Association Between Polymorphisms in DNA Damage Repair Pathway Genes and Female Breast Cancer Risk

Abstract

Breast cancer risk have been discussed to be associated with polymorphisms in genes as well as abnormal DNA damage repair function. This study aims to assess the relationship between genes single nucleotide polymorphisms (SNPs) related to DNA damage repair and female breast cancer risk in Chinese population. A case–control study containing 400 patients and 400 healthy controls was conducted. Genotype was identified using the sequence MassARRAY method and expression of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor-2 (HER-2) in tumor tissues was analyzed by immunohistochemistry assay. The results revealed that ATR rs13091637 decreased breast cancer risk influenced by ER, PR (CT/TT vs. CC: adjusted odds ratio [OR] = 1.54, 95% confidence interval [CI]: 1.04–2.27, p = 0.032; CT/TT vs. CC: adjusted OR = 1.63, 95%CI: 1.14–2.35, p = 0.008) expression. Stratified analysis revealed that PALB2 rs16940342 increased breast cancer risk in response to menstrual status (AG/GG vs. AA: adjusted OR = 1.72, 95%CI: 1.13–2.62, p = 0.011) and age of menarche (AG/GG vs. AA: adjusted OR = 1.54, 95%CI: 1.03–2.31, p = 0.037), whereas ATM rs611646 and Ku70 rs132793 were associated with reduced breast cancer risk influenced by menarche (GA/AA vs. GG: adjusted OR = 0.50, 95%CI: 0.30–0.95, p = 0.033). In a summary, PALB2 rs16940342, ATR rs13091637, ATM rs611646, and Ku70 rs132793 were associated with breast cancer risk.

Introduction

Breast cancer, a highly heterogeneous disease (Duijf et al., 2019), has an increasing impact on health of women worldwide (Trapani et al., 2022). Cancer heterogeneity can be driven by genomic instability (Kudelova et al., 2022). Mutations of genes involved in DNA damage repair pathway could disrupt the normal function, leading to genomic instability that ultimately facilitates the development and progression of cancer (Tubbs and Nussenzweig, 2017). The etiology of breast cancer is multifaceted, involving physiological, environmental, and genetic factors, with polymorphisms of genes across various biological pathways posing potential susceptibility for breast cancer (Bahreini et al., 2021).

Single nucleotide polymorphisms (SNPs) are DNA sequence polymorphisms generated by variations in a single nucleotide, which is the common type of human genetic variation, occurring in every 300 bases (Abd El-Fattah et al., 2018; Fadason et al., 2022). SNPs are common in the human genome, which can cause genetic differences between individuals, serving as significant genetic markers in the investigation of cancer and its characteristics. Growing evidence links breast cancer susceptibility to SNPs (Gao et al., 2019).

DNA damage repair pathways, which play an important role in maintaining genome stability and cellular homeostasis, include mismatch repair (MMR), base excision repair (BER), nucleotide excision repair (NER), nonhomologous end-joining (NHEJ) and homologous recombination (HR) repair (Kottemann and Smogorzewska, 2013; Scully et al., 2019). The occurrence and development of cancer are closely associated with the abnormal DNA damage repair function (Huang and Zhou, 2021).

RAD21, PALB2, ATR, ATM, Ku70, and Ku80 are all involved to DNA damage repair pathways and SNPs of these genes are likely to be linked to cancer risk. For example, RAD21 rs2289937 and rs4579555 have been proved to be associated with cervical cancer (Xia et al., 2018), RAD21 rs16888927, rs16888997, and rs16889040 may affect the risk of breast cancer and ovarian cancer (Sehl et al., 2009).

Studies have proved that variants of PALB2 conferred breast cancer susceptibility in the Chinese population, and PALB2 rs249954 and rs120963 were associated with increased cancer risk (Chen et al., 2008). In addition, several previous studies have explored the association of ATR, Ku70 and Ku80 SNPs with cancer risk (Henríquez-Hernández et al., 2016; Li et al., 2011; Lin et al., 2013; Singh et al., 2018; Vuorinen et al., 2023; Zhang et al., 2017).

However, breast cancer incidence continues to increase among Chinese women and there is still a lack of early screening, detection, and effective targeted treatment for breast cancer (Wang et al., 2021). Studies have demonstrated the association of SNPs with cancer risk, as well as the interconnection between abnormalities in DNA damage repair and cancer. On this basis, we expect to conduct a more in-depth analysis of the relationship between SNPs in genes of DNA damage repair pathway and breast cancer risk, with the goal of identifying potential markers for the disease.

Materials and Methods

Study subjects

Four hundred patients previously diagnosed with breast cancer (mean age 53.17 ± 10.79 years) and 400 healthy controls (mean age 52.46 ± 10.88 years) who took physical examination were recruited for the study in Nanjing First Hospital from January 2008 to January 2017. Both the case and control groups were from the same region. A questionnaire was used to collect information of patients, including smoking, drinking, and family history.

The inclusion criteria were as follows: (1) breast cancer with female patients; (2) no history of smoking and drinking; (3) no other types of cancer; and (4) no personal or family history. In addition, we collected other information about the patients, including age at menarche, reproductive history, menopause, and tumor type. Furthermore, the SNPs of genes we included in the study were associated with the DNA damage repair pathway, including MMR, BER, NER, NHEJ, and HR.

Whole blood samples were collected from patients receiving surgical or pharmacologic treatment. The study protocol was approved by the Institutional Review Board of the Nanjing First Hospital and written informed consent was obtained from all participants (KY20220124-04). The methods were performed in accordance with approved guidelines.

SNP genotyping

DNA was extracted from whole blood samples from patients by the GoldMag-Mini Whole Blood Genomic DNA Purification Kit (GoldMag, Xi'an, China) according to the protocol and the concentration, and purification of the DNA were measured using the spectrometry (DU 530 UV/Vis spectrophotometer, Beckman Instruments, Fullerton, CA, USA). SNP genotyping was performed using Sequence MassARRAY RS-1000 (Sequenom, San Diego, CA, USA). Furthermore, we used Sequenom Typer 4.0 software for data management and analysis (Gabriel et al., 2009; Thomas et al., 2007).

Immunohistochemistry assay

The immunohistochemistry assay was used to detect the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER-2) in paraffin-embedded tumor tissues. The rabbit monoclonal antibodies to ER, PR, and HER-2 used in this study were purchased from Spring Bioscience (Pleasanton, CA, USA).

Statistical analysis

Patient characteristics were assessed using the t-test and fitted chi-squared (χ²) test, and the Hardy–Weinberg equilibrium (HWE) in the healthy control group was assessed using the two-sided χ² test. The association between SNPs and the risk of developing breast cancer was evaluated by calculating the odds ratio (OR) and 95% confidence interval (CI) using logistic regression. We used SPSS 23.0 software (IBM Corp, Armonk, NY, USA) to analyze the data. A p < 0.05 indicating a statistically significant difference.

Results

A total of 400 patients and the same number of healthy controls were included in our study. The information of the study population was summarized in Table 1, and there was no statistically significant difference in age. We collected data on genes involved in DNA damage repair pathways, including MMR, BER, NER, NHEJ, and HR. After a screening process, we identified significant genes within the NHEJ and HR pathways for further analysis. The genotypic distribution of SNP in patients and controls is shown in Table 2, and the genotypic distribution of all SNPs was not biased in HWE.

Table 1.

Characteristics of Patients with Breast Cancer and Healthy Controls

Characteristics	Cases, n (%)	Controls, n (%)	p
Total patients	400	400
Age (mean ± SD, years)	53.17 ± 10.79	52.46 ± 10.88	0.358^a
Menopausal status			0.028^b
Premenopausal	172 (43.00)	203 (50.75)
Postmenopausal	228 (57.00)	197 (49.25)
Menarche			0.571^b
≤14 years old	209 (52.25)	217 (54.25)
>14 years old	191 (47.75)	183 (45.75)
Birth			0.887^b
≤1	207 (51.75)	209 (52.25)
>1	193 (48.25)	191 (47.75)
ER
Negative	151 (37.75)
Positive	249 (62.25)
PR
Negative	188 (47.00)
Positive	212 (53.00)
HER-2
Negative	107 (26.75)
Positive	293 (73.25)

Independent t-test.

Two-sided chi-squared (χ²) test for the selected variables between cases and controls.

ER, estrogen receptor; HER-2, human epidermal growth factor receptor-2; PR, progesterone receptor; SD, standard deviation.

Table 2.

Information on the Included Genetic Variations

Gene	SNP ID	Chromosome	HWE in controls (p value/χ²)
ATR	rs13091637	3:142547597	0.184/1.764
ATM	rs611646	11:108306370	0.102/2.676
PALB	rs16940342	16:23633265	0.376/0.784
	rs249935	16:23613903	0.538/0.380
RAD51	rs2412546	15:40714325	0.666/0.186
	rs4417527	15:40729082	0.781/0.077
	rs1801320	15:40695330	0.345/0.891
BLM	rs2380165	15:90797765	0.665/0.187/
Ku70	rs5751129	22:41619761	0.924/0.009
	rs132793	22:41667677	0.449/0.573
Ku80	rs11685387	2:216109091	0.075/3.172
MLH3	rs175080	14:75047125	0.872/0.026
POLE	rs5744857	12:132659414	0.828/0.047
REV3L	rs462779	6:111374684	0.155/2.020
RAD21	rs16889040	8:116866452	0.792/0.069
	rs16888927	8:116857221	0.076/3.149

HWE, Hardy–Weinberg equilibrium.

Logistic regression revealed that the RAD21 rs16888927 CC genotype was associated with an increased breast cancer risk in all participants (CC vs. TT: adjusted OR = 3.93, 95%CI: 1.08–14.27, p = 0.038). Moreover, it was observed that other SNPs did not exhibit a correlation with the breast cancer risk, as shown in Table 3.

Table 3.

Genotype Distribution of Polymorphisms

Genotype	Case, n (%)	Control, n (%)	OR (95% CI)	p	OR (95% CI)^a	p ^a
ATR rs13091637
CC	265 (66.25)	283 (70.75)	Reference		Reference
CT	122 (30.50)	111 (27.75)	1.17 (0.86–1.60)	0.306	1.20 (0.88–1.63)	0.253
TT	13 (3.25)	6 (1.50)	2.31 (0.87–6.18)	0.094	2.24 (0.84–6.00)	0.109
CT/TT	135 (33.75)	117 (29.25)	1.23 (0.91–1.66)	0.171	1.25 (0.93–1.69)	0.143
ATM rs611646
AA	149 (37.34)	136 (34.52)	Reference		Reference
AT	178 (44.61)	204 (51.78)	0.80 (0.59–1.08)	0.147	0.79 (0.58–1.08)	0.139
TT	72 (18.05)	54 (13.70)	1.22 (0.80–1.86)	0.362	1.18 (0.77–1.81)	0.438
AT/TT	250 (62.66)	258 (65.48)	0.88 (0.66–1.18)	0.407	0.87 (0.65–1.17)	0.368
PALB2 rs16940342
AA	236 (59.30)	241 (64.44)	Reference		Reference
AG	136 (34.17)	115 (30.75)	1.21 (0.89–1.64)	0.227	1.25 (0.91–1.70)	0.164
GG	26 (6.53)	18 (4.81)	1.48 (0.79–2.76)	0.225	1.57 (0.83–2.95)	0.163
AG/GG	162 (40.70)	133 (35.56)	1.24 (0.93–1.66)	0.142	1.29 (0.96–1.73)	0.091
PALB2 rs249935
AA	131 (62.38)	187 (66.08)	Reference		Reference
AG	74 (35.24)	88 (31.09)	1.20 (0.82–1.76)	0.348	1.17 (0.80–1.72)	0.417
GG	5 (2.38)	8 (2.83)	0.89 (0.29–2.79)	0.844	0.86 (0.27–2.69)	0.790
AG/GG	79 (37.62)	96 (33.92)	1.18 (0.81–1.70)	0.396	1.15 (0.79–1.67)	0.476
RAD51 rs2412546
AA	253 (63.41)	145 (49.66)	Reference		Reference
AG	127 (31.83)	128 (43.83)	0.96 (0.71–1.30)	0.795	0.98 (0.73–1.33)	0.906
GG	19 (4.76)	19 (6.51)	0.97 (0.50–1.87)	0.924	0.96 (0.49–1.86)	0.894
AG/GG	146 (36.59)	147 (50.34)	0.96 (0.72–1.28)	0.791	0.98 (0.73–1.31)	0.883
RAD51 rs4417527
GG	194 (48.74)	198 (50.26)	Reference		Reference
GC	170 (42.71)	161 (40.86)	1.08 (0.80–1.44)	0.616	1.10 (0.82–1.48)	0.509
CC	34 (8.54)	35 (8.88)	0.99 (0.59–1.65)	0.974	0.98 (0.59–1.64)	0.935
GC/CC	204 (51.26)	196 (49.75)	1.06 (0.80–1.40)	0.671	1.08 (0.82–1.43)	0.584
RAD51 rs1801320
GG	297 (74.25)	284 (71.00)	Reference		Reference
GC	93 (23.25)	109 (27.25)	0.82 (0.59–1.13)	0.214	0.84 (0.61–1.16)	0.284
CC	10 (2.50)	7 (1.75)	1.37 (0.51–3.64)	0.533	1.36 (0.50–3.57)	0.563
GC/CC	103 (25.75)	116 (29.00)	0.85 (0.62–1.16)	0.303	0.87 (0.64–1.19)	0.380
BLM rs2380165
AA	222 (55.64)	233 (58.54)	Reference		Reference
AG	148 (37.09)	141 (35.43)	1.10 (0.82–1.48)	0.520	1.12 (0.83–1.50)	0.460
GG	29 (7.27)	24 (6.03)	1.27 (0.72–2.25)	0.415	1.23 (0.69–2.19)	0.476
AG/GG	177 (44.36)	165 (41.46)	1.13 (0.85–1.49)	0.408	1.14 (0.86–1.51)	0.378
Ku70 rs5751129
TT	321 (80.25)	323 (81.36)	Reference		Reference
TC	76 (19.00)	70 (17.63)	1.09 (0.76–1.57)	0.630	1.07 (0.75–1.54)	0.711
CC	3 (0.75)	4 (1.01)	0.76 (1.68–3.40)	0.714	0.79 (0.17–3.48)	0.732
TC/CC	79 (19.75)	74 (18.64)	1.07 (0.76–1.53)	0.691	1.06 (0.74–1.50)	0.769
Ku70 rs132793
GG	339 (84.96)	330 (82.50)	Reference		Reference
GA	57 (14.29)	68 (17.00)	0.82 (0.56–1.20)	0.298	0.80 (0.55–1.18)	0.265
AA	3 (0.75)	2 (0.50)	1.46 (0.24–8.80)	0.679	1.66 (0.27–10.11)	0.580
GA/AA	60 (15.04)	70 (17.50)	0.83 (0.57–1.22)	0.346	0.83 (0.57–1.21)	0.322
Ku80 rs11685387
TT	177 (44.36)	174 (44.50)	Reference		Reference
TC	176 (44.11)	185 (47.31)	0.94 (0.70–1.26)	0.655	0.92 (0.68–1.23)	0.561
CC	46 (11.53)	32 (8.18)	1.41 (0.86–2.32)	0.173	1.40 (0.85–2.31)	0.187
TC/CC	222 (55.64)	217 (55.50)	1.01 (0.76–1.33)	0.968	0.99 (0.75–1.31)	0.929
MLH3 rs175080
GG	289 (72.43)	281 (70.25)	Reference		Reference
GA	103 (25.81)	108 (27.00)	0.93 (0.68–1.27)	0.640	0.92 (0.67–1.27)	0.613
AA	7 (1.75)	11 (2.75)	0.62 (0.24–1.62)	0.328	0.58 (0.22–1.53)	0.274
GA/AA	110 (27.59)	119 (29.75)	0.80 (0.66–1.22)	0.495	0.90 (0.65–1.21)	0.456
POLE rs5744857
AA	178 (44.84)	197 (50.90)	Reference		Reference
AG	185 (46.60)	157 (40.57)	1.30 (0.97–1.75)	0.076	1.32 (0.98–1.77)	0.068
GG	34 (8.56)	33 (8.53)	1.14 (0.68–1.92)	0.621	1.17 (0.69–1.98)	0.552
AG/GG	219 (55.16)	190 (49.10)	1.28 (0.96–1.69)	0.089	1.29 (0.97–1.71)	0.076
REV3L rs462779
CC	110 (27.78)	100 (26.46)	Reference		Reference
CT	207 (52.27)	202 (53.44)	0.93 (0.67–1.30)	0.677	0.94 (0.67–1.31)	0.697
TT	79 (19.95)	76 (20.11)	0.95 (0.62–1.43)	0.789	0.96 (0.63–1.46)	0.855
CT/TT	286 (72.22)	278 (73.64)	0.94 (0.68–1.28)	0.679	0.94 (0.69–1.30)	0.717
RAD21 rs16889040
GG	292 (73.37)	290 (73.23)	Reference		Reference
GA	94 (23.62)	97 (24.37)	0.96 (0.69–1.34)	0.818	0.98 (0.71–1.36)	0.904
AA	12 (3.02)	9 (2.26)	1.32 (0.55–3.19)	0.531	1.34 (0.56–3.25)	0.512
GA/AA	106 (26.63)	106 (26.63)	0.99 (0.73–1.36)	0.966	1.01 (0.74–1.39)	0.946
RAD21 rs16888927
TT	291 (72.75)	293 (73.99)	Reference		Reference
TC	98 (24.50)	100 (25.25)	0.99 (0.72–1.36)	0.987	1.01 (0.73–1.39)	0.971
CC	11 (2.75)	3 (0.76)	3.69 (1.02–13.37)	0.047	3.93 (1.08–14.27)	0.038
TC/CC	109 (27.25)	103 (26.01)	1.07 (0.78–1.46)	0.692	1.09 (0.79–1.49)	0.598

Adjusted by age and menopausal status. The results with significant differences are Bold.

CI, confidence interval; OR, odds ratio.

Furthermore, we considered the impact of menopausal status on the risk of breast cancer in the study. Subsequent stratified analysis revealed that there was no significant association between SNPs and breast cancer risk in the premenopausal cohort. In the postmenopausal population, PALB2 rs16940342 AG genotype (AG vs. AA: adjusted OR = 1.59, 95%CI: 1.03–2.48, p = 0.038) and AG/GG genotype (AG/GG vs. AA: adjusted OR = 1.72, 95%CI: 1.13–2.62, p = 0.011) increased the risk of breast cancer, as demonstrated in Table 4.

Table 4.

Stratified Effects of Polymorphisms on Breast Cancer Risk by Menopausal Status

	Premenopausal			Postmenopausal
Genotype	Case/control	OR (95% CI)^a	p ^a	Case/control	OR (95% CI)^a	p ^a
ATR rs13091637
CC	117/141	Reference		148/142	Reference
CT	52/59	1.07 (0.68–1.67)	0.779	70/52	1.32 (0.86–2.04)	0.198
TT	3/3	1.22 (0.24–6.18)	0.811	10/3	3.18 (0.86–11.81)	0.084
CT/TT	55/62	1.07 (0.69–1.67)	0.752	80/55	1.43 (0.94–2.17)	0.092
ATM rs611646
AA	64/69	Reference		85/67	Reference
AT	83/105	0.85 (0.55–1.33)	0.480	95/99	0.74 (0.48–1.14)	0.167
TT	25/25	1.08 (0.56–2.07)	0.828	47/29	1.27 (0.72–2.23)	0.406
AT/TT	108/130	0.89 (0.58–1.37)	0.606	142/128	0.86 (0.58–1.28)	0.460
PALB2 rs16940342
AA	96/104	Reference		140/137	Reference
AG	62/70	0.96 (0.62–1.49)	0.851	74/45	1.59 (1.03–2.48)	0.038
GG	12/13	1.00 (0.43–2.23)	0.990	14/5	2.86 (1.00–8.24)	0.049
AG/GG	74/83	0.96 (0.63–1.47)	0.865	88/50	1.72 (1.13–2.62)	0.011
PALB2 rs249935
AA	63/102	Reference		68/85	Reference
AG	27/37	1.18 (0.65–2.12)	0.591	47/51	1.15 (0.69–1.92)	0.583
GG	3/1	5.05 (0.51–49.98)	0.166	2/7	0.36 (0.07–1.78)	0.210
AG/GG	30/38	1.28 (0.72–2.26)	0.407	49/58	1.06 (0.64–1.74)	0.826
RAD51 rs2412546
AA	105/123	Reference		148/122	Reference
AG	61/69	1.04 (0.67–1.60)	0.870	66/59	0.93 (0.61–1.42)	0.739
GG	6/8	0.88 (0.30–2.61)	0.814	13/11	1.01 (0.44–2.34)	0.985
AG/GG	69/77	1.02 (0.67–1.55)	0.925	79/70	0.94 (0.63–1.41)	0.772
RAD51 rs4417527
GG	76/100	Reference		118/97	Reference
GC	85/83	1.35 (0.88–2.06)	0.168	85/78	0.91 (0.61–1.38)	0.667
CC	11/16	0.90 (0.40–2.06)	0.810	23/19	1.02 (0.52–1.99)	0.952
GC/CC	96/99	1.28 (0.85–1.92)	0.243	108/97	0.94 (0.64–1.37)	0.732
RAD51 rs1801320
GG	123/142	Reference		174/142	Reference
GC	47/57	0.95 (0.60–1.50)	0.831	46/52	0.74 (0.47–1.17)	0.200
CC	2/4	0.57 (0.10–3.19)	0.525	8/3	2.25 (0.58–8.70)	0.238
GC/CC	51/61	0.93 (0.59–1.45)	0.739	54/55	0.82 (0.53–1.28)	0.385
BLM rs2380165
AA	89/120	Reference		133/113	Reference
AG	71/70	1.37 (0.89–2.10)	0.153	77/71	0.93 (0.62–1.40)	0.737
GG	11/11	1.34 (0.56–3.24)	0.514	18/13	1.16 (0.54–2.47)	0.702
AG/GG	82/81	1.36 (0.90–2.06)	0.139	95/84	0.97 (0.66–1.42)	0.868
Ku70 rs5751129
TT	141/168	Reference		180/155	Reference
TC	29/31	1.11 (0.64–1.94)	0.705	47/39	1.04 (0.65–1.68)	0.864
CC	2/1	2.39 (0.21–26.59)	0.480	1/3	0.31 (0.03–2.99)	0.308
TC/CC	31/32	1.15 (0.67–1.98)	0.607	48/42	0.99 (0.62–1.58)	0.973
Ku70 rs132793
GG	147/172	Reference		192/158	Reference
GA	23/30	0.90 (0.50–1.61)	0.716	34/38	0.74 (0.45–1.23)	0.248
AA	2/1	2.34 (0.21–26.11)	0.489	1/1	1.08 (0.07–17.97)	0.959
GA/AA	25/31	0.94 (0.53–1.67)	0.842	35/39	0.75 (0.45–1.24)	0.259
Ku80 rs11685387
TT	76/95	Reference		101/79	Reference
TC	78/86	1.13 (0.74–1.74)	0.568	98/99	0.75 (0.50–1.13)	0.175
CC	18/17	1.32 (0.64–2.74)	0.451	28/15	1.46 (0.73–2.93)	0.284
TC/CC		1.17 (0.77–1.76)	0.466	126/114	0.85 (0.57–1.25)	0.404
MLH3 rs175080
GG	128/143	Reference		161/138	Reference
GA	43/56	0.86 (0.54–1.36)	0.512	60/52	0.99 (0.64–1.53)	0.950
AA	1/4	0.28 (0.03–2.53)	0.256	6/7	0.75 (0.24–2.28)	0.606
GA/AA		0.82 (0.52,1.29)	0.388	66/59	0.96 (0.63–1.46)	0.840
POLE rs5744857
AA	73/99	Reference		105/98	Reference
AG	82/78	1.43 (0.93–2.20)	0.108	103/79	1.23 (0.82–1.84)	0.321
GG	16/22	0.98 (0.48–2.00)	0.958	18/11	1.51 (0.68–3.39)	0.310
AG/GG	98/100	1.33 (0.88–2.00)	0.176	121/90	1.26 (0.86–1.86)	0.240
REV3L rs462779
CC	47/47	Reference		63/53	Reference
CT	88/101	0.87 (0.53–1.43)	0.585	119/101	0.99 (0.63–1.55)	0.960
TT	36/41	0.88 (0.48–1.61)	0.673	43/35	1.05 (0.59–1.87)	0.867
CT/TT	124/142	0.87 (0.55–1.40)	0.572	162/136	1.00 (0.65–1.55)	0.985
RAD21 rs16889040
GG	120/145	Reference		172/145	Reference
GA	46/50	1.11 (0.69–1.77)	0.665	48/47	0.88 (0.56–1.40)	0.594
AA	5/5	1.21 (0.34–4.28)	0.768	7/4	1.49 (0.43–5.20)	0.531
GA/AA	51/55	1.12 (0.71–1.76)	0.628	55/51	0.93 (0.59–1.50)	0.749
RAD21 rs16888927
TT	119/146	Reference		172/147	Reference
TC	48/52	1.13 (0.71–1.79)	0.605	50/48	0.91 (0.58–1.43)	0.682
CC	5/3	2.05 (0.48–8.75)	0.333	6/0
TC/CC	53/55	1.18 (0.75–1.85)	0.470	56/48	1.02 (0.65–1.59)	0.935

Adjusted by age. The results with significant differences are Bold.

Moreover, a subgroup analysis was performed based on patients' age of menarche and number of births (Table 5). The results showed that the PALB2 rs16940342 AG/GG genotype increased the risk of breast cancer in participants who had the menarche before 14 years old (AG/GG vs. AA: adjusted OR = 1.54, 95%CI: 1.03–2.31, p = 0.037). Meanwhile, among participants with menarche after 14 years old, ATM rs611646 AT/TT genotype was associated with decreased breast cancer risk (AT/TT vs. AA: adjusted OR = 0.61, 95%CI: 0.40–0.94, p = 0.026).

Table 5.

Stratified Effects of Single Nucleotide Polymorphisms on Breast Cancer Risk by Menarche and Births

	Menarche ≤14			Menarche >14			Birth ≤1			Birth >1
Genotype	Case/control	OR (95% CI)^a	p ^a	Case/control	OR (95% CI)^a	p ^a	Case/control	OR (95% CI)^a	p ^a	Case/control	OR (95% CI)^a	p ^a
ATR rs13091637
CC	138/148	Reference		127/135	Reference		138/146	Reference		127/137	Reference
CT/TT	71/69	1.08 (0.72–1.62)	0.709	63/48	1.43 (0.91–2.23)	0.117	69/63	1.09 (0.72–1.66)	0.693	66/54	1.34 (0.87–2.07)	0.188
ATM rs611646
AA	70/81	Reference		79/55	Reference		75/71	Reference		74/65	Reference
AT/TT	139/133	1.23 (0.82–1.83)	0.315	111/125	0.61 (0.40–0.94)	0.026	131/134	0.94 (0.63–1.42)	0.774	119/124	0.83 (0.55–1.27)	0.390
PALB2 rs16940342
AA	120/135	Reference		116/106	Reference		117/114	Reference		119/127	Reference
AG/GG	88/66	1.54 (1.03–2.31)	0.037	74/67	0.98 (0.64–1.51)	0.940	88/82	1.10 (0.73–1.64)	0.658	74/51	1.54 (0.97–2.38)	0.052
PALB2 rs249935
AA	67/106	Reference		64/81	Reference		66/99	Reference		65/88	Reference
AG/GG	41/56	1.10 (0.66–1.84)	0.713	38/40	1.22 (0.70–2.12)	0.486	46/46	1.32 (0.77–2.24)	0.305	33/50	0.91 (0.53–1.59)	0.741
RAD51 rs2412546
AA	139/143	Reference		114/102	Reference		129/130	Reference		124/115	Reference
AG/GG	70/72	1.00 (0.67–1.51)	0.985	76/75	0.91 (0.60–1.38)	0.646	78/75	1.04 (0.69–1.55)	0.868	68/72	0.87 (0.57–1.33)	0.521
RAD51 rs4417527
GG	112/112	Reference		82/86	Reference		95/107	Reference		99/91	Reference
GC/CC	95/102	0.94 (0.64–1.38)	0.739	109/94	1.22 (0.81–1.83)	0.348	111/98	1.28 (0.86–1.89)	0.220	93/98	0.87 (0.58–1.30)	0.498
RAD51 rs1801320
GG	160/156	Reference		137/128	Reference		153/150	Reference		144/134	Reference
GC/CC	49/61	0.78 (0.51–1.21)	0.270	54/55	0.92 (0.59–1.43)	0.707	54/59	0.91 (0.59–1.41)	0.684	49/57	0.80 (0.51–1.26)	0.804
BLM rs2380165
AA	113/127	Reference		109/106	Reference		106/123	Reference		116/110	Reference
AG/GG	96/88	1.23 (0.84–1.81)	0.291	81/77	1.02 (0.68–1.54)	0.915	101/84	1.43 (0.97–2.12)	0.074	76/81	0.90 (0.59–1.35)	0.613
Ku70 rs5751129
TT	163/178	Reference		158/145	Reference		169/179	Reference		152/144	Reference
TC/CC	46/38	1.30 (0.81–2.11)	0.281	33/36	0.85 (0.50–1.43)	0.538	38/27	1.47 (0.85–2.52)	0.168	41/47	0.81 (0.51–1.31)	0.400
Ku70 rs132793
GG	170/183	Reference		169/147	Reference		178/182	Reference		161/148	Reference
GA/AA	38/34	1.19 (0.71–1.97)	0.512	22/36	0.54 (0.30–0.95)	0.033	29/27	1.09 (0.62–1.93)	0.761	31/43	0.66 (0.39–1.10)	0.110
Ku80 rs11685387
TT	95/97	Reference		82/77	Reference		91/98	Reference		86/76	Reference
TC/CC	114/114	1.02 (0.69–1.50)	0.921	108/103	0.98 (0.65–1.49)	0.936	116/105	1.18 (0.80–1.75)	0.410	106/112	0.82 (0.55–1.24)	0.347
MLH3 rs175080
GG	147/156	Reference		142/125	Reference		148/148	Reference		141/133	Reference
GA/AA	61/61	1.06 (0.69–1.61)	0.802	49/58	0.74 (0.47–1.17)	0.195	59/61	0.96 (0.62–1.47)	0.842	51/58	0.83 (0.53–1.30)	0.413
POLE rs5744857
AA	95/104	Reference		83//93	Reference		91/103	Reference		87/94	Reference
AG/GG	113/107	1.15 (0.78–1.70)	0.478	106/83	1.43 (0.94–2.15)	0.093	115/102	1.30 (0.86–1.92)	0.196	104/88	1.28 (0.85–1.92)	0.240
REV3L rs462779
CC	52/57	Reference		58/43	Reference		55/49	Reference		55/51	Reference
CT/TT	156/153	1.15 (0.74–1.79)	0.526	130/125	0.78 (0.49–1.24)	0.295	151/146	0.91 (0.58–1.44)	0.698	135/132	0.95 (0.61–1.49)	0.828
RAD21 rs16889040
GG	152/155	Reference		140/135	Reference		147/154	Reference		145/136	Reference
GA/AA	55/61	0.92 (0.60–1.41)	0.696	51/45	1.09 (0.69–1.73)	0.725	59/52	1.26 (0.81–1.97)	0.304	47/54	0.82 (0.52–1.30)	0.398
RAD21 rs16888927
TT	151/157	Reference		140/136	Reference		146/155	Reference		145/138	Reference
TC/CC	56/59	1.03 (0.70–1.57)	0.905	51/44	1.12 (0.70–1.79)	0.636	61/52	1.32 (0.85–2.05)	0.217	48/51	0.90 (0.57–1.42)	0.650

Adjusted by age. The results with significant differences are Bold.

Furthermore, Ku70 rs132793 showed a consistent result that GA/AA genotype (GA/AA vs. GG: adjusted OR = 0.54, 95%CI: 0.30–0.95, p = 0.033) reduced breast cancer risk. Regarding the effect of number of births, our analysis revealed that SNPs were not significantly associated with breast cancer risk in populations with number of births ≤1 or number of births >1.

A subgroup analysis was conducted to assess the influence of ER, PR, and HER-2 expression on the risk of breast cancer (Table 6). We found that the ATR rs13091637 CT/TT genotype increased the risk of ER-negative breast cancer (CT/TT vs. CC: adjusted OR = 1.54, 95%CI: 1.04–2.27, p = 0.032). Besides, the allele T of ATR rs13091637 increased the risk of PR-negative breast cancer (CT/TT vs. CC: adjusted OR = 1.63, 95%CI: 1.14–2.35, p = 0.008). As for participants with HER-2-negative or positive breast cancer, our results did not significantly show an association between SNPs and the risk of developing breast cancer.

Table 6.

Stratified Effects of Single Nucleotide Polymorphisms on Breast Cancer Risk by the Expression of Estrogen Receptor, Progesterone Receptor, and Human Epidermal Growth Factor Receptor-2

		ER (−)			ER (+)			PR (−)			PR (+)			HER2 (−)			HER2 (+)
Genotype	Control	Case	OR (95% CI)^a	p ^a	Case	OR (95% CI)^a	p ^a	Case	OR (95% CI)^a	p ^a	Case	OR (95% CI)^a	p ^a	Case	OR (95% CI)^a	p ^a	Case	OR (95% CI)^a	p ^a
ATR rs13091637
CC	283	92	Reference		173	Reference		112	Reference		153	Reference		69	Reference		196	Reference
CT/TT	117	59	1.54 (1.04–2.27)	0.032	76	1.06 (0.75–1.50)	0.738	76	1.63 (1.14–2.35)	0.008	59	0.93 (0.64–1.35)	0.696	38	1.35 (0.86–2.13)	0.191	97	1.19 (0.86–1.65)	0.294
ATM rs611646
AA	136	60	Reference		89	Reference		76	Reference		73	Reference		37	Reference		112	Reference
AT/TT	258	91	0.81 (0.55–1.19)	0.282	159	0.94 (0.68–1.32)	0.735	112	0.78 (0.55–1.12)	0.185	138	1.00 (0.70–1.42)	0.997	70	0.98 (0.63–1.55)	0.946	180	0.86 (0.62–1.17)	0.328
PALB2 rs16940342
AA	241	89	Reference			Reference		107	Reference		129	Reference		61	Reference		175	Reference
AG/GG	133	61	1.28 (0.86–1.89)	0.218	16	1.26 (0.90–1.75)	0.178	80	1.39 (0.97–2.00)	0.075	82	1.16 (0.82–1.65)	0.396	45	1.30 (0.83–2.03)	0.246	117	1.25 (0.91–1.71)	0.177
PALB2 rs249935
AA	187	53	Reference		78	Reference		61	Reference		70	Reference		38	Reference		93	Reference
AG/GG	96	23	0.82 (0.48–1.43)	0.491	56	1.39 (0.91–2.13)	0.129	32	1.02 (0.62–1.67)	0.946	47	1.28 (0.82–2.00)	0.280	21	1.08 (0.60–1.95)	0.795	58	1.18 (0.78–1.79)	0.424
RAD51 rs2412546
AA	145	91	Reference		161	Reference		112	Reference		141	Reference		69	Reference		184	Reference
AG/GG	147	58	1.05 (0.71–1.55)	0.798	88	0.91 (0.66–1.27)	0.580	75	1.12 (0.78–1.59)	0.550	71	0.84 (0.59–1.19)	0.332	38	0.92 (0.59–1.44)	0.712	108	0.98 (0.72–1.34)	0.982
RAD51 rs4417527
GG	198	72	Reference		122	Reference		86	Reference		108	Reference		50	Reference		144	Reference
GC/CC	196	150	1.09 (0.75–1.59)	0.650	126	1.04 (0.76–1.43)	0.794	101	1.18 (0.83–1.68)	0.349	103	0.96 (0.69–1.35)	0.832	57	1.15 (0.75–1.77)	0.512	147	1.03 (0.76–1.40)	0.849
RAD51 rs1801320
GG	297	110	Reference		187	Reference		134	Reference		163	Reference		80	Reference		217	Reference
GC/CC	103	41	0.91 (0.60–1.39)	0.665	62	0.81 (0.57–1.16)	0.254	54	0.98 (0.67–1.44)	0.928	49	0.74 (0.50–1.08)	0.120	27	0.83 (0.51–1.35)	0.454	76	0.86 (0.61–1.21)	0.382
BLM rs2380165
AA	233	81	Reference		141	Reference		104	Reference		118	Reference		58	Reference		164	Reference
AG/GG	165	69	1.20 (0.82–1.75)	0.347	108	1.08 (0.79–1.49)	0.631	83	1.13 (0.79–1.60)	0.506	94	1.12 (0.80–1.57)	0.496	49	1.20 (0.78–1.84)	0.409	128	1.10 (0.81–1.50)	0.528
Ku70 rs5751129
TT	323	122	Reference		199	Reference		148	Reference		173	Reference		87	Reference		234	Reference
TC/CC	74	29	1.03 (0.64–1.66)	0.901	50	1.10 (0.73–1.63)	0.669	40	1.17 (0.76–1.84)	0.473	39	0.98 (0.64–1.50)	0.917	20	1.01 (0.59–1.76)	0.960	59	1.09 (0.74–1.58)	0.693
Ku70 rs132793
GG	330	128	Reference		211	Reference		156	Reference		183	Reference		93	Reference		246	Reference
GA/AA	70	23	0.84 (0.50–1.41)	0.515	37	0.82 (0.53–1.28)	0.371	32	0.96 (0.61–1.52)	0.859	28	0.72 (0.45–1.15)	0.166	14	0.72 (0.39–1.33)	0.295	46	0.87 (0.58–1.30)	0.492
Ku80rs11685387
TT	174	71	Reference		106	Reference		88	Reference		89	Reference		46	Reference		131	Reference
TC/CC	217	80	0.88 (0.61–1.29)	0.525	144	1.08 (0.79–1.49)	0.626	100	0.90 (0.63–1.28)	0.555	124	1.11 (0.79–1.55)	0.552	62	1.07 (0.69–1.64)	0.772	162	0.98 (0.73–1.34)	0.918
MLH3 rs175080
GG	281	103	Reference		186	Reference		132	Reference		157	Reference		73	Reference		216	Reference
GA/AA	119	48	1.10 (0.73–1.64)	0.654	62	0.79 (0.55–1.13)	0.190	56	1.00 (0.69–1.47)	0.987	54	0.81 (0.56–1.18)	0.274	34	1.10 (0.69–1.74)	0.701	76	0.82 (0.59–1.15)	0.258
POLE rs5744857
AA	197	71	Reference		107	Reference		88	Reference		90	Reference		45	Reference		133	Reference
AG/GG	190	79	1.17 (0.80–1.70)	0.430	140	1.36 (0.98–1.87)	0.061	99	1.17 (0.83–1.67)	0.368	120	1.39 (0.99–1.94)	0.059	61	1.40 (0.91–2.16)	0.129	158	1.24 (0.92–1.69)	0.164
REV3L rs462779
CC	100	36	Reference		74	Reference		44	Reference		66	Reference		25	Reference		85	Reference
CT/TT	278	113	1.14 (0.73–1.77)	0.568	173	0.84 (0.59–1.20)	0.341	143	1.18 (0.78–1.77)	0.436	143	0.78 (0.54–1.13)	0.188	81	1.15 (0.70–1.91)	0.579	205	0.87 (0.62–1.22)	0.416
RAD21 rs16889040
GG	290	109	Reference		183	Reference		138	Reference		154	Reference		81	Reference		211	Reference
GA/AA	106	42	1.06 (0.69–1.61)	0.797	64	0.96 (0.67–1.37)	0.814	50	0.99 (0.67–1.47)	0.975	56	1.00 (0.68–1.45)	0.981	25	0.84 (0.51–1.38)	0.491	81	1.05 (0.75–1.48)	0.779
RAD21 rs16888927
TT	293	109	Reference		182	Reference		138	Reference		153	Reference		81	Reference		210	Reference
TC/CC	103	42	1.10 (0.72–1.68)	0.652	67	1.05 (0.73–1.50)	0.794	50	1.04 (0.69–1.54)	0.862	59	1.10 (0.76–1.60)	0.622	26	0.90 (0.55–1.48)	0.689	83	1.13 (0.80–1.58)	0.489

Adjusted by age. The results with significant differences are Bold.

Discussion

Through a case–control study of 400 breast cancer patients and 400 healthy controls, we assessed the association between SNPs in genes related to DNA damage repair pathways and breast cancer risk. Breast cancer has a major impact on health of women, and its incidence has been increasing globally (Nolan et al., 2023; Trapani et al., 2022). Early detection of cancer and precise intervention strategies contribute to the reduction of cancer mortality (Siegel et al., 2022).

Identifying SNPs associated with cancer susceptibility can help enhance comprehension of the correlation between genetic variants and cancer risk, thereby facilitating the exploration of potential cancer biomarkers (Stracquadanio et al., 2016). Previous studies have showed that the occurrence and development of cancer were closely related to effected SNPs; however, the heterogeneity of SNPs among different races was always reported with inconsistent result and published data of SNPs related to DNA damage repair pathway were not all in agreement to date.

On this basis, we aimed to explore the relationship between Chinese female breast cancer risk and SNPs of genes in DNA damage repair pathway, which were from NHEJ and HR pathways, and we identified SNPs associated with an increased risk of breast cancer as well as some protective factors against the development of this disease in this study.

We assessed the association of SNPs in HR pathway genes with breast cancer susceptibility. ATR is a key kinase of DNA damage which response as a sensor of replication stress (Bradbury et al., 2020), our results revealed that ATR rs13091637 increased the risk of ER-negative, PR-negative, or HER-2-negative breast cancer. Previous studies have also reported the effect of ATR SNPs with breast cancer risk (Lin et al., 2013; Mehmood et al., 2020). ATR signaling facilitates the tolerance to DNA replication stress, and SNPs in ATR may consequently lead to dysfunction of genes and increased DNA damage. Simultaneously, normal cells may also be vulnerable to persistent replication stress (Yano and Shiotani, 2023).

In addition, we investigated ATM, a tumor suppressor involved in the repair of broken double strands of DNA (Stucci et al., 2021). ATM SNPs have been associated to an intermediate risk of breast cancer (Thompson et al., 2005). Li et al. have found that ATM rs611646 increased the risk of lung adenocarcinoma (Han et al., 2017), ATM rs611646 was also discovered to be a susceptibility locus for renal cell cancer by Shu et al. (Shu et al., 2018). However, we found ATM rs611646 was related to decreased risk of breast cancer in participants with menarche after 14 years old.

The inconsistency across the studies may be attributed to the impact of a later age at menarche (>14 years old) could shorten the total reproductive years of a woman, helping to reduce breast cancer risk (Daraei et al., 2019; Yang et al., 2022). And a longer interval between menarche and first pregnancy were associated with an increasing risk of breast cancer (Li et al., 2008; Liu et al., 2016). In addition, longer exposure to estrogen was also a detrimental factor in breast cancer (Collaborative Group on Hormonal Factors in Breast Cancer, 2012; Mishra et al., 2017; Yager and Davidson, 2006).

Furthermore, germline mutations in breast cancer susceptibility genes and SNPs, which contributed to breast cancer risk, were also affected by racial differences (Yap et al., 2019). Genetic diversity can lead to differences in the immune microenvironment and regulatory capacity of individuals. In addition, environmental differences across geographic regions and the lifestyles of various populations also contributed to variations in cancer risk (Martini et al., 2022; Yap, 2023).

We also analyzed PALB2, the partner and localizer of BRCA2 (Nepomuceno et al., 2021), which plays an important role in initiating HR and maintaining genome stability (Foo and Xia, 2022). Previous studies implied a significant relationship between PALB2 SNPs and breast cancer susceptibility, a finding that is consistent with the results of our study (Cecener et al., 2016; Wong et al., 2011; Wu et al., 2018). Our results indicated that PALB2 rs16940342 increased breast cancer risk in specific populations, including postmenopausal individuals, those with menarche occurring after the age of 14, and women who have given birth more than twice.

These populations were exposed to estrogen for long periods of time. Breast cancer risk factors include genetic and environmental factors (Cintolo-Gonzalez et al., 2017; Gail, 2015). Therefore, germline mutations in PALB2, a breast cancer susceptibility gene, may affect the HR pathway leading to genomic instability and tumorigenesis (Wu et al., 2020). In addition, early menarche, late menopause, and increased estrogen exposure were recognized as environmental risk factors for breast cancer (Yoshimura et al., 2022).

RAD21 is a crucial component of cohesin complex involved in the repair of DNA double-stranded breaks (Deng et al., 2022). Dysfunction of cohesin caused by RAD21 mutation can drive genomic instability, which eventually leads to cancer occurrence (Di Nardo et al., 2022). Our results revealed that RAD21 rs16888927 was linked to an increased risk of breast cancer; however, the reliability and consistency of these results were compromised by the limited sample size. It is necessary to expand the sample for further analyses.

Notably, Sehl et al. (2009) found that RAD21 rs16888927 was a risk factor for breast cancer. It has been discovered that RAD21 mutations or absence could impact the inhibitory effect of cohesin on PD-L1 and consequently increased immune evasion of tumor cells (Oreskovic et al., 2022). Sharaf et al. (2022) proposed a correlation between alterations in RAD21 and increased telomere length, which could promote the proliferation of cancer cells. These studies have revealed that RAD21 rs16888927 has the potential to be a marker for breast cancer.

The NHEJ pathway plays a crucial role in DNA damage repair in which Ku70 and Ku80 form dimers that bind to DNA ends, thereby assisting in the repair process (Inagawa et al., 2020). Our results implied that Ku70 rs132793 was associated with a reduced risk of breast cancer in participants who had menarche after 14 years old, whereas Ku80 SNPs had not been implicated in breast cancer risk. However, previous studies have found that Ku70 promoter region polymorphisms may be a breast cancer susceptibility factor, and the association between SNPs and breast cancer risk was stronger in female patients chronically exposed to estrogen (He et al., 2012; Willems et al., 2009).

The relationship between Ku80 SNPs and cancer risk has also been involved in numerous studies (Gomes et al., 2010; Hsu et al., 2009; Li et al., 2011). The discrepancies observed in these studies could be explained by variations in genetic profiles resulting from the impact of SNPs on gene coding. On a broader level, ethnic diversity and geographic influences may play a role in modulating the relationship between SNPs and the risk of breast cancer. In addition, individual characteristics of patients were possible to influence risk assessment such as estrogen expression and immune status.

Genome-wide association studies (GWAS) enable the study of common genetic variants associated with disease susceptibility and survival prognosis (Wang et al., 2019). The application of GWAS is important in cancer treatment, which has contributed to the identification of SNPs associated with increased cancer risk across diverse populations. However, the increase in cancer risk resulting from SNPs was limited (Fanfani et al., 2021b; Sud et al., 2017), which indicated that these risks were likely to be filtered by multiple hypothesis correction procedures in GWAS analyses (Pasaniuc and Price, 2017), especially when the minor allele frequency of locus is low, ultimately resulting in a portion of the cancer risk that cannot be reasonably explained (Fanfani et al., 2021a).

In contrast, our investigation into the correlation between gene SNPs involved in the DNA damage repair pathway and the susceptibility to breast cancer considered the impact of geographical factors and ethnicity, and our study cohort consisted of Chinese women from a homogeneous region. In addition, the individual characteristics of the participants were also considered and subgroup analyses were conducted with respect to their number of births and menopausal status.

This study also has some limitations: (1) breast cancer risk is closely related to estrogen stimulation, so more indicators related to this should be considered; (2) the study cohort had a relatively small sample size, highlighting the importance of expanding the research population for more robust findings; (3) all samples were from the Nanjing First Hospital, which is geographically homogeneous; and (4) the expression spectrum of HER-2 is beyond binary categorizations of positive and negative, and increasing numbers of studies are considering HER-2 expression status rather than simply classifying it as negative or positive (Anderson et al., 2023; Eiger et al., 2021).

Conclusion

PALB2 rs16940342, ATR rs13091637, ATM rs611646, and Ku70 rs132793 were associated with breast cancer risk.

Footnotes

Acknowledgments

We wish to express gratitude to all authors who participated in the research and completed the manuscript.

Authors' Contributions

Y.W. designed the main study, collected the primary data, and drafted the article. Y.S. validated and analyzed the data and further wrote the article. M.T. and X.L. checked all the analyses and refined the article. P.T., X.H., Q.J., and D.Y. provided much help in samples collection and analysis. T.X. and B.H. decided the main direction of the study and made the final revision of the article. All authors read and approved the final version of the article.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

This study was supported by the National Nature Science Foundation of China (Grant No. 81902143), Nanjing Medical and Health Scientific Research Project (Grant No. YKK21137), and the Jiangsu Provincial Medical Key Discipline Cultivation Unit (Grant No. JSDW202239).

Supplementary Material

Supplementary Table S1

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