Literature DB >> 29480000

Promoter Methylation of BRCA1, DAPK1 and RASSF1A iszzm321990Associated with Increased Mortality among Indian Womenzzm321990with Breast Cancer

Prasant Yadav1,2, Mirza Masroor, Kajal Nandi, R C M Kaza, S K Jain, Nita Khurana, Alpana Saxena.   

Abstract

Background: Promoter methylation has been observed for several genes in association with cancer development and progression. Hypermethylation mediated-silencing of tumor suppressor genes (TSGs) may contribute to breast cancer pathogenesis. The present study was conducted to investigate the promoter methylation status of BRCA1, DAPK1 and RASSF1A genes in Indian women with breast cancer. Materials and
Methods: Promoter methylation was evaluated in DNA extracted from mononuclear cells (MNCs) in peripheral blood samples of 60 histopathologically confirmed newly diagnosed, untreated cases of breast cancer as well as 60 age and sex matched healthy controls using MS-PCR. Association of promoter methylation with breast cancer-specific mortality was analyzed with Cox proportional hazards models. Kaplan-Meier survival analysis was performed for overall survival of the breast cancer patients.
Results: We observed a significant increase of BRCA1, DAPK1 and RASSF1A promoter methylation levels by 51.7% (P <0.001), 55.0% (P <0.001) and 46.6% (P <0.001), respectively, when compared to healthy controls. A strong correlation was noted between hypermethylation of the tumor suppressor genes BRCA1 (P= 0.009), DAPK1 (P= 0.008) and RASSF1A (P= 0.02)) with early and advanced stages of breast cancer patients. We also found that breast cancer-specific mortality was significantly associated with promoter methylation of BRCA1 [HR and 95% CI: 3.25 (1.448-7.317)] and DAPK1 [HR and 95% CI: 2.32 (1.05-5.11)], whereas limited significant link was evident with RASSF1A [HR and 95% CI: 1.54 (0.697-3.413].
Conclusion: Our results suggest that promoter methylation of BRCA1, DAPK1 and RASSF1A genes may be associated with disease progression and poor overall survival of Indian women with breast cancer. Creative Commons Attribution License

Entities:  

Keywords:  Promoter methylation; tumor suppressor genes; MS−PCR; breast cancer

Mesh:

Substances:

Year:  2018        PMID: 29480000      PMCID: PMC5980932          DOI: 10.22034/APJCP.2018.19.2.443

Source DB:  PubMed          Journal:  Asian Pac J Cancer Prev        ISSN: 1513-7368


Introduction

Breast cancer is the leading cause of mortality among women worldwide, but the exact etiology of breast cancer remains unknown. DNA methylation has attracted deep investigation in past several years and it has been seen that methylation regulation of genes related to cancer (Das and Singal, 2004). Specifically, aberrant promoter methylation takes place in several genes in cancer development and progression (Widschwendter and Jones 2002). BRCA1 (Catteau et al., 1999; Rice et al., 2000), RASSF1A (Agathanggelou et al., 2001), DAPK1 (Dulaimi et al., 2004) are frequently methylated tumor suppressor genes in breast cancer. The process of gene silencing by methylation and its role in cancer pathogenesis is well mentioned, with methylation of tumor suppressor genes, affecting transcriptional activity of the genes, believed to be the most important drivers of carcinogenesis. Recently, attention is paid to the phenomenon of hypermethylation of disease-related genes in peripheral blood DNA and its involvement in the pathology of cancer and other diseases (Woodson et al., 2001; Widschwendter, et al., 2008; Flanagan et al., 2009; Iwamoto, Yamamoto et al., 2011). This suggested that detection of tumor DNA in the blood may serve as an early and more accessible marker of diagnosis and prognosis of breast cancer. However, the frequency of aberrant methylation in peripheral blood has not been extensively investigated. BRCA1 status may potentially be used as a prognostic marker as several studies have shown that BRCA1 mutated breast cancer is associated with poor survival (Moller et al., 2007). BRCA1 promoter methylation was observed to be significantly associated with breast cancer-specific mortality (Xu et al., 2009, Hsu et al., 2013). DNA methylation markers have been used as an alternative approach to molecular profiling of breast cancer. RASSF1A promoter methylation provides important prognostic information in early stage breast cancer patients (Widschwendter et al., 2004; Jezkova et al., 2016). Promoter methylation of DAPK1 gene was also observed to be associated with DCIS, LCIS and all grades and stages of breast cancer patients (Dulaimi et al., 2004). All of these results suggest that DNA methylation correlates with clinical findings in breast cancer and may help in the prediction of therapeutic strategy for breast cancer. Moreover, these results demonstrate that MNCs DNA may be a potential biomarker for analysis of promoter methylation status. In current study, we investigated the promoter methylation status of BRCA1, DAPK1 and RASSF1A genes in relation to clinicopathological features and breast cancer survival in breast cancer patients.

Materials and Methods

Study Population

The current study was performed on 60 histopathologically confirmed newly diagnosed, untreated cases of North Indian breast cancer patients and 60 age-matched female healthy volunteers. Samples were collected from Department of Surgery, Lok Nayak Jaiprakash Hospital, New Delhi during January 2012 to December 2013. 5ml of peripheral blood sample was collected from each patient as well as healthy volunteer and stored at -80°C. The study was ethically approved by Institutional Ethics Committee, Maulana Azad Medical College, New Delhi. Written informed consent was taken from each study subjects. Demographic data of patients and controls are shown in Table 1.
Table 1

Demographic Features of Breast Cancer Patients and Healthy Controls

ParametersCases (%)Healthy Controls (%)
Patients60 (100%)60 (100%)
Age at diagnosis
 Age ≤ 4526 (43.3)25 (41.7)
 Age > 4534 (56.7)35 (58.3)
Mean±SD49.2 ± 12.4748.69±12.25
Menopause
 Pre21 (35)
 Post39 (65)
TNM Stages
 I3 (5)
 II29 (48.3)
 III25 (41.7)
 IV3 (5)
Tumor Grading
 I4 (6.6)
 II33 (55)
 III23 (38.4)
Lymph Node Status
 Positive29 (48.4)
 Neative31 (51.6)
Chemotherapy
 Adjuvant14 (23.3)
 Neo-Adjuvant46 (76.7)
ER Status
 Positive11 (18.3)
 Neative49 (81.7)
PR Status
 Positive9 (15)
 Neative51 (85)
HER2/neu Status
 Positive23 (38.4)
 Neative37 (61.6)
Distant Metastasis
 Positive3 (5)
 Neative57 (95)
Demographic Features of Breast Cancer Patients and Healthy Controls

Patient data collection and Follow-up

Patient follow-up was done through the hospital records and confirmed by direct patient contact. Tumor characteristics and treatment information was obtained from the patient at the time of diagnosis and/or during the regular visit and verified with hospital record. The questionnaires were administrated to evaluate the demographic features and breast cancer-related features of patients. Patients with a history of any other malignancy or metastasized cancer from any other sites were excluded. The total follow-up period was 45 months and mean follow-up time was 30.98.

DNA extraction and bisulfite modification

DNA extraction was performed on peripheral blood mononuclear Cells (PBMNCs) using Blood DNA extraction kit (Geneaid) by following manufacturer’s instructions. DNA concentrations were measured and 1μg of DNA was used for bisulfite modification. DNA bisulfite modification was performed using Bisulflash DNA modification kit (Epigenetek) according to the manufacturer’s instructions. Bisulfite treated DNA was immediately stored at -20°C.

Methylation Specific- Polymerase Chain Reaction (MS-PCR) Analysis

After bisulfite conversion, Qualitative methylation status of different genes were analyzed by Methylation-Specific Polymerase Chain Reaction (MS-PCR). Primers for MS-PCR were as shown in previous studies (Estellers et al., 1999; Baldwinet al., 2000; Burbee et al., 2001) and also shown in Table 2. PCRs were run in a volume of 25 μl, containing 2ul bisulfite-modified DNA, 12 μl of 2x Hot Start PCR Mastermix (Fermentas), 0.25μl sense primer (25 pM), 0.25 μl antisense primer (25 pM), and 12.5μl H2O. The PCR profile was 95°C for 10 minutes, 40 cycles at 95°C for 45 seconds, primer annealing at 56°C to 60°C for 45 seconds, 72°C for 45 seconds, and a final extension step at 72°C 10 minutes. The amplified PCR products were further electrophoresed on 2% agarose gels and evaluated under ultraviolet light (Figure 1).
Table 2

Primer Sequence for Methylation- Specific Polymerase Chain Reaction used for BRCA1, DAPK1 and RASSF1Agenes

GenePrimer NameSense PrimerAntisense PrimerAnnealing Temp (°C)Size (bp)
BRCA1UnmethylatedGGTTAATTTAGAGTTTTGAGAGATGTCAACAAACTCACACCACACAATCA56182 bp
MethylatedGGTTAATTTAGAGTTTCGAGAGACGTCAACGAACTCACGCCGCGCAATCG56182 bp
DAPK1UnmethylatedGGAGGATAGTTGGATTGAGTTAATGTTCAAATCCCTCCCAAACACCAA60105 bp
MethylatedGGATAGTCGGATCGAGTTAACGTCCCCTCCCAAACGCCGA6097 bp
RASSF1AUnmethylatedGGAGGATAGTTGGATTGAGTTAATGTTGGTTTTTGTGAGTGTGTTTAG60169 bp
MethylatedGCTAACAAACGCGAACCGCCCTCCCAAACGCCGA60169 bp
Figure 1

Representative Results of MS-PCR Analysis for (A) BRCA1, (B) DAPK1 and (C) RASSF1A in Breast Cancer Patients. Lanes M and U correspond to methylated and unmethylated samples respectively and Last Lane to a 100bp ladder as molecular weight marker.

Primer Sequence for Methylation- Specific Polymerase Chain Reaction used for BRCA1, DAPK1 and RASSF1Agenes Representative Results of MS-PCR Analysis for (A) BRCA1, (B) DAPK1 and (C) RASSF1A in Breast Cancer Patients. Lanes M and U correspond to methylated and unmethylated samples respectively and Last Lane to a 100bp ladder as molecular weight marker.

Statistical Analysis

SPSS 16 and GraphPad Statistical software were used for statistical analysis of the study. Methylation frequencies between the patients and healthy volunteers were analyzed using the Chi-square test and values less than 5 were analyzed by Fisher exact test. The Cox proportional hazard regression (Hosmer, 1999) was used to calculate the hazard ratio (HR) and 95% confidence interval (CI) for the association between gene promoter methylation status and breast cancer-specific mortality. Kaplan-Meier survival analysis was performed for overall survival of breast cancer patients. The p-value less than 0.05 was considered to be statistically significant.

Results

Patient Characteristics

Among the cases, 26(43.3%) were age ≤ 45 age group and 34(56.7%) >45 years group. Menopausal status shows that 21(35%) patients were in premenopausal status and 39(65%) patients were in postmenopausal status. TNM staging reveals that 32(53.3%) patients were in early stages (I and II) and 28(46.7%) patients were in advanced stages (III and IV). Histological grading of the patients shows that 4(6.6), 33(55%) and 23(38.4%) were in well differentiated, moderately differentiated and poorly differentiated, respectively. Lymph node status shows that 29(48.4%) cases were positive for lymph node metastasis. Hormone receptor status shows that 11(18.3%) patients were positive for Estrogen receptor (ER), 9(15%) patients were positive for Progesterone receptor (PR) and 23(38.4%) were HER2/neu positive. Of the total breast cancer cases, 3(5%) patients having distant metastasis.

Promoter hypermethylation and clinicopathological features of breast cancer patients

Of the three tumor suppressor genes tested, All three genes (BRCA1, DAPK1 and RASSF1A) were found significantly hypermethylated (P <0.001) in cases than the healthy controls. Their methylation levels were 31/60(51.66%) (P <0.001), 33/60(55%) (P <0.001), 28/60(46.6%) (P <0.001) respectively (Table 3).
Table 3

Association between Promoter Methylation of Tumor Suppressor Genes and Clinico- Pathological Features

BRCA1 Positive n(%)p-valueDAPK1 Positive n(%)p-valueRASSF1A Positivep-value
Cases (60)31 (51.66)<0.00133 (55)<0.00128 (46.6)< 0.001
Controls (60)0 (0)0 (0)00 (0)
Age at Diagnosis
 Age ≤ 45 (26)11 (42.3)0.315 (57.7)0.9212 (46.2)0.8
 Age > 45 (34)20 (58.80)18 (53)16 (47.1)
Menopause Stages
 Pre (21)8 (38.1)0.212 (57)0.98 (38.1)0.4
 Post (39)23 (59)21 (53.8)20 (51.2)
TNM Stages
 Early (I&II) (32)11 (34.3)0.00912 (37.5)0.00810 (31.3)0.02
 Advanced20 (71.4)21 (75)18 (64.3)
(III&IV) (28)
Histological Grading
 I (4)1 (25)0.21 (25)0.381 (25)0.24
 II (33)15 (45.4)20 (60.6)13 (39.4)
 III (23)15 (65.2)12 (52.2)14 (60.9)
Lymph Nodes
 Positive (31)19 (61.3)0.1920 (60.6)0.316 (55.2)0.5
 Negative (29)12 (41.3)13 (44.8)12 (44.4)
Chemotherapy
 Adjuvant (14)4 (28.6)0.068 (57.1)0.95 (37.8)0.5
 Neoadjuvant (46)27 (58.7)25 (54.3)23 (50)
ER Status
 Positive (11)6 (54.6)0.96 (54.5)0.766 (54.6)0.8
 Negative (49)25 (51.0)27 (55.1)23 (46.9)
PR Status
 Positive (09)5 (55.5)15 (55.5)0.635 (55.5)0.5
 Negative (51)26 (51)28 (54.9)24 (47.1)
HER2/neu
 Positive (23)12 (52.2)0.814 (60.8)0.6411 (47.8)0.9
 Negative (37)19 (51.4)19 (51.3)17 (45.9)
Distant Metastasis
 Positive (03)3 (100)0.23 (100)0.23 (100)0.09
 Negative (57)28 (49.2)30 (52.6)25 (43.9)
Association between Promoter Methylation of Tumor Suppressor Genes and Clinico- Pathological Features We found a significant difference between tumor suppressor gene, BRCA1 (P= 0.009), DAPK1 (P= 0.008) and RASSF1A (P= 0.02)) hypermethylation with early and advanced stages of breast cancer patients (Table 3). No significant association was found between tumor suppressor genes (BRCA1, DAPK1 and RASSF1A) and Age at diagnosis, Menopausal status, histological grading, Lymph node status, Chemotherapy, Estrogen receptor (ER), Progesterone receptor (PR), HER2/neu and Distant metastasis.

Promoter Hypermethylation and survival analysis of breast cancer patients

Among total 60 cases of breast cancer, 25 patients died during the follow-up period. We found that all 25 cases died due to the advancement of the disease. Table 4 shows the association of methylation status of BRCA1, DAPK1 and RASSF1A with breast cancer-specific mortality in Indian population. At the end of follow-up, Compared to the cases with unmethylated promoter of BRCA1, cases with methylated promoter having highest risk (HR: 3.25(1.448- 7.317)) of death due to breast cancer. In cases of promoter methylation of DAPK1, we found comparatively low but significant risk (HR: 2.32(1.05-5.11)) of breast cancer-specific mortality than BRCA1 promoter methylation. In comparison of BRCA1 and DAPK1 promoter methylation with survival, RASSF1A promoter methylation having lowest risk ((HR: 1.54(0.697-3.413)) of breast cancer-specific mortality.
Table 4

Hazard Ratios (HRs) and 95% Confidence Intervals (CIs) for the Associations of Gene Promoter Methylation Status and Mortality among Indian Breast Cancer Patients

GenesNo of CasesNo of DeathsHazard ratio (95%CI)
BRCA1
 Unmethylated2971.00 (Ref)
 Methylated31183.25 (1.448- 7.317)
DAPK1
 Unmethylated2771.00 (Ref)
 Methylated33182.32 (1.05-5.11)
RASSF1
 Unmethylated33111.00 (Ref)
 Methylated27141.54 (0.697-3.413)
Hazard Ratios (HRs) and 95% Confidence Intervals (CIs) for the Associations of Gene Promoter Methylation Status and Mortality among Indian Breast Cancer Patients

Discussion

To effectively reduce the disease burden of breast cancer, it is important to identify etiologic factors of the disease as well as factors that predict survival. We studied promoter methylation of three tumor suppressor genes previously found to be associated with breast cancer-specific mortality (Cho et al., 2012). In present study, we found a significant difference between promoter methylation of cases than controls for all three genes. Frequencies for the methylation of these three genes (BRCA1, DAPK1 and RASSF1A) were 51.66%, 55%, 46.6% respectively. Similarly, significant results were also seen in the previous studies analyzed these three genes in different populations (Bagadi et al., 2008; Ahmed et al., 2010; Cho et al., 2012; Spitzwieseret al., 2015). A similar study was performed on the similar population by Sharma et al., (2009) found comparatively higher frequency for RASSF1A, but lower frequency for BRCA1 promoter methylation. Another study by Dulaimi et al., (2004) found almost similar frequency for DAPK1 gene in serum of breast cancer patients. This discrepancy in results was found may be due to various reasons like sample size, race, treatment status, dietary intake, family history etc. While analyzing the number of methylated genes and survival of the patients in a dose-dependent manner, we found significant decrease in overall survival with increase in number of promoter methylated genes (Table 5).
Table 5

Number of Methylated Genes in Relation to Breast Cancer- Specific Mortality among Indian Breast Cancer Patients

No. of genes methylatedNo. of CasesNo. of DeathsHR (95% CI)
0921.00 (ref.)
12140.81 (0.13-4.73)
220122.50 (0.82-7.66)
31074.12 (1.09-15.57)
Number of Methylated Genes in Relation to Breast Cancer- Specific Mortality among Indian Breast Cancer Patients Additionally, we found significant correlation between promoter methylation of all three genes and early and advanced stages of breast cancer patients, which demonstrate an increase in promoter methylation level with the advancement of disease. Several previous studies are in support of our findings (Singh et al., 2011; Tserga et al., 2012). Apart from TNM stages, we are not able to find any correlation between promoter methylation of these tumor suppressor genes and other clinico-pathological features of breast cancer patients. Very limited studies were done to investigate the prognostic role of promoter methylation of these tumor suppressor genes in Indian breast cancer patients. In our study, we have seen a strong association between BRCA1 and DAPK1 promoter methylation with poor prognosis of breast cancer patients. BRCA1 and DAPK1 shown to be significantly associated with poor overall survival (Figure 2a and 2b respectively). For RASSF1A promoter methylation, we have seen a weak association with overall survival (Fig 2c). Furthermore, we have analyzed the combined effect of BRCA1 and DAPK1 methylation in survival of breast cancer patients; we found a significant decrease in breast cancer survival (Figure 2d). A previous study Xu et al., (2009) also found similar association between BRCA1 promoter methylation in breast cancer patients with poor survival. Another study by Cho et al.,(2012) (found similarly weak association between RASSF1A promoter methylation and breast cancer survival.
Figure 2

Kaplan – Meier Survival Plot for Breast Cancer Patients by (a) BRCA1, (b) DAPK1, (c) RASSF1A and (d) BRCA1 + DAPK1 Promoter Methylation Status in Peripheral Blood Samples

Kaplan – Meier Survival Plot for Breast Cancer Patients by (a) BRCA1, (b) DAPK1, (c) RASSF1A and (d) BRCA1 + DAPK1 Promoter Methylation Status in Peripheral Blood Samples Few studies of BRCA1 promoter methylation in normal breast tissues have identified it in 8.3–22% of these tissues (Bean et al., 2007). However, these studies did not confirm the absence of tumor cells and benign proliferative lesions in the analyzed tissues (Bean et al., 2007; Vasilatos et al., 2009). Pu et al., (2003) observed that promoter methylation of RASSF1A was found to be more commonly in healthy female predicted to have a high risk of breast cancer. In conclusion, we found a significant association between BRCA1, DAPK1 and RASSF1A gene promoter methylation with North Indian breast cancer patients compared to healthy controls. Promoter methylation of these three tumor suppressor genes individually and in combined significantly multiply the risk of breast cancer progression. Moreover, we also observed that promoter methylation of these genes associated with high TNM stages and Poor survival of breast cancer patients. Our results indicate that promoter methylation of BRCA1, DAPK1 and RASSF1A genes in PBMNC DNA may be associated with breast cancer progression and poorer overall survival. A large pooled study on Indian breast cancer cases is required to confirm our finding.
  28 in total

1.  Prognostic significance of gene-specific promoter hypermethylation in breast cancer patients.

Authors:  Yoon Hee Cho; Jing Shen; Marilie D Gammon; Yu-Jing Zhang; Qiao Wang; Karina Gonzalez; Xinran Xu; Patrick T Bradshaw; Susan L Teitelbaum; Gail Garbowski; Hanina Hibshoosh; Alfred I Neugut; Jia Chen; Regina M Santella
Journal:  Breast Cancer Res Treat       Date:  2011-08-12       Impact factor: 4.872

2.  Hypermethylation of the breast cancer-associated gene 1 promoter does not predict cytologic atypia or correlate with surrogate end points of breast cancer risk.

Authors:  Gregory R Bean; Catherine Ibarra Drendall; Vanessa K Goldenberg; Joseph C Baker; Michelle M Troch; Carolyn Paisie; Lee G Wilke; Lisa Yee; Paul K Marcom; Bruce F Kimler; Carol J Fabian; Carola M Zalles; Gloria Broadwater; Victoria Scott; Victoria L Seewaldt
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2007-01       Impact factor: 4.254

3.  Epigenetic alterations by methylation of RASSF1A and DAPK1 promoter sequences in mammary carcinoma detected in extracellular tumor DNA.

Authors:  Inas A Ahmed; Carsten M Pusch; Thanaa Hamed; Hamed Rashad; Amal Idris; Amal Abou El-Fadle; Nikolaus Blin
Journal:  Cancer Genet Cytogenet       Date:  2010-06

4.  Association of aberrant DNA methylation with clinicopathological features in breast cancer.

Authors:  Aggeliki Tserga; Nicolaos V Michalopoulos; Georgia Levidou; Penelope Korkolopoulou; George Zografos; Efstratios Patsouris; Angelica A Saetta
Journal:  Oncol Rep       Date:  2011-12-05       Impact factor: 3.906

5.  Epigenetic inactivation of RASSF1A in lung and breast cancers and malignant phenotype suppression.

Authors:  D G Burbee; E Forgacs; S Zöchbauer-Müller; L Shivakumar; K Fong; B Gao; D Randle; M Kondo; A Virmani; S Bader; Y Sekido; F Latif; S Milchgrub; S Toyooka; A F Gazdar; M I Lerman; E Zabarovsky; M White; J D Minna
Journal:  J Natl Cancer Inst       Date:  2001-05-02       Impact factor: 13.506

6.  Association of breast cancer DNA methylation profiles with hormone receptor status and response to tamoxifen.

Authors:  Martin Widschwendter; Kimberly D Siegmund; Hannes M Müller; Heidi Fiegl; Christian Marth; Elisabeth Müller-Holzner; Peter A Jones; Peter W Laird
Journal:  Cancer Res       Date:  2004-06-01       Impact factor: 12.701

7.  Methylation profiling of benign and malignant breast lesions and its application to cytopathology.

Authors:  Robert T Pu; Lauren E Laitala; Patricia M Alli; Mary Jo Fackler; Saraswati Sukumar; Douglas P Clark
Journal:  Mod Pathol       Date:  2003-11       Impact factor: 7.842

8.  CpG island tumor suppressor promoter methylation in non-BRCA-associated early mammary carcinogenesis.

Authors:  Shauna N Vasilatos; Gloria Broadwater; William T Barry; Joseph C Baker; Siya Lem; Eric C Dietze; Gregory R Bean; Andrew D Bryson; Patrick G Pilie; Vanessa Goldenberg; David Skaar; Carolyn Paisie; Alejandro Torres-Hernandez; Tracey L Grant; Lee G Wilke; Catherine Ibarra-Drendall; Julie H Ostrander; Nicholas C D'Amato; Carola Zalles; Randy Jirtle; Valerie M Weaver; Victoria L Seewaldt
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2009-03-03       Impact factor: 4.254

Review 9.  DNA methylation and cancer.

Authors:  Partha M Das; Rakesh Singal
Journal:  J Clin Oncol       Date:  2004-11-15       Impact factor: 44.544

10.  Prognostic relevance of promoter hypermethylation of multiple genes in breast cancer patients.

Authors:  Gayatri Sharma; Sameer Mirza; Yi-Hsin Yang; Rajinder Parshad; Priya Hazrah; Siddartha Datta Gupta; Ranju Ralhan
Journal:  Cell Oncol       Date:  2009       Impact factor: 6.730

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  11 in total

Review 1.  Dietary fat and obesity as modulators of breast cancer risk: Focus on DNA methylation.

Authors:  Micah G Donovan; Spencer N Wren; Mikia Cenker; Ornella I Selmin; Donato F Romagnolo
Journal:  Br J Pharmacol       Date:  2020-01-26       Impact factor: 8.739

Review 2.  Epigenetic Determinants of Racial Disparity in Breast Cancer: Looking beyond Genetic Alterations.

Authors:  Shriya Joshi; Chakravarthy Garlapati; Ritu Aneja
Journal:  Cancers (Basel)       Date:  2022-04-09       Impact factor: 6.575

3.  Clinical impact of PTEN methylation status as a prognostic marker for breast cancer.

Authors:  Amal Ramadan; Maha Hashim; Amr Abouzid; Menha Swellam
Journal:  J Genet Eng Biotechnol       Date:  2021-05-10

4.  Diagnostic value of RASSF1A methylation for breast cancer: a meta-analysis.

Authors:  Mingyi Li; Chunpeng Wang; Binbin Yu; Xueyuan Zhang; Fang Shi; Xin Liu
Journal:  Biosci Rep       Date:  2019-06-28       Impact factor: 3.840

Review 5.  DNA Methylation and Hydroxymethylation in Cervical Cancer: Diagnosis, Prognosis and Treatment.

Authors:  Hongming Zhu; He Zhu; Miao Tian; Dongying Wang; Jiaxing He; Tianmin Xu
Journal:  Front Genet       Date:  2020-04-09       Impact factor: 4.599

Review 6.  Role of DNA Methylation in the Resistance to Therapy in Solid Tumors.

Authors:  Susana Romero-Garcia; Heriberto Prado-Garcia; Angeles Carlos-Reyes
Journal:  Front Oncol       Date:  2020-08-07       Impact factor: 5.738

7.  Screening of Breast Cancer Methylation Biomarkers Based on the TCGA Database.

Authors:  Xuechun Wang; Jia Jia; Xuehong Gu; Wei-Wei Zhao; Caiping Chen; Wanxin Wu; Jiayuan Wang; Midie Xu
Journal:  Int J Gen Med       Date:  2021-12-16

8.  Assessment of DAPK1 and CAVIN3 Gene Promoter Methylation in Breast Invasive Ductal Carcinoma and Metastasis.

Authors:  Esmat Ghalkhani; Mohammad Taghi Akbari; Pantea Izadi; Habibollah Mahmoodzadeh; Fatemeh Kamali
Journal:  Cell J       Date:  2021-08-29       Impact factor: 2.479

9.  The Role of DAPK1 in the Cell Cycle Regulation of Cervical Cancer Cells and in Response to Topotecan.

Authors:  Khayal Gasimli; Monika Raab; Sven Becker; Mourad Sanhaji; Klaus Strebhardt
Journal:  J Cancer       Date:  2022-01-01       Impact factor: 4.207

10.  QuaDMutNetEx: a method for detecting cancer driver genes with low mutation frequency.

Authors:  Yahya Bokhari; Areej Alhareeri; Tomasz Arodz
Journal:  BMC Bioinformatics       Date:  2020-03-23       Impact factor: 3.169

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