the consistent conclusions? To our knowledge, small sample size investigation could lead to confusing findings. Thus, we carried out a meta-analysis with 23 independent case–control studies to explore the correlation between APE1 rs1760944 T>G SNP and the susceptibility of cancer. In the included case–control studies, χ2 test was used to calculate the pooled ORs and CIs. In meta-analysis, we also used χ2 test to evaluate the relationship of APE1 rs1760944 T>G polymorphism with cancer risk. Overall, we found that APE1 rs1760944 T>G polymorphism decreased the risk of cancer in four genetic models. When we conducted subgroup analyses, we found that APE1 rs1760944 T>G polymorphism decreased the risk of lung cancer, breast cancer, osteosarcoma and Asians. To the best of our knowledge, the association might be confounded by some potential bias (e.g. publication bias, heterogeneity and lack of accordance with HWE in controls). Thus, we subsequently performed subgroup analyses. The findings suggested that the APE1 rs1760944 T>G polymorphism might be a protective effect on the development of cancer in Asians only, but not Caucasians. In the present study, one case–control study was incongruent with HWE [37]. When we deleted it and re-calculated the pooled ORs and CIs, the significant relationship was not changed. In the present study, we conducted non-parametric ‘trim-and-fill’ method to explore the potential influence of publication bias. We found that the bias of publication might not alter the findings. We also found that APE1 rs1760944 T>G polymorphism still significantly decreased the risk of some type of cancers.

It was found that inhibition of APE1 activity might reduce cell growth of ovarian cancer [20] and pancreatic cancer [21]. In addition, Luo et al. [48] identified that a decreased APE1 activity could also significantly retard the proliferation of endothelial cells, suggesting its stimulative effect on the development of cancer. Several studies indicated that APE1 rs1760944 G allele decreased APE1 mRNA and protein expression levels [23,24]. Additionally, Lu et al. [24] reported that APE1 rs1760944 G allele was associated with a decreased level of APE1 mRNA by reducing the binding affinity of some transcription factors. Although the pathway of the relationship between APE1 rs1760944 T>G and cancer risk has been not confirmed, it is speculated that this SNP may alter the susceptibility of cancer through the mechanism mentioned above. All observations and speculations should be verified with new molecular studies.

Some limitations of the current analysis should be noted. First, in this meta-analysis, only published literature was eligible and included, and some presumable unpublished studies might be neglected and discarded. Second, heterogeneity and publication bias were apparent, which could distort the pooled results. Our findings should be interpreted with cautions. Third, for lack of sufficient data (e.g., smoking, drinking, age, sex and vegetable and fruit intake and other environmental factors), we only conducted a crude assessment. Finally, only APE1 rs1760944 T>G polymorphism was included to assess the association with the risk of cancer; other functional loci in APE1 gene should not been ignored.

In summary, this updated meta-analysis highlights that the APE1 rs1760944 T>G polymorphism may play a protective role in the development of cancer. Further studies in different race are needed to confirm or refute our findings.

Acknowledgments

We wish to thank Dr. Yafeng Wang (Department of Cardiology, The People’s Hospital of Xishuangbanna Dai Autonomous Prefecture, Jinghong, Yunnan Province, China) for technical support.

Author Contribution

Conceived and designed the experiments: S.C. Performed the experiments: G.D., Y.C. and H.P. Analyzed the data: W.T. and H.Q. Contributed reagents/materials/analysis tools: S.C. Wrote the manuscript: G.D., Y.C. and W.T.

Competing Interests

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

Funding

This work was supported in part by the Young and Middle-aged Talent Training Project of Health Development Planning Commission in Fujian Province [grant number 2016-ZQN-25]; the Program for New Century Excellent Talents in Fujian Province University [grant number NCETFJ-2017B015]; the Joint Funds for the Innovation of Science and Technology, Fujian province [grant numbers 2017Y9099, 2017Y9077]; the National Natural Science Foundation of China [grant number U1705282]; the Ministry of Health, P.R. China [grant number WKJ2016-2-05]; the Natural Science Foundation of Fujian Province [grant numbers 2016J01513, 2017J01259, 2018J01267]; and the Fujian Provincial Health and Family Planning Research Talent Training Program [grant numbers 2015-CX-7, 2018-ZQN-13, 2016-1-11, 2018-1-13].

Abbreviations

     
  • APE1

    apurinic/apyrimidinic endonuclease 1

  •  
  • CI

    confidence interval

  •  
  • HWE

    Hardy–Weinberg equilibrium

  •  
  • OR

    odds ratio

  •  
  • SNP

    single-nucleotide polymorphism

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Author notes

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These authors contributed equally to this work.

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