Abstract
Background
X‐ray repair cross‐complementary 5 (XRCC5) and 6 (XRCC6) are critical for DNA repair. Few studies have assessed their association with breast cancer risk, and related gene‐environment interactions remain poorly understood. This study aimed to determine the influence of XRCC5/6 polymorphisms on breast cancer risk, and their interactions with cigarette smoking, alcohol consumption, and sleep satisfaction.
Methods
The study included 1039 patients with breast cancer and 1040 controls. Four single‐nucleotide polymorphisms of XRCC5 and two of XRCC6 were genotyped. Information about smoking, alcohol consumption, and sleep satisfaction was collected through questionnaires. Odds ratios (OR) and related 95% confidence intervals (95% CI) were assessed using unconditional logistic regression models. Gene‐environment interactions were analyzed using logistic regression with multiplicative interaction models.
Results
XRCC5 rs16855458 was associated with increased breast cancer risk in the co‐dominant (p trend = 0.003) and dominant (CA + AA vs. CC, OR = 1.29, 95% CI = 1.07–1.56, p = 0.008) genetic models after Bonferroni correction. The CG + GG genotype of XRCC6 rs2267437 was associated with an increased risk of estrogen receptor‐negative/progesterone receptor‐negative (ER−/PR−) breast cancer (CG + GG vs. CC: OR = 1.54, 95% CI = 1.12–2.13, p = 0.008) after Bonferroni correction. Moreover, an antagonistic interaction between XRCC5 rs16855458 and alcohol consumption (p interaction = 0.017), and a synergistic interaction between XRCC6 rs2267437 and sleep satisfaction were associated with breast cancer risk (p interaction = 0.0497). However, these interactions became insignificant after Bonferroni correction.
Conclusion
XRCC5 rs16855458 was associated with breast cancer risk, and XRCC6 rs2267437 was associated with the risk of ER−/PR− breast cancer. Breast cancer risk associated with XRCC5 and XRCC6 polymorphisms might vary according to alcohol consumption and sleep satisfaction, respectively, and merit further investigation.
Keywords: alcohol consumption, breast cancer, gene‐environment interaction, sleep satisfaction, smoking, XRCC5, XRCC6
XRCC5 rs16855458 was associated with breast cancer risk, and XRCC6 rs2267437 was associated with the risk of ER−/PR− breast cancer. Breast cancer risk associated with XRCC5 and XRCC6 polymorphisms might vary according to alcohol consumption and sleep satisfaction, respectively, and merit further investigation.
1. INTRODUCTION
The identification of risk factors and susceptible populations of breast cancer has become imperative in China given the recent marked increase of breast cancer cases among Chinese women. 1 , 2 Epidemiological studies identified cigarette smoking, alcohol consumption, and sleep disorders among the several environmental risk factors for breast cancer. 3 , 4 , 5 Nevertheless, only a small proportion of exposed people develop breast cancer, suggesting that breast cancer incidence is not only correlated with environmental factors, but also with genetic susceptibility. Gene‐environment interactions can be deterministic for breast cancer development in individuals who were exposed to environmental risk factors. However, the underlying genetic mechanisms of breast carcinogenesis, especially gene‐environment interactions, remain poorly understood.
Cells under the influence of endogenous and exogenous factors in vivo sustain a large number of DNA‐damaging events daily. 6 X‐ray repair cross‐complementing 5 (XRCC5) and 6 (XRCC6) are key genes of the non‐homologous end joining (NHEJ) repair pathway, which is an important DNA repair mechanism. XRCC5/6 polymorphisms correlate with susceptibility to develop various malignant tumors. 7 , 8 XRCC5 rs16855458 and rs9288516 have potential biological functions as they cause changes in the binding sites of transcription factors, and correlate with risk for hepatocellular carcinoma, 9 , 10 whereas XRCC6 rs2267437 and XRCC5 rs3835 were correlated with breast cancer risk in European women. 11 However, relatively few studies have investigated XRCC5/6 polymorphisms and breast cancer susceptibility in Chinese women, and available results have been inconsistent. For example, a study conducted in Taiwan found that XRCC5 rs3835 was not linked to breast cancer susceptibility, 12 contrasting with the findings of a previous European study. 11 These inconsistencies might be due to the specific genetic landscape of different populations. Moreover, gene‐environment interactions in the occurrence of breast cancer have been largely ignored. Smoking, alcohol consumption, and sleep disorders are considered important environmental carcinogenic factors, and DNA damage caused by these factors is an important underlying mechanism. 13 , 14 , 15 DNA repair pathways are critical to maintain DNA stability and prevent long‐lasting DNA damage caused by environmental exposure. 16 Therefore, polymorphisms on DNA repair genes might interact with these environmental factors during carcinogenesis. Several studies reported interactions between polymorphisms of DNA repair genes such as XRCC1 and smoking in the etiology of breast cancer. 17 However, few studies investigated potential interactions between XRCC5/6 polymorphisms and carcinogenesis‐related environmental factors, especially in Chinese mainland populations. Relevant investigations will contribute to breast cancer risk assessment and the development of intervention strategies.
The present study aimed to clarify the effects of XRCC5/6 polymorphisms on breast cancer susceptibility among Chinese women and their potential interactions with smoking, alcohol consumption, and sleep satisfaction.
2. MATERIALS AND METHODS
2.1. Participants
The study participants have been previously described. 18 Briefly, participants in the case group had newly diagnosed, histologically confirmed breast cancer and were recruited at 21 hospitals in Northern and Eastern China between April 2012 and April 2013. The control group comprised age‐matched (±3 years) volunteers recruited at the same hospital who were examined within 2 months of the case group and were confirmed as being breast cancer free by negative physical and imaging findings. Participants with other malignant tumors were excluded from the study. The ethics committee of the Second Hospital, Cheeloo College of Medicine, Shandong University had approved this study, and all participants signed informed consent.
2.2. Data collection
Demographic information and lifestyle habits of the participants were obtained using a structured questionnaire as described previously. 19 Smokers were defined as those who continuously or cumulatively smoked cigarettes for at least 6 months. Drinkers were defined as those who consumed alcohol at least once a month for at least 6 months. Sleep satisfaction was determined as one of the four responses to the question, “In the most recent 1‐year period, have you been satisfied with your sleep?” The choices were, “very satisfied,” “satisfied,” “dissatisfied,” and “very dissatisfied.” The estrogen receptor (ER) and progesterone receptor (PR) status were determined using immunohistochemical staining and obtained from the medical records of the patients. According to the American Society of Clinical Oncology/College of American Pathologists (2010) guideline recommendations, ER and PR positivity was defined as ≥1% of tumor cells with positive staining. 20
2.3. Genotyping
Fasting blood samples were collected into EDTA Vacutainers (Becton Dickinson and Co.) and stored at −80°C after sedimentation. DNA was extracted using the Wizard Genomic DNA Purification Kit (A1120, Promega). Single‐nucleotide polymorphisms (SNPs) were selected based on their reported association with cancer risk, and with a minor allelic frequency (MAF) >5% according to the 1000 Genomes project (https://www.ncbi.nlm.nih.gov/snp/). rs3835, rs828907, rs16855458, and rs9288516 of XRCC5, 9 , 11 , 21 , 22 and rs2267437 and rs5751131 of XRCC6 9 , 11 were selected for further analysis (Table 1). All participants were genotyped using the Sequenom MassARRAY SNP system (CapitalBio Technology), as previously described. 18 Double‐distilled water was used as a negative control. Tests were repeated on 5% of randomly selected samples as quality control, and 100% consistency was achieved.
TABLE 1.
Distribution of target SNPs of XRCC5/6
| Gene | SNP IDs | Chromosome | Location in gene region | Base change | MAF |
|---|---|---|---|---|---|
| XRCC5 | rs3835 | 2 | Intron variant | G > A | 0.206 |
| XRCC5 | rs828907 | 2 | 2 KB upstream variant | G > T | 0.326 |
| XRCC5 | rs16855458 | 2 | Intron variant | C > A | 0.149 |
| XRCC5 | rs9288516 | 2 | Intron variant | T > A | 0.152 |
| XRCC6 | rs2267437 | 22 | 2 KB upstream variant | C > G | 0.260 |
| XRCC6 | rs5751131 | 22 | Intron variant | A > G | 0.107 |
Abbreviations: MAF, minor allele frequency; SNP, single‐nucleotide polymorphism.
2.4. Statistical analysis
Differences in demographic and lifestyle data between the case and control groups were assessed by χ 2 tests. A population representative was detected using Hardy–Weinberg equilibrium (HWE) in the control group. Associations between co‐dominant (wild‐type homozygous vs. heterozygous vs. mutant homozygous) and dominant (wild‐type homozygous vs. heterozygous and mutant homozygous) models of genetic variants and breast cancer were assessed using unconditional logistic regression. Odds ratios (OR) with 95% confidence interval (95% CI) were estimated after adjustment for age, body mass index (BMI), menstrual status, and family history of breast cancer. Trends were tested by considering the categorical genotypes as continuous variables in co‐dominant inheritance profiles. Gene‐environment interactions were assessed using logistic regression including multiplicative interaction models. Sleep satisfaction was defined as “dissatisfied” versus “satisfied,” and the dominant model with the homozygous common genotypes was adopted as the reference to facilitate the interaction analysis. The Bonferroni correction was employed for multiple testing and the significant p value was set at 0.0083 (0.05/6) for testing the six loci. All data were statistically analyzed using SPSS 23.0 (IBM Corp.).
3. RESULTS
3.1. Demographic and lifestyle characteristics of the participants
Table 2 lists the demographic and lifestyle characteristics of the 1039 cases and 1040 controls. Menstrual status, BMI, family history of breast cancer, smoking, and sleep satisfaction considerably differed between the groups, whereas age and alcohol consumption did not.
TABLE 2.
Demographic and lifestyle characteristics of the participants
| Variable a | Control, n (%) | Case, n (%) | χ 2 | p b |
|---|---|---|---|---|
| Age, y | ||||
| 25–44 | 440 (42.3) | 392 (37.7) | 5.75 | 0.057 |
| 45–59 | 523 (50.3) | 550 (52.9) | ||
| 60–70 | 77 (7.4) | 97 (9.3) | ||
| BMI | 10.00 | 0.007 | ||
| <24 | 515 (51.0) | 481 (48.1) | ||
| 24–27.9 | 407 (40.3) | 388 (38.8) | ||
| ≥28 | 88 (8.7) | 131 (13.1) | ||
| Menstrual status | 9.00 | 0.003 | ||
| Premenopausal | 724 (72.5) | 667 (66.4) | ||
| Postmenopausal | 274 (27.5) | 338 (33.6) | ||
| Family history of breast cancer | 4.13 | 0.042 | ||
| No | 995 (97.4) | 983 (95.7) | ||
| Yes | 27 (2.6) | 44 (4.3) | ||
| Smoking | ||||
| No | 1016 (98.1) | 1000 (96.6) | 4.22 | 0.040 |
| Yes | 20 (1.9) | 35 (3.4) | ||
| Alcohol consumption | 2.50 | 0.114 | ||
| No | 935 (90.3) | 910 (88.1) | ||
| Yes | 101 (9.7) | 123 (11.9) | ||
| Sleep satisfaction | 8.08 | 0.004 | ||
| Satisfied | 874 (85.9) | 829 (81.3) | ||
| Dissatisfied | 143 (14.1) | 191 (18.7) | ||
Abbreviations: BMI, body mass index.
The data were presented in the form of classified variables.
p value was calculated by the χ 2 test, and the p < 0.05 was statistically significant.
3.2. Genotype distribution of XRCC5/6 and breast cancer risk
Table 3 shows the effects of XRCC5/6 SNP genotypes and their adjusted effects on breast cancer risk. All SNPs followed the Hardy–Weinberg equilibrium (p > 0.05). Logistic regression analyses showed that XRCC5 rs16855458 was associated with increased breast cancer risk in the co‐dominant genetic model (p trend = 0.003), and the dominant genetic model (CA + AA vs. CC: OR = 1.29, 95% CI = 1.07–1.56, p = 0.008) after the Bonferroni correction. The other five SNPs were not significantly associated with breast cancer risk.
TABLE 3.
Genotype distribution of XRCC5/6 and breast cancer risk
| Genotype | Control, n (%) | Case, n (%) | OR (95%CI) a | p | HWE b |
|---|---|---|---|---|---|
| XRCC5 rs3835 | 0.173 | ||||
| GG | 855 (84.7) | 866 (85.6) | 1(ref) | ||
| GA | 151 (15.0) | 140 (13.8) | 0.93 (0.72–1.21) | 0.587 | |
| AA | 3 (0.3) | 6 (0.6) | 3.06 (0.61–15.27) | 0.173 | |
| GA + AA | 154 (15.3) | 146 (14.4) | 0.96 (0.74–1.24) | 0.756 | |
| p trend c | 0.965 | ||||
| XRCC5 rs828907 | 0.056 | ||||
| GG | 636 (62.9) | 636 (62.9) | 1(ref) | ||
| GT | 344 (34.0) | 328 (32.4) | 0.92 (0.76–1.12) | 0.416 | |
| TT | 31 (3.1) | 47 (4.6) | 1.41 (0.87–2.29) | 0.159 | |
| GT + TT | 375 (37.1) | 375 (37.1) | 0.96 (0.80–1.17) | 0.708 | |
| p trend c | 0.833 | ||||
| XRCC5 rs16855458 | 0.287 | ||||
| CC | 652 (64.4) | 585 (57.8) | 1(ref) | ||
| CA | 328 (32.4) | 372 (36.8) | 1.24 (1.02–1.51) | 0.031 | |
| AA | 33 (3.3) | 55 (5.4) | 1.78 (1.12–2.82) | 0.014 | |
| CA + AA | 361 (35.6) | 427 (42.2) | 1.29 (1.07–1.56) | 0.008 | |
| p trend c | 0.003 | ||||
| XRCC5 rs9288516 | 0.488 | ||||
| TT | 276 (27.2) | 314 (30.9) | 1 (ref) | ||
| TA | 516 (50.9) | 485 (47.8) | 0.82 (0.66–1.02) | 0.072 | |
| AA | 221 (21.8) | 216 (21.3) | 0.87 (0.67–1.12) | 0.280 | |
| TA + AA | 737 (72.8) | 701 (69.1) | 0.84 (0.68–1.02) | 0.080 | |
| p trend c | 0.222 | ||||
| XRCC6 rs2267437 | 0.247 | ||||
| CC | 628 (61.9) | 623 (61.8) | 1 (ref) | ||
| CG | 332 (32.7) | 329 (32.6) | 1.03 (0.84–1.25) | 0.803 | |
| GG | 54 (5.3) | 56 (5.6) | 0.95 (0.64–1.43) | 0.818 | |
| CG + GG | 386 (38.1) | 385 (38.2) | 1.02 (0.84–1.22) | 0.879 | |
| p trend c | 0.987 | ||||
| XRCC6 rs5751131 | 0.251 | ||||
| AA | 341 (33.9) | 328 (33.1) | 1 (ref) | ||
| AG | 474 (47.1) | 490 (49.4) | 1.17 (0.95–1.44) | 0.134 | |
| GG | 191 (19.0) | 173 (17.5) | 0.97 (0.74–1.26) | 0.797 | |
| AG + GG | 665 (66.1) | 663 (66.9) | 1.11 (0.92–1.35) | 0.288 | |
| p trend c | 0.878 |
Bold values indicate p < 0.05.
Abbreviations: CI, Confidence Interval; HWE, Hardy‐Weinberg equilibrium; OR, Odds Ratio; ref, reference.
Adjusted for age, BMI, menstrual status, and family history of breast cancer.
p value for Hardy‐Weinberg equilibrium.
p trend of the co–dominant inheritance patterns.
3.3. Associations between XRCC5/6 polymorphisms and risk of ER+/PR+, ER−/PR− breast cancer
Among the 1039 cases, 918 (88.4%) cases had explicit joint ER and PR statuses. Overall, 614 (59.1%) cases were ER+/PR+, 212 (20.4%) were ER−/PR−, 77 (7.4%) were ER+/PR−, and 15 (1.4%) cases were ER−/PR+. Due to the limited number of ER+/PR− and ER−/PR+ cases, they were excluded from further analysis. Table 4 shows the associations between the SNPs genotypes of XRCC5/6 and the risk of ER+/PR+, ER−/PR− breast cancer. The CG + GG genotype of XRCC6 rs2267437 was associated with an increased risk of ER−/PR− breast cancer (CG + GG vs. CC: OR = 1.54, 95% CI = 1.12–2.13, p = 0.008) after Bonferroni correction. Increased ER+/PR+ breast cancer risk in the co‐dominant genetic model of XRCC5 rs16855458 after Bonferroni correction was observed (p trend = 0.008). The AA genotype of XRCC5 rs16855458 was associated with an increased risk of both ER+/PR+ and ER−/PR− breast cancer (AA vs. CC: for ER+/PR+, OR = 1.84, 95% CI = 1.10–3.07, p = 0.019; for ER−/PR−, OR = 2.25, 95% CI = 1.14–4.46, p = 0.019), but not after Bonferroni correction. The other SNPs were not significantly associated with ER+/PR+ or ER−/PR− breast cancer risk.
TABLE 4.
Associations between XRCC5/6 polymorphisms and risk of ER+/PR+, ER−/PR− breast cancer
| Genotype | Control, n (%) | ER+/PR+ | ER−/PR− | ||||
|---|---|---|---|---|---|---|---|
| Case, n (%) | OR (95%CI) a | p | Case, n (%) | OR (95%CI) a | p | ||
| XRCC5 rs3835 | |||||||
| GG | 855 (84.7) | 520 (86.5) | 1 (ref) | 171 (83.0) | 1 (ref) | ||
| GA | 151 (15.0) | 77 (12.8) | 0.89 (0.65–1.20) | 0.434 | 34 (16.5) | 1.17 (0.77–1.80) | 0.463 |
| AA | 3 (0.3) | 4 (0.7) | 3.46 (0.62–19.2) | 0.156 | 1 (0.5) | 2.98 (0.27–33.42) | 0.375 |
| GA + AA | 154 (15.3) | 81 (13.5) | 0.92 (0.68–1.24) | 0.589 | 35 (17.0) | 1.20 (0.79–1.82) | 0.404 |
| p trend b | 0.796 | 0.350 | |||||
| XRCC5 rs828907 | |||||||
| GG | 636 (62.9) | 375 (62.3) | 1 (ref) | 129 (62.3) | 1 (ref) | ||
| GT | 344 (34.0) | 196 (32.6) | 0.92 (0.73–1.15) | 0.457 | 74 (35.7) | 1.02 (0.73–1.42) | 0.903 |
| TT | 31 (3.1) | 31 (5.1) | 1.52 (0.89–2.60) | 0.124 | 4 (1.9) | 0.49 (0.15–1.65) | 0.250 |
| GT + TT | 375 (37.1) | 227 (37.7) | 0.97 (0.78–1.21) | 0.783 | 78 (37.7) | 0.98 (0.70–1.35) | 0.880 |
| p trend b | 0.733 | 0.616 | |||||
| XRCC5 rs16855458 | |||||||
| CC | 652 (64.4) | 351 (58.1) | 1 (ref) | 125 (61.0) | 1 (ref) | ||
| CA | 328 (32.4) | 217 (35.9) | 1.23 (0.98–1.45) | 0.077 | 67 (32.7) | 1.08 (0.77–1.52) | 0.660 |
| AA | 33 (3.3) | 36 (6.0) | 1.84 (1.10–3.07) | 0.019 | 13 (6.3) | 2.25 (1.14–4.46) | 0.019 |
| CA + AA | 361 (35.6) | 253 (41.9) | 1.29 (1.04–1.60) | 0.023 | 89 (39.0) | 1.19 (0.86–1.65) | 0.294 |
| p trend b | 0.008 | 0.089 | |||||
| XRCC5 rs9288516 | |||||||
| TT | 276 (27.2) | 192 (31.7) | 1 (ref) | 63 (30.6) | 1 (ref) | ||
| TA | 516 (50.9) | 284 (46.9) | 0.79 (0.62–1.02) | 0.066 | 101 (49.0) | 0.81 (0.56–1.16) | 0.243 |
| AA | 221 (21.8) | 129 (21.3) | 0.85 (0.63–1.15) | 0.294 | 42 (20.4) | 0.79 (0.51–1.24) | 0.310 |
| TA + AA | 737 (72.8) | 413 (68.3) | 0.81 (0.64–1.02) | 0.076 | 143 (69.4) | 0.80 (0.57–1.13) | 0.204 |
| p trend b | 0.230 | 0.273 | |||||
| XRCC6 rs2267437 | |||||||
| CC | 628 (61.9) | 384 (63.8) | 1 (ref) | 107 (52.2) | 1 (ref) | ||
| CG | 332 (32.7) | 189 (31.4) | 0.95 (0.75–1.20) | 0.657 | 87 (42.4) | 1.66 (1.20–2.31) | 0.002 |
| GG | 54 (5.3) | 29 (4.8) | 0.87 (0.54–1.39) | 0.551 | 11 (5.4) | 0.89 (0.41–1.93) | 0.762 |
| CG + GG | 386 (38.1) | 218 (36.2) | 0.94 (0.75–1.17) | 0.556 | 98 (47.8) | 1.54 (1.12–2.13) | 0.008 |
| p trend b | 0.496 | 0.063 | |||||
| XRCC6 rs5751131 | |||||||
| AA | 341 (33.9) | 185 (31.6) | 1 (ref) | 73 (36.0) | 1 (ref) | ||
| AG | 474 (47.1) | 303 (51.7) | 1.24 (0.97–1.57) | 0.083 | 94 (46.3) | 1.07 (0.75–1.53) | 0.693 |
| GG | 191 (19.0) | 98 (16.7) | 0.96 (0.70–1.31) | 0.783 | 36 (17.7) | 0.90 (0.56–1.43) | 0.645 |
| AG + GG | 665 (66.1) | 401 (68.4) | 1.16 (0.92–1.45) | 0.214 | 130 (64.0) | 1.02 (0.73–1.43) | 0.900 |
| p trend b | 0.839 | 0.765 | |||||
Bold values indicate p < 0.05.
Abbreviations: CI, Confidence Interval; ER, estrogen receptor; OR, Odds Ratio; PR, progesterone receptor; ref, reference.
Adjusted for age, BMI, menstrual status, and family history of breast cancer.
p trend of the co‐dominant inheritance patterns.
3.4. Interactions between XRCC5/6 polymorphisms and smoking, alcohol consumption, and sleep satisfaction in breast cancer risk
The effects of potential interactions between target (XRCC5 and XRCC6) SNPs and smoking, alcohol consumption, and sleep satisfaction on breast cancer risk were analyzed. An antagonistic interaction was found between XRCC5 rs16855458 and alcohol consumption (p interaction = 0.017). Compared with nondrinkers carrying the rs16855458 CC genotype, risk for breast cancer was increased in nondrinkers harboring the CA + AA genotype (OR = 1.41, 95% CI = 1.16–1.72, p = 0.001) and drinkers with the CC genotype (OR = 1.63, 95% CI = 1.12–2.38, p = 0.011), but not in drinkers with CA + AA genotype (OR = 1.08, 95% CI = 0.66–1.75, p = 0.769). The XRCC6 rs2267437 genotype synergistically interacted with sleep satisfaction (p interaction = 0.0497). Compared with satisfied sleepers carrying the rs2267437 CC genotype, breast cancer risk increased in dissatisfied sleepers harboring the CG + GG genotype (OR = 1.72, 95% CI = 1.13–2.61, p = 0.011). However, these interactions became insignificant after the Bonferroni correction. The other SNPs evaluated did not show significant interactions with smoking, alcohol consumption, or sleep satisfaction (Table 5).
TABLE 5.
Interactions between XRCC5/6 polymorphisms and smoking, alcohol consumption, and sleep satisfaction in breast cancer risk
| Genotype | Exposure a | Smoking | Alcohol consumption | Sleep satisfaction | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Control, n (%) | Case, n (%) | OR (95%CI) b | p | Control, n (%) | Case, n (%) | OR (95%CI) b | p | Control, n (%) | Case, n (%) | OR (95%CI) b | p | ||
| XRCC5 rs3835 | |||||||||||||
| GG | (−) | 834 (83.0) | 833 (82.6) | 1 (ref) | 764 (76.0) | 764 (75.9) | 1 (ref) | 717 (72.5) | 694 (69.9) | 1 (ref) | |||
| GA + AA | (−) | 151 (15.0) | 141 (14.0) | 0.96 (0.74–1.24) | 0.743 | 145 (14.4) | 124 (12.3) | 0.89 (0.68–1.17) | 0.408 | 132 (13.3) | 113 (11.4) | 0.92 (0.69–1.22) | 0.550 |
| GG | (+) | 17 (1.7) | 29 (2.9) | 1.61 (0.87–2.97) | 0.131 | 87 (8.7) | 96 (9.5) | 1.12 (0.81–1.56) | 0.482 | 119 (12.0) | 154 (15.5) | 1.30 (0.98–1.71) | 0.068 |
| GA + AA | (+) | 3 (0.3) | 5 (0.5) | 1.64 (0.39–6.94) | 0.503 | 9 (0.9) | 22 (2.2) | 2.20 (0.98–4.91) | 0.055 | 21 (2.1) | 32 (3.2) | 1.50 (0.83–2.71) | 0.184 |
| p interaction c | 0.937 | 0.086 | 0.517 | ||||||||||
| XRCC5 rs828907 | |||||||||||||
| GG | (−) | 625 (62.1) | 610 (60.6) | 1 (ref) | 580 (57.6) | 563 (56.0) | 1 (ref) | 533 (53.9) | 521 (52.5) | 1 (ref) | |||
| GT + TT | (−) | 365 (36.2) | 362 (35.9) | 0.99 (0.81–1.20) | 0.894 | 326 (32.4) | 324 (32.2) | 1.01 (0.83–1.24) | 0.917 | 317 (32.1) | 283 (28.5) | 0.90 (0.73–1.11) | 0.338 |
| GG | (+) | 8 (0.8) | 23 (2.3) | 2.85 (1.26–6.45) | 0.012 | 52 (5.2) | 70 (7.0) | 1.47 (0.99–2.18) | 0.053 | 86 (8.7) | 104 (10.5) | 1.22 (0.88–1.70) | 0.230 |
| GT + TT | (+) | 9 (0.9) | 12 (1.2) | 1.18 (0.48–2.89) | 0.711 | 49 (4.9) | 49 (4.9) | 0.91 (0.58–1.43) | 0.690 | 52 (5.3) | 84 (8.5) | 1.46 (0.99–2.15) | 0.057 |
| p interaction c | 0.161 | 0.114 | 0.297 | ||||||||||
| XRCC5 rs16855458 | |||||||||||||
| CC | (−) | 640 (63.4) | 566 (56.2) | 1 (ref) | 593 (58.8) | 501 (49.8) | 1 (ref) | 541 (54.6) | 462 (46.5) | 1 (ref) | |||
| CA + AA | (−) | 351 (34.8) | 407 (40.4) | 1.29 (1.06–1.56) | 0.009 | 317 (31.4) | 387 (38.5) | 1.41 (1.16–1.72) | 0.001 | 308 (31.1) | 343 (34.5) | 1.27 (1.03–1.56) | 0.025 |
| CC | (+) | 11 (1.1) | 18 (1.8) | 1.69 (0.78–3.66) | 0.180 | 57 (5.6) | 81 (8.1) | 1.63 (1.12–2.38) | 0.011 | 96 (9.7) | 111 (11.2) | 1.26 (0.92–1.73) | 0.157 |
| CA + AA | (+) | 7 (0.7) | 17 (1.7) | 2.65 (1.09–6.46) | 0.032 | 42 (4.2) | 37 (3.7) | 1.08 (0.66–1.75) | 0.769 | 45 (4.5) | 77 (7.8) | 1.91 (1.26–2.89) | 0.002 |
| p interaction c | 0.747 | 0.017 | 0.507 | ||||||||||
| XRCC5 rs9288516 | |||||||||||||
| TT | (−) | 268 (26.6) | 299 (29.6) | 1 (ref) | 243 (24.1) | 273 (27.1) | 1 (ref) | 236 (23.8) | 249 (25.0) | 1 (ref) | |||
| TA + AA | (−) | 723 (71.7) | 677 (67.0) | 0.83 (0.68–1.02) | 0.080 | 665 (65.9) | 617 (61.1) | 0.80 (0.65–0.99) | 0.042 | 613 (61.9) | 559 (56.1) | 0.84 (0.67–1.04) | 0.115 |
| TT | (+) | 6 (0.6) | 13 (1.3) | 1.86 (0.69–5.00) | 0.217 | 33 (3.3) | 40 (4.0) | 0.92 (0.54–1.57) | 0.762 | 36 (3.6) | 60 (6.0) | 1.37 (0.84–2.23) | 0.204 |
| TA + AA | (+) | 12 (1.2) | 22 (2.2) | 1.52 (0.73–3.18) | 0.258 | 68 (6.7) | 79 (7.8) | 1.07 (0.54–1.57) | 0.729 | 105 (10.6) | 129 (12.9) | 1.10 (0.79–1.53) | 0.566 |
| p interaction | 0.981 | 0.254 | 0.885 | ||||||||||
| XRCC6 rs2267437 | |||||||||||||
| CC | (−) | 613 (60.7) | 599 (59.7) | 1 (ref) | 570 (56.4) | 541 (54.0) | 1 (ref) | 513 (51.8) | 499 (50.5) | 1 (ref) | |||
| CG + GG | (−) | 379 (37.5) | 371 (37.0) | 1.01 (0.83–1.22) | 0.900 | 341 (33.8) | 343 (34.2) | 1.06 (0.86–1.29) | 0.549 | 337 (34.0) | 302 (30.5) | 0.91 (0.74–1.12) | 0.392 |
| CC | (+) | 13 (1.3) | 21 (2.1) | 1.62 (0.80–3.28) | 0.179 | 56 (5.5) | 77 (7.7) | 1.40 (0.95–2.07) | 0.080 | 97 (9.8) | 116 (11.7) | 1.11 (0.81–1.52) | 0.537 |
| CG + GG | (+) | 5 (0.5) | 13 (1.3) | 2.41 (0.84–6.91) | 0.111 | 43 (4.3) | 41 (4.1) | 1.05 (0.66–1.66) | 0.844 | 44 (4.4) | 72 (7.3) | 1.72 (1.13–2.61) | 0.011 |
| p interaction c | 0.576 | 0.244 | 0.0497 | ||||||||||
| XRCC6 rs5751131 | |||||||||||||
| AA | (−) | 330 (32.9) | 316 (32.0) | 1 (ref) | 306 (30.5) | 288 (29.2) | 1 (ref) | 287 (29.1) | 261 (26.9) | 1 (ref) | |||
| AG + GG | (−) | 652 (65.1) | 639 (64.7) | 1.10 (0.90–1.34) | 0.361 | 599 (59.8) | 581 (58.9) | 1.11 (0.90–1.36) | 0.326 | 559 (56.7) | 528 (54.3) | 1.11 (0.90–1.38) | 0.333 |
| AA | (+) | 11 (1.1) | 12 (1.2) | 1.11 (0.48–2.56) | 0.814 | 34 (3.4) | 39 (4.0) | 1.25 (0.75–2.08) | 0.384 | 48 (4.9) | 62 (6.4) | 1.38 (0.89–2.13) | 0.153 |
| AG + GG | (+) | 9 (0.9) | 21 (2.1) | 2.44 (1.09–5.46) | 0.030 | 63 (6.3) | 78 (7.9) | 1.40 (0.94–2.07) | 0.097 | 92 (9.3) | 121 (12.4) | 1.47 (1.05–2.07) | 0.024 |
| p interaction c | 0.237 | 0.990 | 0.894 | ||||||||||
Bold values indicate p < 0.05.
Abbreviations: CI, confidence interval; OR, odds ratio.
Exposure for smoking: (−), no; (+), yes. Exposure for drinking: (−), no; (+), yes. Exposure for sleep satisfaction: (−), satisfied; (+), dissatisfied.
Adjusted for age, BMI, menstrual status, and family history of breast cancer.
p interaction was calculated by the test for multiplicative interaction.
4. DISCUSSION
This study aimed to determine the influence of XRCC5 and XRCC6 polymorphisms on breast cancer risk, and potential interactions with cigarette smoking, alcohol consumption, and sleep satisfaction. The data revealed that XRCC5 rs16855458 was associated with increased breast cancer risk in the co‐dominant genetic model, and the CG + GG genotype of XRCC6 rs2267437 was associated with an increased risk of ER−/PR− breast cancer, even after Bonferroni correction. Antagonistic interaction between XRCC5 rs16855458 and alcohol consumption, and synergistic interaction between XRCC6 rs2267437 and sleep satisfaction were also found to affect breast cancer risk. However, these interactions became insignificant after applying the Bonferroni correction.
NHEJ repair is a major mechanism responsible for mending mammalian DNA double‐strand breaks. 23 The Ku70 and Ku80 proteins, respectively, encoded by XRCC6 and XRCC5, form the Ku heterodimer that plays key roles in the NHEJ pathway; thereby regulating the DNA repair function. 24 Of note, abnormal Ku70/80 levels correlate with the development of various malignant tumors. 25 , 26 However, relatively few studies have investigated relationships between XRCC5 polymorphisms and tumor susceptibility. The present study showed that XRCC5 rs16855458 polymorphism correlates with breast cancer risk. The function of the rs16855458 polymorphism remains unclear; however, the FASTSNP software revealed that it has the potential to change binding sites of transcription factors. 9 , 10 Indeed, Li et al. 9 correlated the rs16855458 polymorphism with hepatocellular carcinoma risk, with individuals carrying the CA + AA genotype having lower risk than those with the CC genotype. In contrast, this study showed that individuals with the rs16855458 CA + AA genotype had a higher risk of breast cancer. Moreover, the rs16855458 CA + AA genotype identified by Li et al. 9 was protective only among males, whereas in the present study all participants were female. These inconsistencies suggest that the relationship between rs16855458 polymorphism and cancer risk susceptibility differs among various tumors, and/or might be associated with sex differences. Of note, the protective effect of this polymorphism on hepatocellular carcinoma was limited to patients with hepatitis B virus infection, 9 which relies on hepatitis B virus insertion during NHEJ for inducing hepatocellular carcinoma, differing from the pathogenesis mechanisms of breast cancer. 27
Environmental factors are important triggers of DNA damage. Therefore, polymorphisms in genes that repair DNA damage may interact with environmental factors for promoting breast cancer development. Herein, an antagonistic interaction was identified between the XRCC5 rs16855458 CA + AA genotype and alcohol consumption in breast cancer risk. This particular genotype was associated with a reduced risk for hepatocellular carcinoma. 9 Alcohol metabolites in the liver, such as acetaldehyde and reactive oxygen species, are mutagenic and carcinogenic, 28 , 29 and are correlated with an increased risk of breast cancer. Hence, it is possible that the rs16855458 CA + AA genotype reduces the impact of alcohol on breast cancer risk through liver protection. However, the identified rs16855458/alcohol consumption interaction became insignificant after applying the Bonferroni correction: Thus, the role and effect of this specific XRCC5 polymorphism warrant further investigation.
The XRCC6 rs2267437 is located upstream of the CACCC box in the Ku70 promoter region. 30 Changes in this sequence can interfere with transcription factor binding, thus affecting the Ku70 levels and the overall NHEJ repair process. 31 , 32 Several studies found an association between the rs2267437 polymorphism and breast cancer risk. Willems et al. 11 reported a higher risk for breast cancer among women carrying the rs2267437 CG than those with the CC genotype. He et al. 33 also reported similar findings with the rs2267437 CG or GG alleles in a Chinese female population. Herein, no statistically significant association between rs2267437 and the total risk of breast cancer was found. This inconsistency might be partly due to different genetic characteristics of the studied populations. To date, most studies exploring associations between rs2267437 polymorphisms and breast cancer risk included European populations, 11 , 34 and Chinese women have been evaluated in studies conducted mainly in Taiwan 12 and Central China, 33 covering relatively limited geographical areas and small samples. The present research was a multi‐center, large sample study encompassing East and North China, which offered a relatively better representation of the Chinese population. Another possible explanation for the observed data discrepancy could derive from complex gene‐environment interactions. Herein, a slightly synergistic interaction between the rs2267437 polymorphism and sleep satisfaction was reported to affect the occurrence of breast cancer before Bonferroni correction, supporting the importance of gene‐environment interaction. Sleep satisfaction is an important and necessary indicator of sleep quality and is considered to have an even more important effect than objective indicators on predicting sleep disorders and health. 35 , 36 , 37 Night shift work and light exposure are important factors influencing sleep satisfaction, 37 with working night shifts been reported to cause more DNA damage events, 14 while low light exposure can reduce the DNA damage repair function by affecting melatonin release. 38 Thus, sleep satisfaction could be associated with DNA damage events. Since XRCC6 rs2267437 was associated with dysfunctional DNA damage repair, 31 , 32 a potential synergistic effect between rs2267437 and poor sleep patterns can further increase the risk of breast cancer. Although the interaction effect was not significant after Bonferroni correction, it also suggested a strong possible interaction effect as Bonferroni correction was a conservative and stringent correction test. Studies in larger patient cohorts would contribute to elucidating the clinical relevance of this interaction.
Several studies showed that DNA repair defects are associated with hormone receptor‐negative breast cancers. For example, the breast cancer susceptibility gene 1 (BRCA1), a tumor suppressor involved in homologous recombination pathways of DNA repair, was associated with the risk of hormone receptor‐negative breast cancer. 39 , 40 Moreover, polymorphisms of XRCC4, another essential gene in the NHEJ pathway, were found to be associated with PR− breast cancer risk. 41 The present study revealed increased ER+/PR+ breast cancer risk in the co‐dominant genetic model of XRCC5 rs16855458. However, the AA genotype of XRCC5 rs16855458 was associated with an increased risk of both ER+/PR+ and ER−/PR− breast cancer. Therefore, the relationship between XRCC5 rs16855458 and breast cancer risk by different hormone receptor states needs to be further explored. Furthermore, the CG + GG genotype of XRCC6 rs2267437 was found to be associated with an increased risk of ER−/PR− breast cancer even after Bonferroni correction, but not with ER+/PR+ breast cancer. This finding demonstrates that this relationship might be genuine rather than a false positive. Polymorphisms of XRCC6 rs2267437 were related to Ku70 protein expression, which was an essential protein in the NHEJ pathway of DNA double‐strand break. 30 , 31 , 32 Studies had shown that the defects of DNA double‐strand break repair were associated with ER and PR negative breast cancer. 39 , 41 However, the specific mechanism between XRCC6 rs2267437 variation and ER+/PR+ breast cancer risk is still unclear and further investigation is required.
This study has several limitations. The hospital‐based, case‐control design might hold an inherent selection bias. Also, only a few SNPs were selected for analysis, in particular only four variants of XRCC5 and two of XRCC6, based on previous reports. Hence, some important XRCC5/6 SNPs might have been missed. The sample size was also limited; thus, whether the results can be extrapolated to a wider area needs to be explored. Due to the lack of complete human epidermal growth factor receptor 2 (HER2) status data, this study only analyzed the correlation between XRCC5/6 polymorphisms and the risk of breast cancer by different hormone receptor states, but not molecular subtypes. Nonetheless, the findings of this study are a good complement to the existing knowledge. In the future, better‐designed studies with larger samples, systematically selected SNPs, and molecular subtypes’ information are needed to further clarify the effects of gene‐environment interactions on the occurrence of breast cancer.
5. CONCLUSIONS
This study provides new evidence that XRCC5 rs16855458 is associated with breast cancer risk among Chinese women, and that XRCC6 rs2267437 is associated with the risk of ER−/PR− breast cancer. Moreover, potential interactions between XRCC5 rs16855458 and alcohol consumption, and between XRCC6 rs2267437 and sleep satisfaction were identified in relation to breast cancer risk. Despite that interactions became insignificant after conservative multiple‐comparison correction (Bonferroni correction), these results provide novel evidence for risk assessment and individual intervention for breast cancer in Chinese women.
CONFLICT OF INTEREST
All authors declare no conflict of interest.
ACKNOWLEDGMENTS
This study was funded by the Major Scientific and Technological Innovation Project of Shandong Province (2017CXGC1212), and the National Key Research and Development Program of China (2016YFC0901304, 2018YFC0114705). Thanks to all participants in this study.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
REFERENCES
- 1. Global Burden of Disease Cancer C , Fitzmaurice C, Abate D, Abbasi, N , et al. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability‐adjusted life‐years for 29 cancer groups, 1990 to 2017: a systematic analysis for the global burden of disease study. JAMA Oncol. 2019;5(12):1749–1768. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Heer E, Harper A, Escandor N, Sung H, McCormack V, Fidler‐Benaoudia MM. Global burden and trends in premenopausal and postmenopausal breast cancer: a population‐based study. Lancet Glob Health. 2020;8:e1027‐e1037. [DOI] [PubMed] [Google Scholar]
- 3. Chen WY, Rosner B, Hankinson SE, Colditz GA, Willett WC. Moderate alcohol consumption during adult life, drinking patterns, and breast cancer risk. JAMA. 2011;306:1884‐1890. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Macacu A, Autier P, Boniol M, Boyle P. Active and passive smoking and risk of breast cancer: a meta‐analysis. Breast Cancer Res Treat. 2015;154:213‐224. [DOI] [PubMed] [Google Scholar]
- 5. Yang W, Shi Y, Ke X, Sun H, Guo J, Wang X. Long‐term sleep habits and the risk of breast cancer among Chinese women: a case‐control study. Eur J Cancer Prev. 2019;28:323‐329. [DOI] [PubMed] [Google Scholar]
- 6. Hoeijmakers JH. Genome maintenance mechanisms for preventing cancer. Nature. 2001;411:366‐374. [DOI] [PubMed] [Google Scholar]
- 7. Liu Y, Zhang H, Zhou K, et al. Tagging SNPs in non‐homologous end‐joining pathway genes and risk of glioma. Carcinogenesis. 2007;28:1906‐1913. [DOI] [PubMed] [Google Scholar]
- 8. Henríquez‐Hernández LA, Valenciano A, Foro‐Arnalot P, et al. Association between single‐nucleotide polymorphisms in DNA double‐strand break repair genes and prostate cancer aggressiveness in the Spanish population. Prostate Cancer Prostatic Dis. 2016;19:28‐34. [DOI] [PubMed] [Google Scholar]
- 9. Li R, Yang Y, An YU, et al. Genetic polymorphisms in DNA double‐strand break repair genes XRCC5, XRCC6 and susceptibility to hepatocellular carcinoma. Carcinogenesis. 2011;32:530‐536. [DOI] [PubMed] [Google Scholar]
- 10. Yuan H‐Y, Chiou J‐J, Tseng W‐H, et al. FASTSNP: an always up‐to‐date and extendable service for SNP function analysis and prioritization. Nucleic Acids Res. 2006;34:W635‐W641. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Willems P, Claes K, Baeyens A, et al. Polymorphisms in nonhomologous end‐joining genes associated with breast cancer risk and chromosomal radiosensitivity. Genes Chromosomes Cancer. 2008;47:137‐148. [DOI] [PubMed] [Google Scholar]
- 12. Fu YP, Yu JC, Cheng TC, et al. Breast cancer risk associated with genotypic polymorphism of the nonhomologous end‐joining genes: a multigenic study on cancer susceptibility. Cancer Res. 2003;63:2440‐2446. [PubMed] [Google Scholar]
- 13. Seitz HK, Becker P. Alcohol metabolism and cancer risk. Alcohol Res Health. 2007;30:384‐37. [PMC free article] [PubMed] [Google Scholar]
- 14. Cheung V, Yuen VM, Wong GTC, Choi SW. The effect of sleep deprivation and disruption on DNA damage and health of doctors. Anaesthesia. 2019;74:434‐440. [DOI] [PubMed] [Google Scholar]
- 15. Narayan S, Jaiswal AS, Kang D, Srivastava P, Das GM, Gairola CG. Cigarette smoke condensate‐induced transformation of normal human breast epithelial cells in vitro. Oncogene. 2004;23:5880‐5889. [DOI] [PubMed] [Google Scholar]
- 16. Hu JJ, Mohrenweiser HW, Bell DA, Leadon SA, Miller MS. Symposium overview: genetic polymorphisms in DNA repair and cancer risk. Toxicol Appl Pharmacol. 2002;185:64‐73. [DOI] [PubMed] [Google Scholar]
- 17. Pachkowski BF, Winkel S, Kubota Y, Swenberg JA, Millikan RC, Nakamura J. XRCC1 genotype and breast cancer: functional studies and epidemiologic data show interactions between XRCC1 codon 280 His and smoking. Cancer Res. 2006;66:2860‐2868. [DOI] [PubMed] [Google Scholar]
- 18. Huang S, Liu L, Xiang Y, et al. Association of PTPN1 polymorphisms with breast cancer risk: a case‐control study in Chinese females. J Cell Biochem. 2019;120(7):12039‐12050. [DOI] [PubMed] [Google Scholar]
- 19. Liu L‐Y, Wang F, Cui S‐D, et al. A case‐control study on risk factors of breast cancer in Han Chinese women. Oncotarget. 2017;8:97217‐97230. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Hammond ME, Hayes DF, Dowsett M, et al. American Society of Clinical Oncology/College of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer (unabridged version). Arch Pathol Lab Med. 2010;134:e48‐e72. [DOI] [PubMed] [Google Scholar]
- 21. Jin T, Wang Y, Li G, et al. Analysis of difference of association between polymorphisms in the XRCC5, RPA3 and RTEL1 genes and glioma, astrocytoma and glioblastoma. Am J Cancer Res. 2015;5:2294‐2300. [PMC free article] [PubMed] [Google Scholar]
- 22. Li JQ, Chen J, Liu NN, et al. Ku80 gene G‐1401T promoter polymorphism and risk of gastric cancer. World J Gastroenterol. 2011;17:2131‐2136. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Lieber MR. The mechanism of human nonhomologous DNA end joining. J Biol Chem. 2008;283:1‐5. [DOI] [PubMed] [Google Scholar]
- 24. Boulton SJ, Jackson SP. Components of the Ku‐dependent non‐homologous end‐joining pathway are involved in telomeric length maintenance and telomeric silencing. EMBO J. 1998;17:1819‐1828. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Korabiowska M, Voltmann J, Honig JF, et al. Altered expression of DNA double‐strand repair genes Ku70 and Ku80 in carcinomas of the oral cavity. Anticancer Res. 2006;26:2101‐2105. [PubMed] [Google Scholar]
- 26. Mazzarelli P, Parrella P, Seripa D, et al. DNA end binding activity and Ku70/80 heterodimer expression in human colorectal tumor. World J Gastroenterol. 2005;11:6694‐6700. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Bill CA, Summers J. Genomic DNA double‐strand breaks are targets for hepadnaviral DNA integration. Proc Natl Acad Sci USA. 2004;101:11135‐11140. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Varela‐Rey M, Woodhoo A, Martinez‐Chantar ML, Mato JM, Lu SC. Alcohol, DNA methylation, and cancer. Alcohol Res. 2013;35:25‐35. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Testino G. The burden of cancer attributable to alcohol consumption. Maedica (Buchar). 2011;6:313‐320. [PMC free article] [PubMed] [Google Scholar]
- 30. Hosoi Y, Watanabe T, Nakagawa K, et al. Up‐regulation of DNA‐dependent protein kinase activity and Sp1 in colorectal cancer. Int J Oncol. 2004;25:461‐468. [PubMed] [Google Scholar]
- 31. Wang W, Pan X, Huo X, et al. A functional polymorphism C‐1310G in the promoter region of Ku70/XRCC6 is associated with risk of renal cell carcinoma. Mol Carcinog. 2012;51(Suppl 1):E183‐E190. [DOI] [PubMed] [Google Scholar]
- 32. Ouimet M, Cassart P, Lariviere M, Kritikou EA, Simard J, Sinnett D. Functional analysis of promoter variants in KU70 and their role in cancer susceptibility. Genes Chromosomes Cancer. 2012;51:1007‐1013. [DOI] [PubMed] [Google Scholar]
- 33. He W, Luo S, Huang T, et al. The Ku70 ‐1310C/G promoter polymorphism is associated with breast cancer susceptibility in Chinese Han population. Mol Biol Rep. 2012;39:577‐583. [DOI] [PubMed] [Google Scholar]
- 34. Willems P, De Ruyck K, Van den Broecke R, et al. A polymorphism in the promoter region of Ku70/XRCC6, associated with breast cancer risk and oestrogen exposure. J Cancer Res Clin Oncol. 2009;135:1159‐1168. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Ouellet N, Morris DL. Sleep satisfaction of older adults living in the community: identifying associated behavioral and health factors. J Gerontol Nurs. 2006;32:5‐11. [DOI] [PubMed] [Google Scholar]
- 36. Abraham O, Pu J, Schleiden LJ, Albert SM. Factors contributing to poor satisfaction with sleep and healthcare seeking behavior in older adults. Sleep Health. 2017;3:43‐48. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Ohayon MM, Chen MC, Bixler E, et al. A provisional tool for the measurement of sleep satisfaction. Sleep Health. 2018;4:6‐12. [DOI] [PubMed] [Google Scholar]
- 38. Bhatti P, Mirick DK, Randolph TW, et al. Oxidative DNA damage during sleep periods among nightshift workers. Occup Environ Med. 2016;73:537‐544. [DOI] [PubMed] [Google Scholar]
- 39. Chen H, Wu J, Zhang Z, et al. Association between BRCA status and triple‐negative breast cancer: a meta‐analysis. Front Pharmacol. 2018;9:909. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Zhu X, Tian T, Ruan M, et al. Expression of DNA damage response proteins and associations with clinicopathologic characteristics in chinese familial breast cancer patients with BRCA1/2 mutations. J Breast Cancer. 2018;21:297‐305. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Monsees GM, Kraft P, Chanock SJ, Hunter DJ, Han J. Comprehensive screen of genetic variation in DNA repair pathway genes and postmenopausal breast cancer risk. Breast Cancer Res Treat. 2011;125:207‐214. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.