Abstract
Breast cancer (BC) is the most common cancer among women and has high mortality rates. Early detection is supposed to be critical for the patient’s prognosis. In recent years, several studies have investigated global DNA methylation profiles and gene-specific DNA methylation in blood-based DNA to develop putative screening markers for cancer. However, most of the studies have not yet been validated. In our study, we analyzed the promoter methylation of RASSF1A and ATM in peripheral blood DNA of 229 sporadic patients and 151 healthy controls by the MassARRAY EpiTYPER assay. There were no significant differences in DNA methylation levels of RASSF1A and ATM between the sporadic BC cases and the healthy controls. Furthermore, we performed the Infinium HumanMethylation450 BeadChip (450K) array analysis using 48 sporadic BC cases and 48 healthy controls (cases and controls are the same from those of the MassARRAY EpiTYPER assay) and made a comparison with the published data. No significant differences were presented in DNA methylation levels of RASSF1A and ATM between the sporadic BC cases and the healthy controls. So far, the evidence for powerful blood-based methylation markers is still limited and the identified markers need to be further validated.
Keywords: breast cancer, DNA methylation, RASSF1A, ATM
1. Introduction
Breast cancer (BC) is one of the most common cancers in women worldwide [1,2]. The early detection of breast cancer plays an important role in successful treatment and outcome. To date, mammography is still the main screening method for breast cancer early detection. However, the limitations of this method are noticed as tumors can only be partially identified in women (mostly young women) with dense breasts [3,4]. Therefore, the identification of new reliable biomarkers for the screening and diagnosis of breast cancer is urgently needed.
Epigenetic events are critical factors in the development of human cancers [5,6,7,8]. Aberrant methylation in the promoter regions of tumor suppressor genes is related to carcinogenesis through transcriptional silencing of gene expression [9], leading to the initiation and progression of cancer [10,11]. Global hypomethylation and gene-specific hypermethylation were shown to be associated with malignancy [12,13,14]. Several studies showed that these epigenetic changes were early events of a variety of cancers including breast [15,16,17], lung [18], and colon cancer [19], and all of them could be recognized as common hallmarks in different kinds of tumors [20]. Similar alterations also existed in blood-derived DNA, suggesting that blood-based DNA methylation could reveal new biomarkers for BC screening or diagnosis [21,22]. A major advantage of blood-based DNA methylation is the easy accessibility of blood samples to investigate DNA methylation in cancer patients.
Recently, studies have been focused on two tumor suppressor genes, RAS-association domain family member 1A (RASSF1A) and ataxia-telangiectasia mutated gene (ATM). RASSF1A takes part in apoptosis induction, proliferation regulation, and microtubules stabilization [23]. Aberrant hypermethylation of RASSF1A has been demonstrated in various solid tumors, including lung, prostate, ovary, and breast cancer tissue samples [24,25,26,27]. Furthermore, a previous study has demonstrated no RASSF1A promoter hypermethylation in peripheral blood from normal blood donors, which suggested the potential of RASSF1A as a biomarker for cancers [28]. ATM plays a critical role in repairing DNA double-strand breaks and is involved in numerous processes including recognition of damaged DNA, recruitment of repair proteins, signaling to cell cycle checkpoints, transcriptional regulation, and activation of apoptosis [29]. Hypermethylation of the ATM promoter has been shown in gastric lymphoma, glioma, colonic cancer, adenoma, and breast cancer tissue samples [30,31,32,33]. Moreover, we summarized recent studies which investigated promoter methylation changes of RASSF1A and ATM in peripheral blood (plasma, serum, or whole blood) from BC patients and healthy controls (see Supplementary Table S1). To our surprise, the results of these studies were inconsistent. The promoter methylation of RASSF1A did not show a significant difference between BC patients and healthy controls in two quantitative studies [34,35]. It is worth noting that the sample sizes analyzed in these two studies were rather low. For ATM, two studies reported its hypermethylation in BC patients, but the CpG sites they investigated were located in the gene body or intragenic region instead of the promoter [36,37]. Altogether, blood-based DNA methylation of the RASSF1A and ATM promoter remains unclear in BC patients. However, these findings reveal the potential of RASSF1A and ATM promoter hypermethylation as novel biomarkers for cancer early detection.
Therefore, in this study, we aimed to conduct a case-control study with a large sample size to investigate RASSF1A and ATM promoter methylation in BC patients and healthy controls. A quantitative method, the MassARRAY EpiTYPER assay, was applied by using peripheral blood DNA in order to find useful blood-based biomarkers for BC early detection.
In addition, we compared previously published results of blood-based DNA methylation of specific genes found to be associated with breast cancer with our results by Infinium HumanMethylation450 BeadChip (450K) array analysis of sporadic breast cancer cases and healthy controls.
2. Results
2.1. Promoter Methylation Levels of RASSF1A and ATM in BC Patients and Healthy Controls and Its Correlation to Clinical Characteristics
In order to investigate the promoter methylation levels of RASSF1A and ATM, we performed the MassARRAY EpiTYPER assay by using peripheral blood DNA from BC patients and healthy controls. Our results showed that the promoter methylation levels of RASSF1A and ATM were quite low in both BC patients and healthy controls. In the amplicon of RASSF1A, 17 CpG sites were measured and the average methylation levels of all these CpG sites were 0.037 and 0.042 in the BC patients and the healthy controls, respectively. Compared to the healthy controls, no significant difference was represented in the methylation levels of the BC patients among all these 17 CpG sites (see Table 1).
Table 1.
Comparison of RASSF1A DNA methylation between the BC patients and the healthy controls in peripheral blood.
| CpG Site | Case, n | Control, n | BC Cases Median (IQR) | Controls Median (IQR) | p-Value * | p-Value ** |
|---|---|---|---|---|---|---|
| RASSF1A_CpG_1 | 222 | 144 | 0.00 (0.00–0.00) | 0.00 (0.00–0.00) | 0.03 | 0.31 |
| RASSF1A_CpG_8 | 216 | 134 | 0.00 (0.00–0.02) | 0.00 (0.00–0.02) | 1.00 | 0.76 |
| RASSF1A_CpG_9 | 223 | 141 | 0.01 (0.00–0.02) | 0.01 (0.00–0.03) | 1.00 | 0.78 |
| RASSF1A_CpG_11,12 | 187 | 121 | 0.13 (0.11–0.16) | 0.14 (0.11–0.17) | 1.00 | 0.38 |
| RASSF1A_CpG_13 | 222 | 141 | 0.04 (0.00–0.08) | 0.04 (0.00–0.08) | 1.00 | 0.90 |
| RASSF1A_CpG_14,15 | 187 | 121 | 0.13 (0.11–0.16) | 0.14 (0.11–0.17) | 1.00 | 0.35 |
| RASSF1A_CpG_16 | 226 | 146 | 0.02 (0.00–0.03) | 0.02 (0.02–0.03) | 1.00 | 0.15 |
| RASSF1A_CpG_19 | 216 | 134 | 0.00 (0.00–0.02) | 0.00 (0.00–0.02) | 1.00 | 0.09 |
| RASSF1A_CpG_20 | 216 | 134 | 0.00 (0.00–0.02) | 0.00 (0.00–0.02) | 1.00 | 0.34 |
| RASSF1A_CpG_21,22 | 226 | 145 | 0.05 (0.04–0.06) | 0.05 (0.03–0.07) | 0.62 | 0.64 |
| RASSF1A_CpG_23 | 226 | 146 | 0.00 (0.00–0.00) | 0.00 (0.00–0.00) | 0.28 | 0.70 |
| RASSF1A_CpG_24 | 226 | 145 | 0.03 (0.02–0.05) | 0.03 (0.02–0.07) | 1.00 | 0.79 |
| RASSF1A_CpG_25 | 223 | 141 | 0.01 (0.00–0.02) | 0.01 (0.00–0.03) | 1.00 | 0.31 |
| RASSF1A_CpG_26 | 226 | 146 | 0.04 (0.03–0.05) | 0.04 (0.03–0.05) | 1.00 | 0.76 |
| MEAN | 226 | 146 | 0.037 (0.029–0.048) | 0.042 (0.032–0.054) | 0.25 | 0.78 |
Abbreviations: IQR: interquartile range. * p-value for the difference between controls and patients was analyzed by Mann-Whitney U test and was adjusted by Bonferroni-Holm method, α = 0.00333. ** p-value for the difference between controls and patients was analyzed by logistic regression and was adjusted by age, α = 0.05.
As for ATM, 30 CpG sites were analyzed in its amplicon. The average methylation levels of all investigated CpG sites of ATM were 0.047 and 0.048 in the BC patients and the healthy controls, respectively. No visible difference was detected in the methylation levels of these CpG sites between the BC cases and the healthy controls (see Table 2). The CpG sites of RASSF1A and ATM analyzed in this study are shown in Supplementary Figure S1.
Table 2.
Comparison of ATM DNA methylation between the BC patients and the healthy controls in peripheral blood.
| CpG Site | Case, n | Control, n | BC Cases Median (IQR) | Controls Median (IQR) | p-Value * | p-Value ** |
|---|---|---|---|---|---|---|
| ATM_CpG_1 | 222 | 146 | 0.02 (0.01–0.03) | 0.02 (0.01–0.02) | 1.00 | 0.95 |
| ATM_CpG_2,3,4,5 | 142 | 95 | 0.07 (0.06–0.10) | 0.07 (0.06–0.09) | 1.00 | 0.91 |
| ATM_CpG_6 | 223 | 146 | 0.00 (0.00–0.00) | 0.00 (0.00–0.00) | 1.00 | 0.37 |
| ATM_CpG_7,8 | 223 | 146 | 0.05 (0.03–0.06) | 0.04 (0.03–0.06) | 1.00 | 0.60 |
| ATM_CpG_10,11 | 221 | 145 | 0.11 (0.10–0.13) | 0.12 (0.10–0.13) | 1.00 | 0.95 |
| ATM_CpG_12 | 223 | 146 | 0.00 (0.00–0.00) | 0.00 (0.00–0.00) | 1.00 | 0.94 |
| ATM_CpG_13,14 | 223 | 146 | 0.03 (0.02–0.04) | 0.03 (0.02–0.04) | 1.00 | 0.43 |
| ATM_CpG_17 | 219 | 145 | 0.00 (0.00–0.04) | 0.01 (0.00–0.04) | 1.00 | 0.73 |
| ATM_CpG_18,19 | 223 | 146 | 0.13 (0.12–0.16) | 0.14 (0.12–0.18) | 0.22 | 0.18 |
| ATM_CpG_20,21 | 223 | 146 | 0.02 (0.02–0.03) | 0.02 (0.02–0.03) | 1.00 | 0.36 |
| ATM_CpG_26 | 219 | 146 | 0.06 (0.04–0.09) | 0.07 (0.05–0.09) | 1.00 | 0.21 |
| ATM_CpG_27 | 223 | 146 | 0.11 (0.10–0.12) | 0.11 (0.10–0.12) | 1.00 | 0.66 |
| ATM_CpG_28 | 222 | 146 | 0.02 (0.02–0.03) | 0.02 (0.02–0.03) | 1.00 | 0.75 |
| ATM_CpG_29 | 223 | 146 | 0.03 (0.02–0.03) | 0.03 (0.02–0.03) | 1.00 | 0.68 |
| ATM_CpG_32 | 223 | 145 | 0.00 (0.00–0.00) | 0.00 (0.00–0.00) | 1.00 | 0.53 |
| ATM_CpG_33 | 223 | 146 | 0.00 (0.00–0.00) | 0.00 (0.00–0.00) | 1.00 | 0.15 |
| ATM_CpG_34 | 223 | 146 | 0.13 (0.10–0.16) | 0.14 (0.10–0.18) | 1.00 | 0.70 |
| ATM_CpG_35 | 219 | 145 | 0.00 (0.00–0.04) | 0.01 (0.00–0.04) | 1.00 | 0.78 |
| ATM_CpG_36 | 223 | 145 | 0.00 (0.00–0.00) | 0.00 (0.00–0.00) | 1.00 | 0.14 |
| ATM_CpG_37 | 223 | 146 | 0.13 (0.10–0.16) | 0.14 (0.10–0.18) | 1.00 | 0.16 |
| ATM_CpG_38 | 192 | 135 | 0.00 (0.00–0.01) | 0.00 (0.00–0.01) | 1.00 | 0.95 |
| ATM_CpG_39 | 223 | 146 | 0.02 (0.01–0.04) | 0.02 (0.01–0.03) | 1.00 | 0.91 |
| MEAN | 223 | 146 | 0.047 (0.042–0.055) | 0.048 (0.042–0.056) | 1.00 | 0.37 |
Abbreviations: IQR: interquartile range. * p-value for the difference between controls and patients was analyzed by Mann-Whitney U test and was adjusted by Bonferroni-Holm method, α = 0.00227. ** p-value for the difference between controls and patients was analyzed by logistic regression and was adjusted by age, α = 0.05.
To further explore the association between the promoter methylation of RASSF1A/ATM and the clinical characteristics of the BC patients, statistical analyses were performed. All the BC patients were classified into different subgroups according to their clinical properties (see Table 3). To our surprise, no significant correlation was represented between the methylation levels of all the CpG sites in RASSF1A or ATM amplicons and the clinical characteristics of the BC patients (see Supplementary Tables S2 and S3).
Table 3.
Characteristics of the sporadic BC patients.
| Characteristics | BC Patients Number (%) |
|---|---|
| Tumor, lymph node and metastasis (TNM) Stage | |
| stage 0 | 1 (0.4%) |
| stage I | 69 (30.1%) |
| stage II | 72 (31.4%) |
| stage III | 15 (6.6%) |
| stage IV | 4 (1.7%) |
| neoadjuvant chemotherapy * | 50 (21.8%) |
| unknown | 18 (7.9%) |
| Type of BC | |
| Ductal | 179 (78.2%) |
| Lobular | 13 (5.7%) |
| Ductal-Lobular | 3 (1.3%) |
| ductal carcinoma in situ (DCIS) | 4 (1.7%) |
| Others | 10 (4.4%) |
| unknown | 24 (10.5%) |
| Estrogen receptor (ER) Status a | |
| negative | 21 (9.2%) |
| positive | 160 (69.9%) |
| unknown | 48 (21.0%) |
| Progesterone receptor (PR) Status a | |
| negative | 36 (15.7%) |
| positive | 145 (63.3%) |
| unknown | 48 (21.0%) |
| Human epidermal growth factor receptor 2 (HER2) Status b | |
| negative | 165 (72.1%) |
| positive | 16 (7.0%) |
| unknown | 48 (21.0%) |
a Immunoreactive score (IRS): ER/PR negative: IRS 0–2; ER/PR positive: IRS 3–12. b HER2 negative: IHC-score 0–1; HER2 positive: IHC-score 3; If IHC-score = 2, FISH/CISH was further analyzed, HER2 is recognized as positive if it is amplified. * Patients were treated with neoadjuvant chemotherapy, no stage is given here.
Taken together, these results demonstrated that the promoter methylation levels of RASSF1A and ATM included in our study were not statistically different between the BC patients and the healthy controls.
2.2. Comparison of the Results from This Study with the Results of Infinium HumanMethylation450 BeadChip Array and with Literature
To better interpret the results of the Sequenom MassARRAY EpiTYPER assay, we also performed an epigenome-wide Infinium HumanMethylation450 BeadChip array (450K array). In line with the MassARRAY data, the methylation levels of all the investigated CpG sites of RASSF1A and ATM were rather low. No significant difference was revealed in the methylation levels of RASSF1A and ATM between the BC patients and the healthy controls. Interestingly, we identified four CpG sites of RASSF1A and 13 CpG sites of ATM which were identical in both the Sequenom MassArray EpiTYPER assay and the 450K array. These data confirmed the findings of our MassARRAY analysis (see Table 4).
Table 4.
Comparison of methylation levels of peripheral blood DNA between different analytical methods.
| Gene | 450K Results | Sequenom MassARRAY EpiTYPER Assay | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| CpG | Cases No./CTL No. | BC Cases Mean ± SD | CTL Mean ± SD |
p-Value a | CpG | Cases No./CTL No. | BC Cases Median (IQR) | CTL Median (IQR) | p-Value b | |
| RASSF1A | cg 12966367 | 48/48 | 0.029 ± 0.005 | 0.027 ± 0.004 | 0.19 | CpG_1 | 229/151 | 0.00 (0.00–0.00) | 0.00 (0.00–0.00) | 0.03 |
| cg25486143 | 48/48 | 0.014 ± 0.002 | 0.014 ± 0.002 | 0.80 | CpG_14 | 229/151 | 0.13 (0.11–0.16) | 0.14 (0.11–0.17) | 1.00 | |
| cg06172942 | 48/48 | 0.038 ± 0.007 | 0.038 ± 0.011 | 0.91 | CpG_15 | 229/151 | 0.13 (0.11–0.16) | 0.14 (0.11–0.17) | 1.00 | |
| cg 03297783 | 48/48 | 0.015 ± 0.002 | 0.016 ± 0.002 | 0.35 | CpG_19 | 229/151 | 0.00 (0.00–0.02) | 0.00 (0.00–0.02) | 1.00 | |
| cg08047457 * | 48/48 | 0.030 ± 0.004 | 0.030 ± 0.004 | 0.87 | CpG_2 | 229/151 | — | — | — | |
| cg25747192 * | 48/48 | 0.043 ± 0.007 | 0.043 ± 0.008 | 0.99 | CpG_3 | 229/151 | — | — | — | |
| cg21554552 * | 48/48 | 0.031 ± 0.005 | 0.030 ± 0.005 | 0.42 | CpG_4 | 229/151 | — | — | — | |
| cg27569446 * | 48/48 | 0.012 ± 0.002 | 0.011 ± 0.002 | 0.65 | CpG_5 | 229/151 | — | — | — | |
| cg04540383 * | 48/48 | 0.032 ± 0.004 | 0.033 ± 0.005 | 0.92 | CpG_18 | 229/151 | — | — | — | |
| Mean | 48/48 | 0.204 ± 0.005 | 0.205 ± 0.005 | 0.56 | Mean | 229/151 | 0.037 (0.029–0.048) | 0.042 (0.032–0.054) | 0.25 | |
| ATM | cg19288979 | 48/48 | 0.075 ± 0.006 | 0.076 ± 0.007 | 0.95 | CpG_1 | 229/151 | 0.02 (0.01–0.03) | 0.02 (0.01–0.02) | 1.00 |
| cg10610482 | 48/48 | 0.040 ± 0.005 | 0.037 ± 0.005 | 0.31 | CpG_2 | 229/151 | 0.07 (0.06–0.10) | 0.07 (0.06–0.09) | 1.00 | |
| cg12848864 | 48/48 | 0.051 ± 0.007 | 0.048 ± 0.007 | 0.38 | CpG_4 | 229/151 | 0.07 (0.06–0.10) | 0.07 (0.06–0.09) | 1.00 | |
| cg03165700 | 48/48 | 0.047 ± 0.007 | 0.044 ± 0.005 | 0.16 | CpG_5 | 229/151 | 0.07 (0.06–0.10) | 0.07 (0.06–0.09) | 1.00 | |
| cg15504467 | 48/48 | 0.027 ± 0.003 | 0.026 ± 0.004 | 0.36 | CpG_6 | 229/151 | 0.00 (0.00–0.00) | 0.00 (0.00–0.00) | 1.00 | |
| cg05033322 | 48/48 | 0.028 ± 0.004 | 0.027 ± 0.003 | 0.82 | CpG_7 | 229/151 | 0.05 (0.03–0.06) | 0.04 (0.03–0.06) | 1.00 | |
| cg16693212 | 48/48 | 0.033 ± 0.004 | 0.031 ± 0.004 | 0.29 | CpG_8 | 229/151 | 0.05 (0.03–0.06) | 0.04 (0.03–0.06) | 1.00 | |
| cg15370815 | 48/48 | 0.074 ± 0.007 | 0.072 ± 0.007 | 0.60 | CpG_10 | 229/151 | 0.11 (0.10–0.13) | 0.12 (0.10–0.13) | 1.00 | |
| cg16788234 | 48/48 | 0.052 ± 0.008 | 0.051 ± 0.007 | 0.82 | CpG_11 | 229/151 | 0.11 (0.10–0.13) | 0.12 (0.10–0.13) | 1.00 | |
| cg24030675 | 48/48 | 0.013 ± 0.001 | 0.013 ± 0.001 | 0.58 | CpG_17 | 229/151 | 0.00 (0.00–0.04) | 0.01 (0.00–0.04) | 1.00 | |
| cg06053805 | 48/48 | 0.012 ± 0.001 | 0.011 ± 0.003 | 0.14 | CpG_18 | 229/151 | 0.13 (0.12–0.16) | 0.14 (0.12–0.18) | 0.22 | |
| cg06750635 | 48/48 | 0.012 ± 0.001 | 0.011 ± 0.001 | 0.69 | CpG_19 | 229/151 | 0.13 (0.12–0.16) | 0.14 (0.12–0.18) | 0.22 | |
| cg25400013 | 48/48 | 0.016 ± 0.002 | 0.016 ± 0.001 | 0.94 | CpG_21 | 229/151 | 0.02 (0.02–0.03) | 0.02 (0.02–0.03) | 1.00 | |
| cg20342375 * | 48/48 | 0.021 ± 0.003 | 0.020 ± 0.002 | 0.085 | CpG_9 | 229/151 | — | — | — | |
| cg22837512 * | 48/48 | 0.023 ± 0.003 | 0.023 ± 0.002 | 0.99 | CpG_15 | 229/151 | — | — | — | |
| cg18391757 * | 48/48 | 0.025 ± 0.004 | 0.025 ± 0.004 | 0.72 | CpG_22 | 229/151 | — | — | — | |
| Mean | 48/48 | 0.236 ± 0.002 | 0.235 ± 0.002 | 0.79 | Mean | 229/151 | 0.047 (0.042–0.055) | 0.048 (0.042–0.056) | 1.00 | |
a p-value was ajusted by age, batch, cell type and multiple test. b p-value for the difference between breast cancer patients and healthy controls was analyzed by Mann-Whitney U test and was adjusted by Bonferroni-Holm method, α = 0.00333 and α = 0.00227 for RASSF1A and ATM respectively. * CpG sites were included in RASSF1A and ATM amplicons but were not investigated by Sequenom MassARRAY EpiTYPER assay in this study. Abbreviations: CTL, control; BC, breast cancer.
To understand the current research status on gene-specific DNA methylation in blood-based DNA as a screening biomarker for breast cancer early detection, we reviewed recent studies which investigated blood-based DNA methylation of BRCA1, APC, RARB, ESR1, CDH1, SYK, TIMP3, GSTP1, DAPK, and IGF2 between BC patients and healthy controls [38]. Interestingly, the results of blood DNA methylation levels of BRCA1, APC, RARB, ESR1, TIMP3, and GSTP1 were inconsistent among the previous studies, but a higher frequency of methylated DAPK in peripheral DNA was reported in BC cases compared to healthy controls in several studies (see Supplementary Table S4) [36,37,39,40]. Moreover, the methylation levels of CDH1, IGF2, and SYK were not significantly different in peripheral blood DNA between BC cases and healthy controls according to the published data [34,35,41,42,43,44].
Next, we investigated the methylation levels of these genes by a 450K array from our own group. Compared to the healthy controls, the methylation levels did not show a significant difference in most of the CpG sites of these genes in the BC cases, except for a few CpG sites including BRCA1 (cg13782816), APC (cg01240931 and cg14511739), ESR1 (cg25565730), CDH1 (cg26508465), and GSTP1 (cg06841499) (see Supplementary Table S4).
3. Discussion
In this work, we conducted a large cohort case-control study to investigate the promoter methylation changes of two tumor suppressor genes (RASSF1A and ATM) in peripheral blood DNA as potential epigenetic markers for breast cancer risk and early detection. To our surprise, no significant differences in the methylation level of RASSF1A and ATM in peripheral blood DNA were revealed between BC cases and healthy controls.
RASSF1A methylation in BC patients has been analyzed by several groups using either blood serum or blood plasma samples [39,40,45,46,47,48,49,50]. Jo-Heon Kim et al. observed significantly higher methylation frequencies of RASSF1A in ductal carcinoma in situ (DCIS) or invasive ductal carcinoma (IDC) than in control subjects, but the CpG sites they investigated in the RASSF1A promoter region were different from ours [49]. In contrast, Zmetakova et al., evaluated DNA methylation profiles of the RASSF1A promoter by pyrosequencing in invasive breast cancer. They demonstrated no significant difference in peripheral blood DNA between BC cases and healthy controls, which was in line with our findings [35]. Moreover, another study by Brooks et al., also showed no significant difference in RASSF1A promoter methylation between BC cases and controls, although they used serum instead of whole blood samples. Interestingly, the CpG sites analyzed in their study partially overlapped with some CpG sites in our RASSF1A amplicon [51].
For ATM, we demonstrated that the promoter methylation level in peripheral blood DNA of BC patients was not significantly different from that of healthy controls. Consistent with this, Flanagan et al. detected no significant difference in methylation of the ATM promoter CpG islands in white blood cell DNA between BC cases and healthy controls [36].
In fact, it is difficult to compare the results between our study and the published data, where different techniques for methylation analysis have been used and different CpG sites have been investigated. Moreover, depending on the technique used for quantification, methylation levels can differ, even for the same CpG site [52]. The promoter region is located at around 100–1000 base pairs before the transcription start sites. A length of around 100–500 base pairs can be amplified by PCR, which means that the investigation cannot cover the entire promoter region. Currently, there is no standard for blood DNA methylation analysis. The exact quantity of methylated CpG sites as the biomarker for breast cancer risk remains unclear.
In our study, we chose whole blood DNA to investigate the methylation levels. Recently, the pivotal significance of circulating tumor cells (CTCs) has been realized for breast cancer patients, even in patients without metastases. It is possible that the whole blood was mixed by CTCs, further affecting the analysis of RASSF1A or ATM promoter methylation. In fact, peripheral blood samples from epidemiological studies always comprise mixed cell populations, but it is not feasible or practical to fractionate cell populations in an epidemiological study setting [53]. At present, many studies use cell-free DNA (cfDNA) to investigate DNA methylation, but its low concentration and small fragments may disrupt the detection of blood-based DNA methylation [54,55,56]. Even if the pooling method can improve the concentration of cfDNA, the possible bias may still occur. In addition, the origin of cfDNA is still uncertain and it requires more evidence to elucidate the mechanism of cfDNA release [57].
Radpour et al. demonstrated that heterogeneity of methylation changes exists in carcinogenesis, but no single gene has been shown to be methylated in all types of breast cancer [58]. Therefore, a panel of genes should be considered as biomarkers for breast cancer screening. Our 450K data analyzed additional ten genes to screen for potential methylation CpG sites as biomarkers for breast cancer early detection. The methylation levels did not show a significant difference in most of the CpG sites of these genes in BC cases.
With the analysis of RASSF1A and ATM using the MassARRAY EpiTYPER assay, we could confirm the results of our 450K methylation data for these two genes. However, the methylation levels detected in our samples were quite low. We identified that the average methylation levels of RASSF1A and ATM were at around 0.04 and 0.05, respectively. In accordance with this, a study by Cho et al., reported that the methylation level of RASSF1A was below 4% [34]. The MassARRAY system could detect the methylation level as low as 5% [59]. For some of the analyzed CpG sites, no detectable quantitative value could be gained because the results were below this detection limit. Future studies applying digital PCR techniques in next-generation sequencing (PCR/NGS)-based analysis may improve the detection limit.
Another limitation of the 450K results was the limited sample size, which included only 48 cases and 48 controls. Thus, larger multicenter prospective study cohorts are needed to validate these results.
In conclusion, the promoter CpG methylation status of RASSF1A and ATM in peripheral blood included in our study was unable to distinguish between BC cases and healthy controls. Further prospective studies should be carried out to evaluate whether RASSF1A or ATM promoter methylation could be suitable biomarkers for breast cancer early detection [39].
4. Materials and Methods
4.1. Study Population
This study was approved by the Ethics Committee of University Hospital in Heidelberg (S-039/2008, 27 April 2009; S-175/2010, 26 May 2010). All samples of the BC cases and the healthy controls were obtained from centers in southwest Germany. All the enrolled patients and the healthy controls were Caucasian and were given written informed consent. Peripheral whole-blood samples from the BC patients were successively collected before therapeutic treatments at the University Hospital of Heidelberg. Clinical characteristics of the BC patients were defined according to the American Joint Committee on Cancer staging manual [60]. Detailed characteristics of the sporadic BC cases were described in Table 3. In the group of healthy controls, peripheral whole-blood samples were consecutively collected from blood donors at the University Hospital of Heidelberg. Donors approved the use of their blood samples for research purposes. All the donors were healthy when donating blood and none of them had a family history of BC. Blood samples were collected between 2011 and 2014 and a total of 229 sporadic BC patients and 151 healthy controls were randomly selected for this study (see Table 5).
Table 5.
Sample Information.
| Gene | Sample Type | Group | Number | Age (y, Mean ± SD) |
|---|---|---|---|---|
| RASSF1A | Peripheral blood DNA | Sporadic BC | 229 | 48.37 ± 7.08 |
| Controls | 151 | 43.76 ± 14.49 | ||
| ATM | Peripheral blood DNA | Sporadic BC | 229 | 48.37 ± 7.08 |
| Controls | 151 | 43.76 ± 14.49 |
4.2. DNA Isolation and Bisulfite Conversion
DNA was isolated from 200 µL aliquots of whole blood using the QIAamp DNA Blood Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s recommendations. NanoDrop ND-1000 UV/Vis-Spectralphotometer 3.3 (peqLab, Erlangen, Germany) was used to measure DNA quality and quantity. DNA bisulfite treatment was carried out using the EZ-96 DNA methylation Gold kit (Zymo Research Corporation, Orange, CA, USA) as described by the manufacturer.
4.3. Primer Design and PCR Amplification
The PCR primers for RASSF1A and ATM amplicon sequences (see Table 6 and Table 7) were designed with the online tool “epidesigner” (http://www.epidesigner.com/start3.html). PCR was performed in a final reaction volume of 6 µL which included bisulfite-treated DNA (10 ng/µL), CoralLoad Buffer (10×; Qiagen, Valencia, CA, USA), forward and reverse primers (1 µM of each; Sigma, Darmstadt, Germany), dNTPs (10 mM), and HotStar Taq DNA polymerase (5 U/µL; Qiagen, Valencia, CA, USA). The touch-down PCR profile was 5 min of activation at 95 °C, 30 s of denaturation at 94 °C, and 30 s of annealing at a temperature reduced from 59 °C to 53 °C (every 2 °C), followed by a final extension at 72 °C for 1 min. After a maintenance of 5 min at 72 °C, the reaction ended up at 4 °C. PCR products were electrophoresed on 1% agarose gels and evaluated under ultraviolet light.
Table 6.
Bisulfite-specific primers for the target amplicons.
| Target | Primer | Sequence (5′-3′) |
|---|---|---|
| RASSF1A | sense | aggaagagagGTAATGGAAATTTGGGTGTAGGGAT |
| antisense | cagtaatacgactcactatagggagaaggctCTAACAACCCAAAATAACAAAACCA | |
| ATM | sense | aggaagagagAGGGAAAATTTTTGGTTTTAAAGGT |
| antisense | cagtaatacgactcactatagggagaaggctCCATATCCACCAATAACCAAC |
Table 7.
Sequences of the target amplicons.
| Amplicon | Sequence (5′-3′) |
|---|---|
| RASSF1A | GCAATGGAAACCTGGGTGCAGGGACTGTGGGGCCCGAAGGCGGGGCTGGGCGCGCTCTCGCAGAGCCCCCCCCGCCTTGCCCTTCCTTCCCTCCTTCGTCCCCTCCTCACACCCCACCCCGGACGGCCACAACGACGGCGACCGCAAAGCACCACGCGGAGATACCCGTGTTTCTGGAGGCCAGCTTTACTGTGCTAGAGGAAGAGGGTCCCCACATCCGGCCCTGGCCCTCCTGGTCCGGTTTGCTGAAGCAACACACTTGGCCTACCCACTGGGTGGGGCAGGAAGTCTCGAGCCTTCACTTGGGGTGAGGAGGAGGGAGATCGGTCAGCAGCTTTACCGCCCGCTCTGCTCTCCACTGCGGAGACTGGGGCTCCGGCAGAGGCTGGACCGTGATCTTGAGGTTCAGGGGTGCATTCTGGGTGGATTCCCTTGGCATGGGTGGTCGGCCCTCAGCAACTGCAGCCCTCATTTGGCTCTGTCACCCTGGGCTGCCAG |
| ATM | AGGGAAAACCTTTGGCCTCAAAGGTCCTTCTGTCCAGCATAGCCGGGTCCAATAACCCTCCATCCCGCGTCCGCGCTTACCCAATACAAGCCGGGCTACGTCCGAGGGTAACAACATGATCAAAACCACAGCAGGAACCACAATAAGGAACAAGACTCAGGTTAAAGCAAACACAGCGACAGCTCCTGCGCCGCATCTCCTGGTTCCAGTGGCGGCACTGAACTCGCGGCAATTTGTCCCGCCTCTTTCGCTTCACGGCAGCCAATCGCTTCCGCCAGAGAAAGAAAGGCGCCGAAATGAAACCCGCCTCCGTTCGCCTTCGGAACTGTCGTCACTTCCGTCCTCAGACTTGGAGGGGCGGGGATGAGGAGGGCGGGGAGGACGACGAGGGCGAAGAGGGTGGGTGAGAGCCCCGGAGCCCGAGCCGAAGGGCGAGCCGCAAACGCTAAGTCGCTGGCCATTGGTGGACATGG |
4.4. Methylation Analysis
For methylation analysis, the Sequenom MassARRAY EpiTYPER assay was applied as described previously [61]. The PCR amplicons were conducted subsequently according to the protocol of the Sequenom EpiTYPER Assay and cleaned by Resin. A nanodispenser was used to transfer the products to a 384 SpectroCHIP (SEQUENOM, San Diego, CA, USA). The chips were read by a Sequenom Mass Spectrometer system (SEQUENOM, San Diego, CA, USA). Data was gathered by SpectroACQUIRE v3.3.1.3 software (SEQUENOM, San Diego, CA, USA) and visualized with MassArray EpiTYPER v1.0 software (SEQUENOM, San Diego, CA, USA). Results were depicted as “beta” values (β) between 0 and 1.
4.5. 450K Methylation Study
Epigenome-wide DNA methylation profiling on 96 age matched blood DNAs from 48 sporadic BC cases and 48 healthy controls was performed by applying Infinium HumanMethylation450 BeadChip (450K) as described before [61]. In brief, DNA was extracted from whole blood samples. Then, the DNA samples were bisulfate converted, purified, and applied to the BeadChips (Illumina, San Diego, CA, USA). Image processing and data extraction were performed following Illumina’s instructions. Further details of the 450K array analysis were given in Tang et al. [61].
4.6. Statistical Analysis
SPSS statistics 24.0 (IBM, NY, USA) was used for statistical analyses of the data. Normality of distribution was evaluated by the Kolmogorov-Smirnoff test. Non-normally distributed data was analyzed by the nonparametric Mann-Whitney U or Kruskal-Wallis H test. All tests were performed two-tailed at the significance level p < 0.05.
Acknowledgments
We thank Sabine Serick and Yanxiang Jiang for their technical support. This study was supported by the German Cancer Research Center (DKFZ) and University Women’s Clinic, Heidelberg. Xue Cao was financed by the China Scholarship Council (CSC).
Abbreviations
| BC | breast cancer |
| RASSF1A | RAS-association domain family member 1A gene |
| ATM | ataxia-telangiectasia mutated gene |
| 450K | Infinium HumanMethylation450 BeadChip |
| IQR | interquartile range |
| DCIS | ductal carcinoma in situ |
| IDC | invasive ductal carcinoma |
| cfDNA | cell free DNA |
| CTCs | circulating tumor cells |
Supplementary Materials
Supplementary materials can be found at http://www.mdpi.com/1422-0067/19/3/900/s1.
Author Contributions
Xue Cao, Qiuqiong Tang, and Barbara Burwinkel conceived and designed the experiments; Xue Cao, Qiuqiong Tang, and Katarina Cuk performed the experiments; Xue Cao and Tim Holland-Letz analyzed the data; Sarah Schott, Jörg Heil, Michael Golatta, Christof Sohn, and Andreas Schneeweiss contributed materials; Xue Cao, Michael Golatta, and Barbara Burwinkel wrote the paper.
Conflicts of Interest
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
References
- 1.Ferlay J., Soerjomataram I., Dikshit R., Eser S., Mathers C., Rebelo M., Parkin D.M., Forman D., Bray F. Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer. 2015;136:E359–E386. doi: 10.1002/ijc.29210. [DOI] [PubMed] [Google Scholar]
- 2.Siegel R., Naishadham D., Jemal A. Cancer statistics, 2013. CA Cancer J. Clin. 2013;63:11–30. doi: 10.3322/caac.21166. [DOI] [PubMed] [Google Scholar]
- 3.Elmore J.G., Barton M.B., Moceri V.M., Polk S., Arena P.J., Fletcher S.W. Ten-year risk of false positive screening mammograms and clinical breast examinations. N. Engl. J. Med. 1998;338:1089–1096. doi: 10.1056/NEJM199804163381601. [DOI] [PubMed] [Google Scholar]
- 4.Alagaratnam T.T., Wong J. Limitations of mammography in Chinese females. Clin. Radiol. 1985;36:175–177. doi: 10.1016/S0009-9260(85)80104-5. [DOI] [PubMed] [Google Scholar]
- 5.Baylin S.B., Herman J.G., Graff J.R., Vertino P.M., Issa J.P. Alterations in DNA methylation: A fundamental aspect of neoplasia. Adv. Cancer Res. 1998;72:141–196. [PubMed] [Google Scholar]
- 6.Sadikovic B., Al-Romaih K., Squire J.A., Zielenska M. Cause and consequences of genetic and epigenetic alterations in human cancer. Curr. Genom. 2008;9:394–408. doi: 10.2174/138920208785699580. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Merlo A., Herman J.G., Mao L., Lee D.J., Gabrielson E., Burger P.C., Baylin S.B., Sidransky D. 5′ CpG island methylation is associated with transcriptional silencing of the tumour suppressor p16/CDKN2/MTS1 in human cancers. Nat. Med. 1995;1:686–692. doi: 10.1038/nm0795-686. [DOI] [PubMed] [Google Scholar]
- 8.Herman J.G., Umar A., Polyak K., Graff J.R., Ahuja N., Issa J.P., Markowitz S., Willson J.K., Hamilton S.R., Kinzler K.W., et al. Incidence and functional consequences of hMLH1 promoter hypermethylation in colorectal carcinoma. Proc. Natl. Acad. Sci. USA. 1998;95:6870–6875. doi: 10.1073/pnas.95.12.6870. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Baylin S.B. DNA methylation and gene silencing in cancer. Nat. Clin. Pract. Oncol. 2005;2(Suppl. 1):S4–S11. doi: 10.1038/ncponc0354. [DOI] [PubMed] [Google Scholar]
- 10.Leonhardt H., Cardoso M.C. DNA methylation, nuclear structure, gene expression and cancer. J. Cell. Biochem. 2000;79(Suppl. 35):78–83. doi: 10.1002/1097-4644(2000)79:35+<78::AID-JCB1129>3.0.CO;2-J. [DOI] [PubMed] [Google Scholar]
- 11.Yuan Y., Liu H., Sahin A., Dai J.L. Reactivation of SYK expression by inhibition of DNA methylation suppresses breast cancer cell invasiveness. Int. J. Cancer. 2005;113:654–659. doi: 10.1002/ijc.20628. [DOI] [PubMed] [Google Scholar]
- 12.Robertson K.D., Jones P.A. DNA methylation: Past, present and future directions. Carcinogenesis. 2000;21:461–467. doi: 10.1093/carcin/21.3.461. [DOI] [PubMed] [Google Scholar]
- 13.Momparler R.L. Cancer epigenetics. Oncogene. 2003;22:6479–6483. doi: 10.1038/sj.onc.1206774. [DOI] [PubMed] [Google Scholar]
- 14.Ehrlich M. DNA hypomethylation, cancer, the immunodeficiency, centromeric region instability, facial anomalies syndrome and chromosomal rearrangements. J. Nutr. 2002;132(Suppl. 8):2424S–2429S. doi: 10.1093/jn/132.8.2424S. [DOI] [PubMed] [Google Scholar]
- 15.Umbricht C.B., Evron E., Gabrielson E., Ferguson A., Marks J., Sukumar S. Hypermethylation of 14-3-3 sigma (stratifin) is an early event in breast cancer. Oncogene. 2001;20:3348–3353. doi: 10.1038/sj.onc.1204438. [DOI] [PubMed] [Google Scholar]
- 16.Evron E., Dooley W.C., Umbricht C.B., Rosenthal D., Sacchi N., Gabrielson E., Soito A.B., Hung D.T., Ljung B., Davidson N.E., et al. Detection of breast cancer cells in ductal lavage fluid by methylation-specific PCR. Lancet. 2001;357:1335–1336. doi: 10.1016/S0140-6736(00)04501-3. [DOI] [PubMed] [Google Scholar]
- 17.Evron E., Umbricht C.B., Korz D., Raman V., Loeb D.M., Niranjan B., Buluwela L., Weitzman S.A., Marks J., Sukumar S. Loss of cyclin D2 expression in the majority of breast cancers is associated with promoter hypermethylation. Cancer Res. 2001;61:2782–2787. [PubMed] [Google Scholar]
- 18.Belinsky S.A., Nikula K.J., Palmisano W.A., Michels R., Saccomanno G., Gabrielson E., Baylin S.B., Herman J.G. Aberrant methylation of p16(INK4a) is an early event in lung cancer and a potential biomarker for early diagnosis. Proc. Natl. Acad. Sci. USA. 1998;95:11891–11896. doi: 10.1073/pnas.95.20.11891. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Esteller M., Sparks A., Toyota M., Sanchez-Cespedes M., Capella G., Peinado M.A., Gonzalez S., Tarafa G., Sidransky D., Meltzer S.J., et al. Analysis of adenomatous polyposis coli promoter hypermethylation in human cancer. Cancer Res. 2000;60:4366–4371. [PubMed] [Google Scholar]
- 20.Hanahan D., Weinberg R.A. Hallmarks of cancer: The next generation. Cell. 2011;144:646–674. doi: 10.1016/j.cell.2011.02.013. [DOI] [PubMed] [Google Scholar]
- 21.Li L., Choi J.Y., Lee K.M., Sung H., Park S.K., Oze I., Pan K.F., You W.C., Chen Y.X., Fang J.Y., et al. DNA methylation in peripheral blood: A potential biomarker for cancer molecular epidemiology. J. Epidemiol. 2012;22:384–394. doi: 10.2188/jea.JE20120003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Jung K., Fleischhacker M., Rabien A. Cell-free DNA in the blood as a solid tumor biomarker—A critical appraisal of the literature. Clin. Chim. Acta. 2010;411:1611–1624. doi: 10.1016/j.cca.2010.07.032. [DOI] [PubMed] [Google Scholar]
- 23.Dammann R., Li C., Yoon J.H., Chin P.L., Bates S., Pfeifer G.P. Epigenetic inactivation of a RAS association domain family protein from the lung tumour suppressor locus 3p21.3. Nat. Genet. 2000;25:315–319. doi: 10.1038/77083. [DOI] [PubMed] [Google Scholar]
- 24.Richter A.M., Walesch S.K., Dammann R.H. Aberrant Promoter Methylation of the Tumour Suppressor RASSF10 and Its Growth Inhibitory Function in Breast Cancer. Cancers. 2016;8:26. doi: 10.3390/cancers8030026. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Choi Y.L., Kang S.Y., Shin Y.K., Choi J.S., Kim S.H., Lee S.J., Bae D.S., Ahn G. Aberrant hypermethylation of RASSF1A promoter in ovarian borderline tumors and carcinomas. Virchows Arch. 2006;448:331–336. doi: 10.1007/s00428-005-0091-3. [DOI] [PubMed] [Google Scholar]
- 26.Pfeifer G.P., Dammann R. Methylation of the tumor suppressor gene RASSF1A in human tumors. Biochemistry. 2005;70:576–583. doi: 10.1007/s10541-005-0151-y. [DOI] [PubMed] [Google Scholar]
- 27.Liu L., Yoon J.H., Dammann R., Pfeifer G.P. Frequent hypermethylation of the RASSF1A gene in prostate cancer. Oncogene. 2002;21:6835–6840. doi: 10.1038/sj.onc.1205814. [DOI] [PubMed] [Google Scholar]
- 28.Kristensen L.S., Raynor M.P., Candiloro I., Dobrovic A. Methylation profiling of normal individuals reveals mosaic promoter methylation of cancer-associated genes. Oncotarget. 2012;3:450–461. doi: 10.18632/oncotarget.480. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Ahmed M., Rahman N. ATM and breast cancer susceptibility. Oncogene. 2006;25:5906–5911. doi: 10.1038/sj.onc.1209873. [DOI] [PubMed] [Google Scholar]
- 30.Begam N., Jamil K., Raju S.G. Promoter Hypermethylation of the ATM Gene as a Novel Biomarker for Breast Cancer. Asian Pac. J. Cancer Prev. 2017;18:3003–3009. doi: 10.22034/APJCP.2017.18.11.3003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Majchrzak-Celinska A., Paluszczak J., Szalata M., Barciszewska A.M., Nowak S., Kleszcz R., Sherba A., Baer-Dubowska W. The methylation of a panel of genes differentiates low-grade from high-grade gliomas. Tumour Biol. 2015;36:3831–3841. doi: 10.1007/s13277-014-3025-3. [DOI] [PubMed] [Google Scholar]
- 32.Huang Q., Su X., Ai L., Li M., Fan C.Y., Weiss L.M. Promoter hypermethylation of multiple genes in gastric lymphoma. Leuk. Lymphoma. 2007;48:1988–1996. doi: 10.1080/10428190701573224. [DOI] [PubMed] [Google Scholar]
- 33.Bai A.H., Tong J.H., To K.F., Chan M.W., Man E.P., Lo K.W., Lee J.F., Sung J.J., Leung W.K. Promoter hypermethylation of tumor-related genes in the progression of colorectal neoplasia. Int. J. Cancer. 2004;112:846–853. doi: 10.1002/ijc.20485. [DOI] [PubMed] [Google Scholar]
- 34.Cho Y.H., Yazici H., Wu H.C., Terry M.B., Gonzalez K., Qu M., Dalay N., Santella R.M. Aberrant promoter hypermethylation and genomic hypomethylation in tumor, adjacent normal tissues and blood from breast cancer patients. Anticancer Res. 2010;30:2489–2496. [PMC free article] [PubMed] [Google Scholar]
- 35.Zmetakova I., Danihel L., Smolkova B., Mego M., Kajabova V., Krivulcik T., Rusnak I., Rychly B., Danis D., Repiska V., et al. Evaluation of protein expression and DNA methylation profiles detected by pyrosequencing in invasive breast cancer. Neoplasma. 2013;60:635–646. doi: 10.4149/neo_2013_082. [DOI] [PubMed] [Google Scholar]
- 36.Flanagan J.M., Munoz-Alegre M., Henderson S., Tang T., Sun P., Johnson N., Fletcher O., Dos Santos Silva I., Peto J., Boshoff C., et al. Gene-body hypermethylation of ATM in peripheral blood DNA of bilateral breast cancer patients. Hum. Mol. Genet. 2009;18:1332–1342. doi: 10.1093/hmg/ddp033. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Brennan K., Garcia-Closas M., Orr N., Fletcher O., Jones M., Ashworth A., Swerdlow A., Thorne H., Riboli E., Vineis P., et al. Intragenic ATM methylation in peripheral blood DNA as a biomarker of breast cancer risk. Cancer Res. 2012;72:2304–2313. doi: 10.1158/0008-5472.CAN-11-3157. [DOI] [PubMed] [Google Scholar]
- 38.Tang Q., Cheng J., Cao X., Surowy H., Burwinkel B. Blood-based DNA methylation as biomarker for breast cancer: A systematic review. Clin. Epigenetics. 2016;8:115. doi: 10.1186/s13148-016-0282-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Ahmed I.A., Pusch C.M., Hamed T., Rashad H., Idris A., El-Fadle A.A., Blin N. Epigenetic alterations by methylation of RASSF1A and DAPK1 promoter sequences in mammary carcinoma detected in extracellular tumor DNA. Cancer Genet. Cytogenet. 2010;199:96–100. doi: 10.1016/j.cancergencyto.2010.02.007. [DOI] [PubMed] [Google Scholar]
- 40.Dulaimi E., Hillinck J., Ibanez de Caceres I., Al-Saleem T., Cairns P. Tumor suppressor gene promoter hypermethylation in serum of breast cancer patients. Pt 1Clin. Cancer Res. 2004;10:6189–6193. doi: 10.1158/1078-0432.CCR-04-0597. [DOI] [PubMed] [Google Scholar]
- 41.Cho Y.H., McCullough L.E., Gammon M.D., Wu H.C., Zhang Y.J., Wang Q., Xu X., Teitelbaum S.L., Neugut A.I., Chen J., et al. Promoter Hypermethylation in White Blood Cell DNA and Breast Cancer Risk. J. Cancer. 2015;6:819–824. doi: 10.7150/jca.12174. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Harrison K., Hoad G., Scott P., Simpson L., Horgan G.W., Smyth E., Heys S.D., Haggarty P. Breast cancer risk and imprinting methylation in blood. Clin. Epigenetics. 2015;7:92. doi: 10.1186/s13148-015-0125-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Ito Y., Koessler T., Ibrahim A.E., Rai S., Vowler S.L., Abu-Amero S., Silva A.L., Maia A.T., Huddleston J.E., Uribe-Lewis S., et al. Somatically acquired hypomethylation of IGF2 in breast and colorectal cancer. Hum. Mol. Genet. 2008;17:2633–2643. doi: 10.1093/hmg/ddn163. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Widschwendter M., Apostolidou S., Raum E., Rothenbacher D., Fiegl H., Menon U., Stegmaier C., Jacobs I.J., Brenner H. Epigenotyping in peripheral blood cell DNA and breast cancer risk: A proof of principle study. PLoS ONE. 2008;3:e2656. doi: 10.1371/journal.pone.0002656. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Hoque M.O., Feng Q., Toure P., Dem A., Critchlow C.W., Hawes S.E., Wood T., Jeronimo C., Rosenbaum E., Stern J., et al. Detection of aberrant methylation of four genes in plasma DNA for the detection of breast cancer. J. Clin. Oncol. 2006;24:4262–4269. doi: 10.1200/JCO.2005.01.3516. [DOI] [PubMed] [Google Scholar]
- 46.Papadopoulou E., Davilas E., Sotiriou V., Georgakopoulos E., Georgakopoulou S., Koliopanos A., Aggelakis F., Dardoufas K., Agnanti N.J., Karydas I., et al. Cell-free DNA and RNA in plasma as a new molecular marker for prostate and breast cancer. Ann. N. Y. Acad. Sci. 2006;1075:235–243. doi: 10.1196/annals.1368.032. [DOI] [PubMed] [Google Scholar]
- 47.Yazici H., Terry M.B., Cho Y.H., Senie R.T., Liao Y., Andrulis I., Santella R.M. Aberrant methylation of RASSF1A in plasma DNA before breast cancer diagnosis in the Breast Cancer Family Registry. Cancer Epidemiol. Biomarkers Prev. 2009;18:2723–2725. doi: 10.1158/1055-9965.EPI-08-1237. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Van der Auwera I., Elst H.J., Van Laere S.J., Maes H., Huget P., van Dam P., Van Marck E.A., Vermeulen P.B., Dirix L.Y. The presence of circulating total DNA and methylated genes is associated with circulating tumour cells in blood from breast cancer patients. Br. J. Cancer. 2009;100:1277–1286. doi: 10.1038/sj.bjc.6605013. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Kim J.H., Shin M.H., Kweon S.S., Park M.H., Yoon J.H., Lee J.S., Choi C., Fackler M.J., Sukumar S. Evaluation of promoter hypermethylation detection in serum as a diagnostic tool for breast carcinoma in Korean women. Gynecol. Oncol. 2010;118:176–181. doi: 10.1016/j.ygyno.2010.04.016. [DOI] [PubMed] [Google Scholar]
- 50.Kloten V., Becker B., Winner K., Schrauder M.G., Fasching P.A., Anzeneder T., Veeck J., Hartmann A., Knuchel R., Dahl E. Promoter hypermethylation of the tumor-suppressor genes ITIH5, DKK3, and RASSF1A as novel biomarkers for blood-based breast cancer screening. Breast Cancer Res. 2013;15:R4. doi: 10.1186/bcr3375. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Brooks J.D., Cairns P., Shore R.E., Klein C.B., Wirgin I., Afanasyeva Y., Zeleniuch-Jacquotte A. DNA methylation in pre-diagnostic serum samples of breast cancer cases: Results of a nested case-control study. Cancer Epidemiol. 2010;34:717–723. doi: 10.1016/j.canep.2010.05.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Wu H.C., Delgado-Cruzata L., Flom J.D., Perrin M., Liao Y., Ferris J.S., Santella R.M., Terry M.B. Repetitive element DNA methylation levels in white blood cell DNA from sisters discordant for breast cancer from the New York site of the Breast Cancer Family Registry. Carcinogenesis. 2012;33:1946–1952. doi: 10.1093/carcin/bgs201. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Xu X., Gammon M.D., Hernandez-Vargas H., Herceg Z., Wetmur J.G., Teitelbaum S.L., Bradshaw P.T., Neugut A.I., Santella R.M., Chen J. DNA methylation in peripheral blood measured by LUMA is associated with breast cancer in a population-based study. FASEB J. 2012;26:2657–2666. doi: 10.1096/fj.11-197251. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Chan K.C., Zhang J., Hui A.B., Wong N., Lau T.K., Leung T.N., Lo K.W., Huang D.W., Lo Y.M. Size distributions of maternal and fetal DNA in maternal plasma. Clin. Chem. 2004;50:88–92. doi: 10.1373/clinchem.2003.024893. [DOI] [PubMed] [Google Scholar]
- 55.Radpour R., Sikora M., Grussenmeyer T., Kohler C., Barekati Z., Holzgreve W., Lefkovits I., Zhong X.Y. Simultaneous isolation of DNA, RNA, and proteins for genetic, epigenetic, transcriptomic, and proteomic analysis. J. Proteome Res. 2009;8:5264–5274. doi: 10.1021/pr900591w. [DOI] [PubMed] [Google Scholar]
- 56.Li Y., Zimmermann B., Rusterholz C., Kang A., Holzgreve W., Hahn S. Size separation of circulatory DNA in maternal plasma permits ready detection of fetal DNA polymorphisms. Clin. Chem. 2004;50:1002–1011. doi: 10.1373/clinchem.2003.029835. [DOI] [PubMed] [Google Scholar]
- 57.Van der Vaart M., Pretorius P.J. The origin of circulating free DNA. Clin. Chem. 2007;53:2215. doi: 10.1373/clinchem.2007.092734. [DOI] [PubMed] [Google Scholar]
- 58.Radpour R., Barekati Z., Kohler C., Lv Q., Burki N., Diesch C., Bitzer J., Zheng H., Schmid S., Zhong X.Y. Hypermethylation of tumor suppressor genes involved in critical regulatory pathways for developing a blood-based test in breast cancer. PLoS ONE. 2011;6:e16080. doi: 10.1371/journal.pone.0016080. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Ehrich M., Nelson M.R., Stanssens P., Zabeau M., Liloglou T., Xinarianos G., Cantor C.R., Field J.K., van den Boom D. Quantitative high-throughput analysis of DNA methylation patterns by base-specific cleavage and mass spectrometry. Proc. Natl. Acad. Sci. USA. 2005;102:15785–15790. doi: 10.1073/pnas.0507816102. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60.Edge S.B., Compton C.C. The American Joint Committee on Cancer: The 7th edition of the AJCC cancer staging manual and the future of TNM. Ann. Surg. Oncol. 2010;17:1471–1474. doi: 10.1245/s10434-010-0985-4. [DOI] [PubMed] [Google Scholar]
- 61.Tang Q., Holland-Letz T., Slynko A., Cuk K., Marme F., Schott S., Heil J., Qu B., Golatta M., Bewerunge-Hudler M., et al. DNA methylation array analysis identifies breast cancer associated RPTOR, MGRN1 and RAPSN hypomethylation in peripheral blood DNA. Oncotarget. 2016;7:64191–64202. doi: 10.18632/oncotarget.11640. [DOI] [PMC free article] [PubMed] [Google Scholar]
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