Skip to content

Advertisement

  • Research article
  • Open Access
  • Open Peer Review

Association of APEX1 and OGG1 gene polymorphisms with breast cancer risk among Han women in the Gansu Province of China

BMC Medical Genetics201819:67

https://doi.org/10.1186/s12881-018-0578-9

  • Received: 5 October 2017
  • Accepted: 18 April 2018
  • Published:
Open Peer Review reports

Abstract

Background

Genetic variations in key DNA repair genes may influence DNA repair capacity, DNA damage and breast carcinogenesis. The current study aimed to estimate the association of APEX1 and OGG1 polymorphisms with the risk of breast cancer development.

Methods

A total of 518 patients with histopathologically confirmed breast cancer and 921 region- and age-matched cancer-free controls were genotyped for the APEX1 polymorphisms rs3136817 and rs1130409 and the OGG1 polymorphisms rs1052133 and rs2072668 using a QuantStudio™ 12 K Flex Real-Time PCR System.

Results

The rs3136817 heterozygous TC genotype along with the rs3136817 dominant model (TC + CC) was strongly associated with breast cancer susceptibility (odds ratio [OR] = 0.670, 95% confidence interval [95% CI]: 0.513 - 0.873, P = 0.003; OR = 0.682, 95% CI: 0.526 - 0.883, P = 0.004, respectively). No significant associations were observed among rs1130409, rs1052133, rs2072668 and breast cancer risk. Furthermore, an allele combination analysis revealed that APEX1 haplotypes containing C-T (alleles rs3136817 and rs1130409) conferred a significantly lower risk (corrected P < 0.001).

Conclusion

This research is the latest report showing that an APEX1 rs3136817 heterozygous genotype may have a positive influence on DNA repair capacity in patients with breast cancer and thus may have a potential protective effect for Chinese Han women.

Keywords

  • APEX1
  • OGG1
  • Polymorphisms
  • Haplotype
  • Breast cancer

Background

A recent epidemiological report from the National Central Cancer Registry (NCCR) of China showed that breast cancer is the most commonly diagnosed cancer among women in China. For female breast cancer, the 5-year survival rate (73%) in China is considerably reduced compared with corresponding estimates in Australia (89%) and the United States (90%) and to a lesser degree in Europe (82%) [1]. Breast cancer ranks as the sixth leading cause of cancer deaths among women in rural areas of China, while the incidence rate (37.86/100,000) is continually increasing [2]. Numerous breast cancer susceptibility genes have been identified by molecular epidemiological studies of cancer (e.g., BRCA1/2, TP53, PTEN and ATM). BRCA1 and BRCA2 mutations comprise 32 to 82% of hereditary breast cancer cases [3]. The prevalence of these mutations, however, is considerably reduced in the Chinese population. For example, BRCA1 and BRCA2 mutations contribute to only 10% of hereditary breast cancer cases [4, 5]. Thus, identification of other novel susceptibility loci among Chinese breast cancer patients is needed. Analyses of genetic polymorphisms reveal correlations between breast cancer susceptibility and many loci [6, 7]. Recently, a considerable number of novel polymorphic variants have been identified by genome-wide association studies (GWAS) for breast cancer susceptibility [8, 9].

DNA repair systems act to safeguard the integrity of the genome against damage caused by exogenous and endogenous carcinogens and by mutagens. Defects in DNA repair capability can increase the risk of carcinogenesis [10], including breast cancer. Patients with breast cancer exhibit significantly reduced DNA repair proficiency [11]. The high accumulation of DNA damage may cause the initiation of carcinogenesis and aberrant cell division [12]. Additionally, several base excision repair (BER) enzymes may also participate in the regulation of other biological processes, including BER, cell cycle progression [13], transcription initiation [14] and apoptosis [15]. Moreover, 8-oxoguanine DNA glycosylase 1 (OGG1) and apurinic/apyrimidinic endonuclease 1 (APEX1) are key enzymes in the BER pathway of oxidative DNA damage, excising abasic residues. OGG1 is a glycosidase that hydrolyzes the bonds between damaged bases and the sugar-phosphate backbone in DNA, creating an abasic site, while the endonuclease APEX1 cleaves the 5′ end of the abasic site. Mutations in these genes are expected to lead to a mutation-prone phenotype and contribute to tumor formation [16]. In recent years, studies of breast cancer and DNA repair have emphasized the relationship between different single nucleotide polymorphisms (SNPs) in DNA repair-related genes and the probability of developing carcinoma. Kim et al. [17] reported that the combined effect of APEX1 Asp148Glu was associated with an increased risk of breast cancer in a Korean population. Likewise, Roberts et al. [18] drew the conclusion that SNPs in nucleotide excision repair (NER) genes and BER genes affect the risk of developing breast cancer. Work by Smith et al. [19] demonstrated only a slight positive association between individual DNA repair genotypes and breast cancer risk; however, the combined effects of multiple polymorphisms in DNA repair pathways may be more noteworthy.

The present work aimed to investigate whether APEX1 and OGG1 gene polymorphisms (frequency ≥ 5%) exert any synergistic effects on breast cancer risk. Using HapMap data, we selected four putative functional tag SNPs in APEX1 (rs1130409 and rs3136817) and OGG1 (rs1052133 and rs2072668) and further evaluated the genetic interactions of these four polymorphisms and their relation to breast cancer risk among the study population of Han women living in the Gansu area in Northwest China.

Methods

Study participants and data collection

A total of 518 histopathologically confirmed patients with breast cancer were enrolled in this study from the Gansu Provincial Cancer Hospital (Gansu Province, China) between December 2014 and August 2017. None of these patients had been previously treated with systemic therapy. For the age- and region-matched controls, 921 cancer-free women were randomly selected from the Health Examination Surveys conducted in the same hospital. Inclusion criteria included negative history of all forms of cancer and no family history of breast cancer. All related data were drawn from questionnaires (see Additional file 1) and the patients’ medical charts. Informed consent was obtained from all individual participants, and this research was approved by the ethical committee of the Gansu Provincial Academic Institute for Medical Research and Gansu Provincial Cancer Hospital (IRB number: A201412040036).

SNP identification and selection

Details of the SNP identification and selection have been described previously [20]. Briefly, tag SNPs can be used to identify most genetic variations existing in a gene and develop markers to assess the relationship between a disease and a specific region, regardless of whether the tag SNPs have functional effects [21]. Employing selection for tagger pairing processes, tag SNPs were selected from the HapMap CHB database (HapMap Data Rel 24/phase II Nov08). Tag SNPs were chosen by using Haploview version 4.0 software according to the following selection criteria: minor allele frequency (MAF) ≥ 0.05 and pair-wise r2 ≥ 0.8. Finally, the APEX1 polymorphisms rs1130409 and rs3136817 and the OGG1 polymorphisms rs1052133 and rs2072668 were selected.

Genotyping method

A total of 5 mL of peripheral blood was collected in tubes containing EDTA from each of the participants for genomic DNA extraction using the genomic DNA Extraction Kit VER.3.0 according to the manufacturer’s instructions (TaKaRa Biotechnology, China). The DNA samples had A260/A230 ratios from 1.9 to 2.0 and A260/A280 ratios from 1.7 to 1.9; the concentrations of the samples were adjusted to 50 ng/μl. DNase/RNase-free distilled water was used in each assay as a non-template control (NTC). The multiplex TaqMan assays for genotyping were performed using the QuantStudio™ 12 K Flex Real-Time PCR System (Applied Biosystems, CA, USA) according to the manufacturer’s recommended operating conditions. The genotype data were analyzed using the OpenArray® SNP Genotyping Analysis software (version 1.0.5) (Applied Biosystems, CA, USA). To evaluate quality control, all genotyping assays were blind to the subject status. In addition, the reproducibility of genotyping was evaluated by direct sequencing in 20% of the samples randomly selected from each SNP and reached 100%.

Statistical analysis

To assess the deviation of the genotype frequencies of the four tag SNPs from Hardy-Weinberg equilibrium (HWE) in the control subjects, the chi-square goodness-of-fit test was used. The distribution of the APEX1 and OGG1 genotypes were compared between controls and patients using Pearson’s chi-square test (goodness-of-fit) or Fisher’s exact test. For the association tests, multivariate unconditional logistic regression adjusting for age was performed to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the associations of the APEX1 and OGG1 polymorphisms with breast cancer risk and clinicopathologic features. As reported previously, classical genetic models, including dominant models and recessive models, were used to analyze the associations [22]. The frequency distributions of the APEX1 and OGG1 haplotypes were estimated using SHEsis online software (http://analysis.bio-x.cn/myanalysis.php) [23, 24].

To identify potential gene-gene interactions, we employed the multifactor dimensionality reduction (MDR) method (http://www.epistasis.org) to evaluate the best cross-group model for further confirmation. MDR is a robust and novel data-mining approach that was applied as described previously [25, 26]. Briefly, this method converts two or more discrete genetic variables to a single attribute. The aim of this approach is to identify the overall best combination all loci. With MDR, the newly formed one-dimensional variable can be assessed to predict disease status using cross-validation and permutation testing corrections by repeating the entire analysis on 1000 datasets. Furthermore, a final best MDR model is chosen that simultaneously has the maximal testing accuracy and cross-validation consistency (CVC).

To avoid false-positive results, haplotypes with frequencies greater than 3% were considered in all of the research participants. Bonferroni correction was employed to make the adjustment for multiple comparisons. All statistical analyses were performed using SPSS software (version 16.0), and differences were considered significant at P < 0.05.

Results

Demographic and pathological characteristics

All the detailed clinical and pathological characteristics of the study participants are presented in Table 1. The average age of the breast cancer patients was 49.3 (standard deviation [SD] = 8.2), and the average age of the controls was 48.4 years (SD = 8.9). No significant differences were observed between the two groups with respect to mean age and age distribution (P > 0.064). The distribution of all four SNPs in the control subjects obeyed the predicted Hardy-Weinberg equilibrium values (P > 0.05).
Table 1

Characteristics in breast cancer cases

Characteristics

Cases (%)

LN involvement

 Positive

179 (34.56)

 Negative

237 (45.75)

 Unknown

102 (19.69)

ER

 Positive

330 (63.71)

 Negative

170 (32.82)

 Unknown

18 (3.47)

PR

 Positive

266 (51.35)

 Negative

230 (44.40)

 Unknown

22 (4.25)

P53

 Positive

199 (38.42)

 Negative

267 (51.54)

 Unknown

52 (10.04)

Her-2

 Positive

117 (22.59)

 Negative

379 (73.17)

 Unknown

22 (4.25)

Ki-67

 Positive

298 (57.53)

 Negative

172 (33.20)

 Unknown

48 (9.27)

Age of onset

  ≤ 35

46 (8.88)

  > 35

472 (91.12)

Menopausal

 Premenopause

255 (49.23)

 Postmenopause

163 (31.47)

 Unknown

100 (19.31)

LN lymph node, ER estrogen receptor, PR progesterone receptor, Her-2 human epidermal growth factor receptor 2, Ki-67 monoclonal antibody Ki-67

Association between genotypes and breast cancer risk

The genotypic and allelic distributions of all four SNPs are summarized in Table 2. The ancestral alleles were regarded as the reference group. In APEX1 rs3136817, we found that the heterozygous TC genotype and the C allele were associated with breast cancer risk (adjusted OR = 0.670, 95% CI: 0.513 - 0.873, P = 0.003; adjusted OR = 0.729, 95% CI: 0.576 - 0.923, P = 0.009, respectively). Moreover, a decreased breast cancer risk was conferred by the combined rs3136817 genotypes (TC + CC) in the dominant model (adjusted OR = 0.682, 95% CI: 0.526 - 0.883, P = 0.004). However, no significant differences were found for overall genotype frequencies of rs1130409 in APEX1 and the other two SNPs (rs2072668 and rs1052133) in OGG1.
Table 2

Genotype frequencies of APEX1 and OGG1 gene polymorphisms in controls and cases and their associations with breast cancer

SNPs

Genotype

Case N (%)

Control N (%)

OR (95% CI)a

P value

APEX1

 rs1130409

GG

91(17.57)

150(16.29)

Reference

 

GT

242(46.72)

417(45.28)

0.955(0.703-1.298)

0.770

TT

185(35.71)

354(38.44)

0.864(0.629-1.187)

0.368

G

424(40.93)

717(38.93)

Reference

 

T

612(59.07)

1125(61.07)

0.922(0.789-1.078)

0.309

Dominantb

  

0.914(0.685-1.218)

0.538

Recessivec

  

0.894(0.714-1.119)

0.328

 rs3136817

TT

412(79.54)

668(72.53)

Reference

 

TC

99(19.11)

240(26.06)

0.670(0.513-0.873)

0.003

CC

7(1.35)

13(1.41)

0.915(0.359-2.329)

0.851

T

923(89.09)

1576(85.56)

Reference

 

C

113(10.91)

266(14.44)

0.729(0.576-0.923)

0.009

Dominant

  

0.682(0.526-0.883)

0.004

Recessive

  

1.002(0.394-2.547)

0.996

OGG1

 rs2072668

CC

83(16.02)

156(16.94)

Reference

 

CG

253(48.84)

440(47.77)

1.072(0.786-1.461)

0.661

GG

182(35.14)

325(35.29)

1.056(0.763-1.459)

0.744

C

419(40.44)

752(40.83)

Reference

 

G

617(59.56)

1090(59.17)

1.019(0.872-1.191)

0.811

Dominant

  

1.065(0.795-1.427)

0.673

Recessive

  

1.002(0.799-1.258)

0.984

 rs1052133

CC

87(16.80)

154(16.72)

Reference

 

CG

243(46.91)

431(46.80)

0.986(0.725-1.342))

0.928

GG

188(36.29)

336(36.48)

0.985(0.715-1.356)

0.925

C

417(40.25)

739(40.12)

Reference

 

G

619(59.75)

1103(59.88)

0.993(0.850-1.161)

0.934

Dominant

  

0.985(0.738-1.317)

0.921

Recessive

  

0.995(0.795-1.246)

0.966

OR odds ratio, CI confidence interval;

aORs were adjusted for age;

bThe dominant model: comparing the combination of heterozygotes and minor allele homozygotes with the major allele homozygotes;

cThe recessive model: comparing minor allele homozygotes with the combination of heterozygotes and major allele homozygotes

Association between genotypes and clinicopathological features

We investigated the association between the genotypes of each SNP and the clinicopathological features of the breast cancer patients, including progesterone receptor (PR), estrogen receptor (ER), P53 protein, Ki67 protein, human epidermal growth factor receptor 2 (Her-2), staging and lymph node metastasis. Only statistically significant results are presented for rs1130409 and rs2072668 in Table 3. In order to facilitate to compare data of subtype study, related data of rs3136817 which associated with the risk of breast cancer were also added in Table 3. For the APEX1 polymorphism rs1130409, the frequency of the GT and TT genotypes was increased in ER-positive patients compared with the GG genotype (adjusted OR = 1.709, 95% CI: 1.028 - 2.842, P = 0.039; adjusted OR = 1.725, 95% CI: 1.016 - 2.930, P = 0.043, respectively), and rs1130409 was also associated with ER-positive patients in the dominant model (adjusted OR = 1.716, 95% CI: 1.068-2.759, P = 0.026). Furthermore, compared with the GG genotype, the TT homozygous genotype frequency was decreased in Her-2-positive patients (adjusted OR = 0.508, 95% CI: 0.281 - 0.917, P = 0.025). For the OGG1 polymorphism rs2072668, compared with the CC genotype, the homozygous GG genotype showed a reduced frequency in Ki-67-positive patients (adjusted OR = 0.536, 95% CI: 0.297 - 0.968, P = 0.039).
Table 3

Clinicopathological features and APEX1 and OGG1 gene polymorphisms

Clinical features

SNPs

Genotype

N (%)

OR (95% CI)a

P value

Positive

Negative

APEX1

 ER

rs1130409

GG

48(14.55)

38(22.35)

Reference

 

GT

158(47.88)

74(43.53)

1.709(1.028-2.842)

0.039

TT

124(37.58)

58(34.12)

1.725(1.016-2.930)

0.043

Dominantb

  

1.716(1.068-2.759)

0.026

Recessivec

  

1.174(0.796-1.732)

0.417

rs3136817

TT

267(80.91)

130(76.47)

Reference

 

TC

59(17.88)

37(21.76)

0.779(0.491-1.236)

0.288

CC

4(1.21)

3(1.76)

0.673(0.148-3.063)

0.608

Dominant

  

0.771(0.492-1.207)

0.255

Recessive

  

0.708(0.156-3.213)

0.655

 Her-2

rs1130409

GG

27(23.08)

59(15.57)

Reference

 

GT

56(47.86)

176(46.44)

0.689(0.399-1.190)

0.182

TT

34(29.06)

144(37.99)

0.508(0.281-0.917)

0.025

Dominant

  

0.608(0.364-1.015)

0.057

Recessive

  

0.663(0.423-1.040)

0.074

rs3136817

TT

95(81.20)

298(78.63)

Reference

 

TC

21(17.95)

75(19.79)

0.875(0.512-1.497)

0.627

CC

1(0.85)

6(1.58)

0.505(0.060-4.265)

0.531

Dominant

  

0.848(0.501-1.433)

0.537

Recessive

  

0.519(0.062-4.365)

0.546

 Ki-67

rs3136817

TT

239(80.20)

134(77.91)

Reference

 

TC

54(18.12)

36(20.93)

0.842(0.525-1.350)

0.475

CC

5(1.68)

2(1.16)

1.451(0.277-7.610)

0.660

Dominant

  

0.873(0.552-1.383)

0.564

Recessive

  

1.502(0.287-7.854)

0.630

OGG1

 Ki-67

rs2072668

CC

55(18.46)

21(12.21)

Reference

 

CG

146(48.99)

82(47.67)

0.675(0.381-1.195)

0.178

GG

97(32.55)

69(40.12)

0.536(0.297-0.968)

0.039

Dominant

  

0.612(0.355-1.052)

0.076

Recessive

  

0.913(0.620-1.344)

0.644

ER estrogen receptor, Her-2 human epidermal growth factor receptor 2, Ki-67 monoclonal antibody Ki-67, OR odds ratio, CI confidence interval;

aORs were adjusted for age;

bThe dominant model: comparing the combination of heterozygotes and minor allele homozygotes with the major allele homozygotes;

cThe recessive model: comparing minor allele homozygotes with the combination of heterozygotes and major allele homozygotes

Haplotype analyses

We further investigated haplotype frequencies in the patients and controls. Only haplotypes with a frequency greater than 5% are presented in Table 4. In this combination, haplotype CT in APEX1 had a lower frequency in patients compared with controls. After applying the Bonferroni multiple adjustment, only one significant P value was observed at the APEX1 CT haplotype (P < 0.001), indicating a significant difference. In addition, no statistically significant differences between controls and patients were observed in the frequencies of other APEX1 and OGG1 haplotypes.
Table 4

Frequency distributions of haplotypes of APEX1 and OGG1 in cases and controls

Haplotype

Case (freq%)

Control (freq%)

P valuec

OR (95% CI)

Pc

APEX1a

 CG

94.19 (9.10%)

179.03 (9.70%)

0.581

0.929 (0.715-1.207)

NS

 CT

18.81 (1.80%)

86.97 (4.70%)

7.10e-05

0.373 (0.225-0.618)

< 0.001

 TG

329.81 (31.80%)

537.97 (29.2%)

0.140

1.132 (0.960-1.335)

NS

 GG

593.19 (57.30%)

1038.03 (56.40%)

0.639

1.038 (0.890-1.210)

NS

OGG1b

 CC

403.81 (39.00%)

728.87 (39.60%)

0.908

0.991 (0.847-1.159)

NS

 CG

15.19 (1.50%)

23.13 (1.30%)

NS

 GC

13.19 (1.30%)

10.13 (0.50%)

NS

 GG

603.81 (58.3%)

1079.87 (58.6%)

0.908

1.009 (0.862-1.181)

NS

Pc corrected P value (after Bonferroni multiple adjustment), OR odds ratio, CI confidence interval, NS not significant

aThe order of SNPs in APEX1 is rs3136817 and rs1130409

bThe order of SNPs in OGG1 is rs2072668 and rs1052133

cP value calculated by Fisher’s exact test

Interaction analysis

We further adopted the analytical method of MDR data mining to examine the potential interactions among the four polymorphisms within the APEX1 and OGG1 genes. Table 5 shows the best interaction models identified by the MDR analysis, with testing balance accuracy (TBA) and cross-validation consistency. APEX1 rs3136817, as the best one-locus model, had the maximum testing-balanced accuracy (0.5323) and maximum consistency (100%) among the four SNPs. A four-locus model (rs2072668, rs1052133, rs3136817 and rs1130409) also exhibited maximum consistency (100%), but its TBA (0.5029) was lower than that of the one-locus model. Moreover, the combination of rs3136817 and rs2072668 formed the best two-locus model, with a high TBA (0.5291) and CVC (70%), among all combinations of two SNPs. The combination of rs3136817, rs2072668 and rs1130409 produced the best three-locus model, with a high TBA (0.5044) and CVC (90%), among all pair-wise combination of three SNPs. However, no significant association was observed for these interaction models out of 1000 permutations.
Table 5

MDR interaction analysis between SNP-SNP

Each overall best model

Testing Balance Accuracy

CVCa

P Valueb

One-locus: rs3136817

0.5323

10/10

0.1419

Two-locus: One-locus plus rs2072668

0.5291

7/10

0.3846

Three-locus: Two-locus plus rs1130409

0.5044

9/10

0.7383

Four-locus: Three-locus plus rs1052133

0.5029

10/10

0.7502

aCVC cross-validation consistency

bP-values as calculated after 1000 permutations

Discussion

Genetic polymorphisms in key genes involved in DNA repair may influence DNA damage response, carcinogenesis and DNA repair capacity. Polymorphic variants have been confirmed to be good candidates for assessing cancer risk. We estimated the relationship between APEX1 and OGG1 gene polymorphisms and breast cancer risk among 1430 Han women of Northwest China in this experiment using a tag SNP-based study. The key finding was that the APEX1 polymorphism rs3136817 might mediate synergistic and independent effects on breast carcinogenesis. We found that the rs3136817 heterozygous TC genotype and the combined genotype (TC + CC) were associated with decreased breast cancer risk.

DNA excision repair capacity is known to play a crucial role in carcinogenesis [27]. If changes that occur in the DNA sequence due to copying errors are not corrected, they may ultimately interfere with cell function. Damaged or inappropriate bases can be repaired by several mechanisms. The BER pathway is considered the primary mechanism involved in protecting against gene mutations and repairing DNA damage, and APEX1 and OGG1 are key components of this pathway. The BER proteins encoded by these genes act in a highly coordinated manner at the site of DNA damage. For example, repair of 8-oxoguanine, an oxidized base, is initiated by the OGG1 glycosylase, which recognizes and removes damaged bases, forming an apurinic site that is cleaved by APEX1. The resulting single-strand break can be subsequently repolymerized by DNA ligase 3 and polymerase β [28]. According to our current results, patients carrying the rs3136817 TC genotype had a reduced breast cancer risk, implying that heterozygosity at rs3136817 may have a positive influence on DNA repair capacity in DNA damage responsive pathways and thus potentially prevent breast cancer. Similar results were observed in bladder cancer and lung cancer. Nevertheless, this protection was not observed in individuals carrying other SNPs. Similar researches were only reported in bladder cancer and lung cancer. Zhu et al. [29] reported that APEX1 rs3136817 TC genotype was associated with a decreased risk of bladder cancer. However, Li et al. [30] demonstrated that no association between APEX1 rs3136817 and the risk of radiation-induced pneumonitis grade ≥ 3. Our study is the first to demonstrate that the rs3136817 heterozygous genotype was associated with a decreased risk of breast cancer. Of all the SNPs assessed, the rs1130409 and rs1052133 polymorphisms showed the most consistent relationship to breast cancer in two previous reports, demonstrating that these variations have no significant impact on breast cancer risk [18, 31]. However, a few reports which were different from our results also indicated that breast cancer risk was significantly associated with APEX1 rs1130409 in North Indian [32], Korean [17] and Caucasian [19] women. To the best of our knowledge, the rs3136817 polymorphic locus has not been previously evaluated regarding its association with cancer, except for bladder cancer and lung cancer. Moreover, the possibility that the associations noted above might have occurred by chance cannot be excluded. Further evidence in different regional populations and larger sample sizes, in addition to functional studies, are required to reinforce these results.

We further found that the APEX1 rs1130409 GT and TT genotypes were increased in ER-positive patients by analyzing the association of clinicopathological features with the four SNPs, suggesting that these genotype carriers exhibited adverse clinicopathological features of breast cancer and failed to benefit from endocrine therapy. In addition, a lower frequency of women carrying the TT genotype was Her-2 positive. Her-2 is a ligand-less member of the human epidermal growth factor receptor family. Approximately 15% of patients with breast cancer exhibit Her-2 over-expression, which is associated with invasive behavior, unresponsiveness to common endocrine therapies, poor prognosis and reduced survival [33, 34]. For OGG1, the rs2072668 polymorphism was also associated with the pathologic characteristics of patients. The rs2072668 homozygous GG genotype carriers had a reduced frequency of Ki-67-positive expression. The Ki-67 protein is a nuclear marker of cell proliferation that is expressed at high levels in breast cancer patients, and increased expression is associated with worse outcomes [35, 36]. Recent studies have indicated that long-term outcomes may be predicted by changes in Ki-67 expression after endocrine treatment [37]. Therefore, our current results indicate that the APEX1 rs1130409 TT genotype and the OGG1 rs2072668 GG genotype deficiencies may lead to poorer prognosis and reduced survival.

In our study, the associations between breast cancer risk and haplotypes were also assessed. The APEX1 haplotype containing C-T (alleles rs3136817 and rs1130409) was observed at a higher frequency in the controls. Consequently, we infer that the APEX1 CT haplotype may be involved in decreasing the risk of breast cancer. MDR analysis has been used to examine the interactions of multiple genes in common diseases as a promising data-mining approach because it easily overcomes some of the limitations and inadequacies of traditional statistics, such as logistic regression, to characterize and examine gene-gene and gene-environment interactions. Meanwhile, a four-locus interaction associated with cumulative breast cancer risk was identified by MDR analysis. However, the four-locus best interaction model did not exhibit significant improvements compared with the other models in this case. The main effect model of rs3136817 performed best, which implies that the APEX1 rs3136817 polymorphism is a powerful risk factor and may play an important role in the interaction with other SNPs in affecting breast cancer development, either synergistically or antagonistically. Nevertheless, no statistical significance was observed, and evidence is therefore needed to support the hypothesis of SNP-SNP interactions in the future.

Conclusions

In summary, our data provide clear evidence that the rs3136817 polymorphism in APEX1 and a corresponding haplotype may be involved in breast cancer risk in the Han women of Northwest China. Notably, the rs3136817 heterozygous variant exhibited enhancement in the major DNA repair pathway, and this capacity may prevent the early development of breast cancer. As far as we know, this study is the latest to report that the APEX1 rs3136817 genotype is associated with cancer risk. The findings further suggest that the combined effect of SNPs determines the individual women’s risk for breast cancer. A better understanding of the mechanism of carcinogenesis will facilitate improved, individualized pharmaceutical therapy for patients with breast cancer and the implementation of breast cancer prevention strategies.

Abbreviations

APEX1: 

Apurinic/apyrimidinic endonuclease 1

BER: 

Base excision repair

CIs: 

Confidence intervals

CVC: 

Cross-validation consistency

ER: 

Estrogen receptor

GWAS: 

Genome-wide association studies

Her-2: 

Human epidermal growth factor receptor 2

HWE: 

Hardy-Weinberg equilibrium

MDR: 

Multifactor dimensionality reduction

NCCR: 

National Central Cancer Registry

NER: 

Nucleotide excision repair

NTC: 

Non-template control

OGG1: 

8-oxoguanine DNA glycosylase 1

ORs: 

Odds ratios

PR: 

Progesterone receptor

SD: 

Standard deviation

SNPs: 

Single nucleotide polymorphisms

TBA: 

Testing balance accuracy

Declarations

Acknowledgments

We wish to thank all the patients and healthy volunteers for providing blood samples. We also thank the nurses, clinical researchers and regulatory coordinators at the Department of Breast Surgery and Research Center of Translational Medicine at Gansu Provincial Cancer Hospital for their collaboration.

Funding

This study was supported by the 11th batch Science and Technology Support Project of Gansu Province of China (Grant No. 1011FKCA089) and Gansu province science and technology project (Grant No. 17JR3TA013).

Availability of data and materials

The clinical raw datasets used and/or analyzed during the current study will be available from corresponding author due to some patient confidentiality.

Authors’ contributions

The conception and design of study: TW and HS. Performed the experiments: TW, HW, HuG, LW, GZ and YZ. Performed breast ultrasound and X-ray examination, patient’s recruitment and collected the clinical data: SY, HoG and BZ. Extracted genomic DNA and helped with the analysis of questionnaire data: XZ and HL. Designed primers and performed DNA sequencing: HZ. Performed the statistical analyses: TW and HZ. Drafting the article or revising it critically for the intellectual content: TW, HW and HS. All authors read and approved the final manuscript.

Ethics approval and consent to participate

All related data were drawn from questionnaires and the patients’ medical charts. Informed consent was obtained from all individual participants, and this research was approved by the Ethical Committee of the Gansu Provincial Academic Institute for Medical Research and Gansu Provincial Cancer Hospital, IRB number A201412040036 (approved on December 04, 2014).

Competing interest

The authors declare that they have no competing interests.

Publisher’s Note

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

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Research Center of Translational Medicine, Gansu Provincial Academic Institute for Medical Research, NO. 2 Xiaoxihu East Street, Lanzhou, Gansu, 730050, People’s Republic of China
(2)
Department of Breast Surgery, Gansu Provincial Cancer Hospital, Lanzhou, Gansu, 730050, People’s Republic of China

References

  1. Zeng H, Zheng R, Guo Y, Zhang S, Zou X, Wang N, et al. Cancer survival in China, 2003-2005: a population-based study. Int J Cancer. 2015;136(8):1921–30. https://doi.org/10.1002/ijc.29227.View ArticlePubMedGoogle Scholar
  2. Chen W, Zheng R, Zeng H, Zhang S, He J. Annual report on status of cancer in China, 2011. Chin J Cancer Res. 2015;27(1):2–12. https://doi.org/10.3978/j.issn.1000-9604.2015.01.06.View ArticlePubMedPubMed CentralGoogle Scholar
  3. King MC, Marks JH, Mandell JB, New York Breast Cancer Study Group. Breast and ovarian cancer risks due to inherited mutations in BRCA1 and BRCA2. Science. 2003;302(5645):643–6. https://doi.org/10.1126/science.1088759.View ArticlePubMedGoogle Scholar
  4. Kang PC, Phuah SY, Sivanandan K, Kang IN, Thirthagiri E, Liu JJ, et al. Recurrent mutation testing of BRCA1 and BRCA2 in Asian breast cancer patients identify carriers in those with presumed low risk by family history. Breast Cancer Res Treat. 2014;144(3):635–42. https://doi.org/10.1007/s10549-014-2894-x.View ArticlePubMedGoogle Scholar
  5. Liede A, Narod SA. Hereditary breast and ovarian cancer in Asia: genetic epidemiology of BRCA1 and BRCA2. Hum Mutat. 2002;20(6):413–24. https://doi.org/10.1002/humu.10154.View ArticlePubMedGoogle Scholar
  6. Menashe I, Maeder D, Garcia-Closas M, Figueroa JD, Bhattacharjee S, Rotunno M, et al. Pathway analysis of breast cancer genome-wide association study highlights three pathways and one canonical signaling cascade. Cancer Res. 2010;70(11):4453–9. https://doi.org/10.1158/0008-5472.CAN-09-4502.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Pharoah PD, Antoniou A, Bobrow M, Zimmern RL, Easton DF, Ponder BA. Polygenic susceptibility to breast cancer and implications for prevention. Nat Genet. 2002;31(1):33–6. https://doi.org/10.1038/ng853.View ArticlePubMedGoogle Scholar
  8. Long J, Cai Q, Sung H, Shi J, Zhang B, Choi JY, et al. Genome-wide association study in east Asians identifies novel susceptibility loci for breast cancer. PLoS Genet. 2012;8(2):e1002532. https://doi.org/10.1371/journal.pgen.1002532.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Li J, Humphreys K, Heikkinen T, Aittomaki K, Blomqvist C, Pharoah PD, et al. A combined analysis of genome-wide association studies in breast cancer. Breast Cancer Res Treat. 2011;126(3):717–27. https://doi.org/10.1007/s10549-010-1172-9.View ArticlePubMedGoogle Scholar
  10. Torgovnick A, Schumacher B. DNA repair mechanisms in cancer development and therapy. Front Genet. 2015;6:157. https://doi.org/10.3389/fgene.2015.00157.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Matta J, Echenique M, Negron E, Morales L, Vargas W, Gaetan FS, et al. The association of DNA Repair with breast cancer risk in women. A comparative observational study. BMC Cancer. 2012;12:490. https://doi.org/10.1186/1471-2407-12-490.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Yuan SS, Hou MF, Hsieh YC, Huang CY, Lee YC, Chen YJ, et al. Role of MRE11 in cell proliferation, tumor invasion, and DNA repair in breast cancer. J Natl Cancer Inst. 2012;104(19):1485–502. https://doi.org/10.1093/jnci/djs355.View ArticlePubMedGoogle Scholar
  13. Chaudhry MA. Base excision repair of ionizing radiation-induced DNA damage in G1 and G2 cell cycle phases. Cancer Cell Int. 2007;7:15. https://doi.org/10.1186/1475-2867-7-15.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Jung HJ, Kim HL, Kim YJ, Weon JI, Seo YR. A novel chemopreventive mechanism of selenomethionine: enhancement of APE1 enzyme activity via a Gadd45a, PCNA and APE1 protein complex that regulates p53-mediated base excision repair. Oncol Rep. 2013;30(4):1581–6. https://doi.org/10.3892/or.2013.2613.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Horton JK, Stefanick DF, Prasad R, Gassman NR, Kedar PS, Wilson SH. Base excision repair defects invoke hypersensitivity to PARP inhibition. Mol Cancer Res. 2014;12(8):1128–39. https://doi.org/10.1158/1541-7786.MCR-13-0502.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Karahalil B, Bohr VA, Wilson DM 3rd. Impact of DNA polymorphisms in key DNA base excision repair proteins on cancer risk. Hum Exp Toxicol. 2012;31(10):981–1005. https://doi.org/10.1177/0960327112444476.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Kim KY, Han W, Noh DY, Kang D, Kwack K. Impact of genetic polymorphisms in base excision repair genes on the risk of breast cancer in a Korean population. Gene. 2013;532(2):192–6. https://doi.org/10.1016/j.gene.2013.09.069.View ArticlePubMedGoogle Scholar
  18. Roberts MR, Shields PG, Ambrosone CB, Nie J, Marian C, Krishnan SS, et al. Single-nucleotide polymorphisms in DNA repair genes and association with breast cancer risk in the web study. Carcinogenesis. 2011;32(8):1223–30. https://doi.org/10.1093/carcin/bgr096.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Smith TR, Levine EA, Freimanis RI, Akman SA, Allen GO, Hoang KN, et al. Polygenic model of DNA repair genetic polymorphisms in human breast cancer risk. Carcinogenesis. 2008;29(11):2132–8. https://doi.org/10.1093/carcin/bgn193.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Wang T, Wang H, Guo H, Yang S, Zhu G, Guo H, et al. Polymorphisms in the DNA repair gene ERCC2/XPD and breast cancer risk: a HapMap-based case-control study among Han Women in a Chinese less-developed area. Genet Test Mol Biomarkers. 2014;18(10):703–10. https://doi.org/10.1089/gtmb.2014.0028.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumenstiel B, et al. The structure of haplotype blocks in the human genome. Science. 2002;296(5576):2225–9. https://doi.org/10.1126/science.1069424.View ArticlePubMedGoogle Scholar
  22. Xu F, Li D, Zhang Q, Fu Z, Yuan W, Pang D, et al. Association of CD27 and CD70 gene polymorphisms with risk of sporadic breast cancer in Chinese women in Heilongjiang Province. Breast Cancer Res Treat. 2012;133(3):1105–13. https://doi.org/10.1007/s10549-012-1987-7.View ArticlePubMedGoogle Scholar
  23. Li Z, Zhang Z, He Z, Tang W, Li T, Zeng Z, et al. A partition-ligation-combination-subdivision EM algorithm for haplotype inference with multiallelic markers: update of the SHEsis (http://analysis.bio-x.cn). Cell Res. 2009;19(4):519–23. https://doi.org/10.1038/cr.2009.33.View ArticlePubMedGoogle Scholar
  24. Shi YY, He L. SHEsis, a powerful software platform for analyses of linkage disequilibrium, haplotype construction, and genetic association at polymorphism loci. Cell Res. 2005;15(2):97–8. https://doi.org/10.1038/sj.cr.7290272.View ArticlePubMedGoogle Scholar
  25. Namkung J, Kim K, Yi S, Chung W, Kwon MS, Park T. New evaluation measures for multifactor dimensionality reduction classifiers in gene-gene interaction analysis. Bioinformatics. 2009;25(3):338–45. https://doi.org/10.1093/bioinformatics/btn629.View ArticlePubMedGoogle Scholar
  26. Hahn LW, Ritchie MD, Moore JH. Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics. 2003;19(3):376–82.View ArticlePubMedGoogle Scholar
  27. Deng L, Kimmel M, Foy M, Spitz M, Wei Q, Gorlova O. Estimation of the effects of smoking and DNA repair capacity on coefficients of a carcinogenesis model for lung cancer. Int J Cancer. 2009;124(9):2152–8. https://doi.org/10.1002/ijc.24149.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Sangrajrang S, Schmezer P, Burkholder I, Waas P, Boffetta P, Brennan P, et al. Polymorphisms in three base excision repair genes and breast cancer risk in Thai women. Breast Cancer Res Treat. 2008;111(2):279–88. https://doi.org/10.1007/s10549-007-9773-7.View ArticlePubMedGoogle Scholar
  29. Zhu G, Su H, Lu L, Guo H, Chen Z, Sun Z, et al. Association of nineteen polymorphisms from seven DNA repair genes and the risk for bladder cancer in gansu province of China. Oncotarget. 2016; https://doi.org/10.18632/oncotarget.9146.
  30. Li H, Liu G, Xia L, Zhou Q, Xiong J, Xian J, et al. A polymorphism in the DNA repair domain of APEX1 is associated with the radiation-induced pneumonitis risk among lung cancer patients after radiotherapy. Br J Radiol. 2014;87(1040):20140093. https://doi.org/10.1259/bjr.20140093.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Chang-Claude J, Ambrosone CB, Lilla C, Kropp S, Helmbold I, von Fournier D, et al. Genetic polymorphisms in DNA repair and damage response genes and late normal tissue complications of radiotherapy for breast cancer. Br J Cancer. 2009;100(10):1680–6. https://doi.org/10.1038/sj.bjc.6605036.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Mitra AK, Singh N, Singh A, Garg VK, Agarwal A, Sharma M, Chaturvedi R, Rath SK. Association of polymorphisms in base excision repair genes with the risk of breast cancer: a case-control study in North Indian women. Oncol Res. 2008;17(3):127–35.View ArticlePubMedGoogle Scholar
  33. Wolff AC, Hammond ME, Hicks DG, Dowsett M, McShane LM, Allison KH, et al. Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update. J Clin Oncol. 2013;31(31):3997–4013. https://doi.org/10.1200/JCO.2013.50.9984.View ArticlePubMedGoogle Scholar
  34. Cheang MC, Chia SK, Voduc D, Gao D, Leung S, Snider J, et al. Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J Natl Cancer Inst. 2009;101(10):736–50. https://doi.org/10.1093/jnci/djp082.View ArticlePubMedPubMed CentralGoogle Scholar
  35. de Azambuja E, Cardoso F, de Castro G Jr, Colozza M, Mano MS, Durbecq V, et al. Ki-67 as prognostic marker in early breast cancer: a meta-analysis of published studies involving 12,155 patients. Br J Cancer. 2007;96(10):1504–13. https://doi.org/10.1038/sj.bjc.6603756.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Trihia H, Murray S, Price K, Gelber RD, Golouh R, Goldhirsch A, et al. Ki-67 expression in breast carcinoma: its association with grading systems, clinical parameters, and other prognostic factors--a surrogate marker? Cancer. 2003;97(5):1321–31. https://doi.org/10.1002/cncr.11188.View ArticlePubMedGoogle Scholar
  37. Dowsett M, Smith IE, Ebbs SR, Dixon JM, Skene A, A'Hern R, et al. Prognostic value of Ki67 expression after short-term presurgical endocrine therapy for primary breast cancer. J Natl Cancer Inst. 2007;99(2):167–70. https://doi.org/10.1093/jnci/djk020.View ArticlePubMedGoogle Scholar

Copyright

Advertisement