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Associations of MDM2 and MDM4 Polymorphisms with Early-Stage Breast Cancer

Agnė Bartnykaitė
Aistė Savukaitytė
Rasa Ugenskienė
Monika Daukšaitė
Erika Korobeinikova
Jurgita Gudaitienė
3 and
Elona Juozaitytė
Oncology Research Laboratory, Oncology Institute, Lithuanian University of Health Sciences, LT-50161 Kaunas, Lithuania
Department of Genetics and Molecular Medicine, Hospital of Lithuanian University of Health Sciences Kaunas Clinics, LT-50161 Kaunas, Lithuania
Department of Oncology and Hematology, Hospital of Lithuanian University of Health Sciences Kaunas Clinics, LT-50161 Kaunas, Lithuania
Author to whom correspondence should be addressed.
J. Clin. Med. 2021, 10(4), 866;
Submission received: 7 January 2021 / Revised: 7 February 2021 / Accepted: 13 February 2021 / Published: 19 February 2021


Breast cancer is one of the most common cancers worldwide. Single nucleotide polymorphisms (SNPs) in MDM2 and MDM4 have been associated with various cancers. However, the influence on clinical characteristics of breast cancer has not been sufficiently investigated yet. Thus, this study aimed to investigate the relationship between SNPs in MDM2 (rs2279744, rs937283, rs937282) and MDM4 (rs1380576, rs4245739) and I–II stage breast cancer. For analysis, the genomic DNA was extracted from 100 unrelated women peripheral blood. Polymorphisms were analyzed with polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay. The study showed that MDM2 rs937283 and rs937282 were significantly associated with estrogen receptor status and human epidermal growth factor receptor 2 (HER2) status. SNPs rs1380576 and rs4245739, located in MDM4, were significantly associated with status of estrogen and progesterone receptors. Our findings suggest that rs937283 AG, rs937282 CG, rs1380576 CC, and rs4245739 AA genotypes were linked to hormonal receptor positive breast cancer and may be useful genetic markers for disease assessment.

1. Introduction

Breast cancer is one of the most common cancers among women [1,2]. Today many studies claim that breast cancer is a multifactorial disease, and the etiology of cancer is often unknown [3]. However, it has been shown that the major cause is the combination of genetic and environmental factors [1,4,5,6]. It has been demonstrated that overexpression or amplification of MDM2 and MDM4 genes are common in many malignancies, including breast cancer [7,8,9,10]. MDM2, which is mapped to chromosome 12q14.3–q15, and MDM4, which is located on chromosome 1 region q32, encode MDM2 and MDM4 proteins, respectively [11]. The evidence suggests that MDM2 and MDM4 may play significant roles in breast cancer formation, progression, prognosis, and protection from cancer [7,8,12,13,14]. The cellular processes could be related with other important protein p53, product of TP53 gene, which is a key regulator in genomic stability, cell cycle, autophagy, apoptosis, and necrosis [4,10,15]. It is known that MDM2 and MDM4 proteins perform distinct but cooperative functions in regulation of cellular p53 activity through a combination of p53 degradation and direct transcriptional squelching [16,17,18,19]. TP53 is the most common inactivated tumor suppressor gene in various human cancer types (including breast cancer). In spite of this fact, protein p53 encoded by wild-type TP53 can be functionally inactivated by abnormal structure or elevated levels of MDM2 and MDM4 [9,20,21,22,23]. Consequently, blocking MDM2 and MDM4 has been proposed as a cancer treatment strategy [24,25,26,27,28,29,30].
Several studies observed the associations between single nucleotide polymorphisms (SNPs) in MDM2 and MDM4 genes and various cancers [9,16,23,24,31,32,33,34,35,36]. In spite of this, the influence on breast cancer has not been sufficiently investigated yet. There is the lack of studies investigating the MDM2 and MDM4 associations with the clinical and morphological characteristics such as tumor size, nodal status, histologic grade, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status, proliferation rate, etc. This information might assist as prognostic factor and/or predictive marker for response to treatment of breast cancer. The prognostic and predictive strength of various characteristics is different. However, it was found that women with ER-positive breast cancer, compared to ER-negative, have a better prognosis and the treatment for positive status tumor is more efficacious. Low histologic grade as well as PR-positive and HER2-negative status is also usually associated with better breast cancer prognosis. Meanwhile, high histologic grade, high proliferation rate, and triple negative breast cancer subtype are characterized by unfavorable prognosis [37,38,39,40].
In the present study we determined the relationship between MDM2 and MDM4 SNPs and characteristics of breast tumor. The information may improve the assessment of prognostic and/or predictive value of MDM2 and MDM4 genotypes in breast cancer patients.

2. Materials and Methods

2.1. Patients

The homogeneous study group consisted of 100 unrelated Lithuanian women with a diagnosis of primary breast cancer. All women were treated in the Hospital of Lithuanian University of Health Sciences Kaunas Clinics. The age between 30 and 50 years at the time of diagnosis, early stage (I–II) of the disease and premenopausal status were preferred. All clinical and tumor pathomorphological data of the patients were obtained from the medical records with the help of the oncologists. The exclusion criteria were other malignancies, significant comorbidities, and incomplete medical documentation. Study data included the age at diagnosis, pathological tumor size (pT), status of pathological lymph node involvement (N), status of estrogen (ER) and progesterone (PR) receptors, human epidermal growth factor receptor 2 (HER2) status, tumor grade (G1 and G2, G3), progress, metastasis, and death.
The study was performed at the Oncology Research Laboratory (Oncology Institute, Lithuanian University of Health Sciences). The study was approved by Kaunas Regional Biomedical Research Ethical Committee (protocols Nr. BE-2-10 and Nr. P1-BE-2-10/2014). A written informed consent was obtained from all the participants.

2.2. DNA Isolation and Genotyping

The blood samples were collected in EDTA-containing tubes from all included subjects in 2014–2017. For SNP analysis genomic DNA was extracted from peripheral blood leukocytes with a commercially available DNA extraction kit (Thermo Fisher Scientific Baltics, Lithuania). The SNPs in MDM2 and MDM4 genes were analyzed with polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay according to a self-made protocol. PCR reactions were carried out in a total volume of 25 μL containing distilled water (dH2O), 1x DreamTaq buffer, 0.2 mM of each dNTP, DMSO, 0.24 pmol/μL of forward and reverse primers, 0.02 U DreamTaq polymerase and template DNA. Only the PCR mix for rs937283 was different and composed of distilled water (dH2O), 1x Taq buffer (with NH4), 0.2 mM of each dNTP, MgCl2, 0.4 pmol/μL of forward and reverse primers, 0.025 U Taq polymerase and template DNA. The negative control was included in each experiment to ensure the accuracy of the amplification. The primer sequences were described previously [4,5,41,42,43]. The thermal conditions and primer sequences are shown in Table 1.
DNA sequences from the amplified region of genotypes were screened manually for specific restriction enzyme sites ( and programs were used). Enzymes with predicted exclusive cutting sites in each genotype were selected and used in RFLP development and analysis (Table 2). All PCR and RFLP products were fractioned electrophoretically (5 V/cm) on a 2 or 3% agarose gel with ethidium bromide.

2.3. Statistical Analysis

Statistical analysis was performed by using SPSS (Statistical Package for the Social Sciences) version 20.0 statistical software (SPSS Inc., Chicago, IL, USA). The deviation from Hardy–Weinberg equilibrium was tested ( Pearson’s Chi-square or Monte Carlo tests were used to determine the statistical significance of the association between categorical values for each genotype group (genotype model). The odds ratios (ORs) and 95% confidence intervals (CIs) were obtained from univariate logistic regression analyses to evaluate associations between SNPs and cancer characteristics. Additionally, multivariate logistic regression analyses were performed to estimate the adjusted ORs. The multivariate analyses were conducted in three models (No. 1, No. 2 and No. 3). Model No. 1 included combination of SNPs in MDM2 or MDM4 as potential covariates. In model No. 2, the receptors as additional confounding variables were included. In model No. 3, SNPs and all analyzed breast cancer characteristics (age, status of receptors, pT, N, and G) were considered as potential covariates. The overall survival was measured from the date of diagnosis till the event—last follow-up or death. Survival plots were generated using the Kaplan–Meier method. The differences between genotypes were assessed using the log-rank test. p < 0.05 was determined as criterion for statistical significance for all executed statistical tests.

3. Results

3.1. The Distribution of the Breast Tumor Features, Alleles and Genotypes

Our analysis included 100 Lithuanian breast cancer patients and 65 of them were diagnosed over 40 years. 68% of patients had >1 mm but ≤5 mm tumors and 45% of the subjects had tumors spread to the lymph nodes. 57% of patients had positive ER expression, 48% had positive PR expression, and 22% had increased HER2 expression. In 71% of the subjects, the tumor was well or moderately differentiated (grade G1 and G2). In 31 of 100 cases, cancer had progressed, and 22% women died of breast cancer (Table 3).
Five SNPs across MDM2 (rs2279744, rs937283, rs937282) and MDM4 (rs1380576, rs4245739) genes were evaluated for associations with breast cancer phenotype. All observed genotype distributions, excluding MDM4 rs1380576 (p = 0.02), were in agreement with the Hardy–Weinberg equilibrium (p > 0.05). The allele and genotype distributions of the studied SNPs are summarized in Table 4.

3.2. The Associations of MDM2 Polymorphisms with Breast Cancer Characteristics

The results of the analysis showed some significant associations between the MDM2 polymorphisms and tumor features. The SNPs rs937283 and rs937282 were significantly associated with ER (p = 0.023 and p = 0.021, respectively) and HER2 (p = 0.010 and p = 0.033, respectively) status. Meanwhile, no significant differences were observed in the association analysis between the rs2279744 genotypes and tumor features (p > 0.05). Results are summarized in Supplementary Material Table S1. Furthermore, the effect of SNPs in MDM2 on the overall survival was analyzed but no significant associations were detected (Supplementary Material Figure S1).
As shown in Table 5, after identifying MDM2 rs937283, it was found that patients carrying the AG genotype were predisposed to higher rates of ER-positive disease (OR = 2.538, 95% CI 1.336–4.823, p = 0.004). When compared with the AA genotype, the AG was also significantly associated with decreased chances of HER2-positive breast cancer (OR = 0.231, 95% CI 0.060–0.894, p = 0.034). In addition, the logistic regression analysis revealed a significant association (OR = 2.538, 95 % CI 1.366–4.823, p = 0.004) between rs937282 CG genotype and ER-positive status (Table 5.)
We also performed a combined evaluation of rs937283 and rs937282 with status of ER and HER2 (Supplementary Material Table S2). However, the analysis of a possible joint effect did not reveal significant results. Following the adjustment for more confounding variables (models No. 2 and No. 3), the associations also remained non-significant.

3.3. The Associations of MDM4 Polymorphisms with Breast Cancer Characteristics

Both SNPs, located in MDM4, rs1380576 and rs4245739, showed statistically significant associations with ER (p = 0.005 and p = 0.010, respectively) and PR (p = 0.010 and p = 0.016, respectively) status. Results are summarized in Supplementary Material Table S1. Results also showed that SNPs in MDM4 were not statistically associated with overall survival (Supplementary Material Figure S1).
Individuals having the rs1380576 CC genotype had an OR of 2.263 (95% CI 1.319–3.883, p = 0.003) of an ER-positive status compared to individual having the GG genotype. Moreover, CC genotype was significantly associated with the PR-positive disease (OR = 12.462, 95% CI 1.486–104.514, p = 0.020) (Table 6).
As indicated in Table 6, we found that rs4245739 AA genotype was significantly associated with ER-positive breast cancer in comparison with CC genotype (OR = 1.913, 95% CI 1.155–3.168, p = 0.012). After logistic regression analysis, no reliable differences were found between rs4245739 polymorphism genotype and PR-positive status (p > 0.05).
However, according to results of multivariate logistic regression analysis, all investigated associations resulted in loss of significance (Supplementary Material Table S2). The results suggested that in this case other factors were more important.

4. Discussion

Breast cancer is one of the most common cancers affecting females worldwide [2]. The etiology and pathogenesis of breast cancer is complicated [6]. Various risk factors, including genetic predisposition, have been characterized, but the accurate molecular mechanisms of breast cancer cause and characteristics are still unclear [23]. Additionally, some studies predicate that overexpression/amplification of MDM2 and MDM4 are common in many malignancies [7,8,9,17]. Genetic variations (for example, SNPs) in the MDM2 and MDM4 genes may also be associated with breast cancer. Moreover, it is established that TP53 pathway is one of the central pathways involved in various human cancers, including breast cancer [23,44,45]. Many studies have shown that MDM2 and MDM4 proteins are also involved in TP53 pathway by performing cooperative functions in negatively regulating p53 protein, which is the key regulator in essential cellular processes [16,17,19].
In our study we investigated the MDM2 (rs2279744, rs937283, rs937282) and MDM4 (rs1380576, rs4245739) single nucleotide polymorphisms in 100 Lithuanian breast cancer patients. We investigated the relationship of MDM2 and MDM4 polymorphisms genotype with the clinicopathological features, including the patient’s age at diagnosis, pathological tumor size (pT), pathological lymph node involvement (N), status of estrogen (ER), progesterone (PR) receptors and human epidermal growth factor receptor 2 (HER2), tumor grade (G1 and G2, G3), progress, metastasis, and death. For comparisons of all genotype and associated features, logistic regression analysis was used to determine odds ratios and p values. Our results revealed that some SNPs of MDM2 and MDM4 might be significantly associated with the breast cancer characteristics. To the best of our knowledge, this is the first study to examine the role of MDM2 and MDM4 polymorphisms in breast cancer patients in Lithuania.
As the data of our study shows, associations for MDM2 and MDM4 SNPs with age at breast cancer diagnosis, pathological tumor size, lymph node involvement status, histological grade, progression, metastasis, and death were evaluated but no significant associations were found. In addition, no significant associations between SNPs and overall survival were detected.
MDM2 rs2279744 polymorphism is located in the promoter region. It has been shown to increase the affinity of the stimulatory protein 1 (Sp1, transcriptional activator), resulting in higher levels of MDM2 mRNA and protein [46]. In our study, rs2279744 polymorphism did not show any significant association with analyzed breast cancer characteristics. Like our study, Miedl et al. [47] have reported no significant associations of rs2279744 with breast cancer features (age, menopausal status, ER, PR, and HER2 status, stage, grade, and tumor size). Similarly, Yilmaz et al. [48] did not find any relationships among tumor grade, tumor size, lymph node involvement, metastasis status, and this polymorphism. In contrast, Yadav with colleagues [23] have found a significant correlation between rs2279744 and HER2/neu-positive status and distant metastasis (p = 0.003 and p = 0.04, respectively) in breast cancer. Furthermore, analyzing control and breast cancer cohorts, Paulin et al. [36] indicated that rs2279744 was associated with tumor grade and nodal involvement. Their study revealed that GG genotype was associated with high grade tumors (OR = 1.64, 95% CI = 1.06–2.53, p = 0.025) and greater nodal involvement (OR = 2.51, 95% CI = 1.26–4.98, p = 0.009). Moreover, in concordance with our findings, they report that rs2279744 was not associated with age at diagnosis of breast cancer, tumor ER, PR, HER2 status, and menopausal status (p > 0.05).
rs937283 is located in the 5′ untranslated region (UTR). This genetic variant enhances the transcription activity of the MDM2 gene thereby increases the mRNA and protein expression levels of MDM2 [35]. Meanwhile, rs937282 is located in the promoter region of MDM2. Similarly to rs2279744, rs937282 can affect the affinity of CAAT/enhancer binding protein α (C/EBP α), resulting in higher expression of MDM2 [33]. In the present study, we found the relationships between these two SNPs of MDM2 (rs937283 and rs937282) and ER, HER2 status. We observed that heterozygous genotypes were significantly associated with ER-positive breast cancer. The analysis also revealed a statistically significant association between the rs937283 AG genotype and decreased chances of HER2-positive breast cancer. It is known that ER receptor positive breast cancer is normally associated with much better prognosis. Consequently, our results suggest that heterozygous genotypes of rs937283 and rs937282 polymorphisms could indicate better prognosis for breast cancer patients. Literature sources presented only the data of these SNPs and their influence on the risk of various cancers, but there was no evidence that rs937283 and rs937282 had been analyzed with breast cancer characteristics. Some correlations with other types of tumors have been recently identified. For example, Jiao et al. [35] studied association of rs937283 with clinicopathological characteristics (gender, age at diagnosis, family history, invasion, aggression, and lag-time) in retinoblastoma patients, and significant associations with tumor invasion and high aggression were identified.
MDM4 rs1380576 is the intronic variant and its exact functional changes remain unknown. We have observed that rs1380576 polymorphism was significantly associated with ER and PR status. In the present study, we found that patients with CC genotype were linked to positive status of ER and PR. Conversely, Hashemi et al. [5] did not show significant associations between rs1380576 and status of ER and PR (p > 0.05). In agreement with our study, they have also reported no significant relationship between rs1380576 and other clinical breast cancer characteristics such as age, tumor size, grade, stage, histological type, HER2 status. There is the lack of studies investigating the rs1380576 associations with the clinical and morphological characteristics of breast cancer. Consequently, further studies concerning the role of this SNP may be considered.
rs4245739 polymorphism is located in the 3′ untranslated region (UTR) of the MDM4 gene, affects mRNA stability and proteins level [31]. In this study, we confirmed the impact of MDM4 rs4245739 on ER status, and we consider that AA genotype could indicate better breast cancer prognosis. Our findings for rs4245739 and ER status concur with those of Bauer et al. [17]. They analyzed rs4245739 in 815 breast cancer patients and results showed that AA genotype was associated with a 1.8-fold increased probability to develop an ER-positive tumor (p = 0.042). We discovered AA genotype to be associated with ER-positive status with OR = 1.913. What is more, according to Purrington et al. [49], rs4245739 C allele was related to ER-negative status (OR = 1.14, 95 % CI 1.10–1.18, p = 2.1 × 10−12). Meanwhile, Milne et al. [50] in their study claimed that rs4245739 was not associated with ER-positive disease. Moreover, Pedram et al. [51] (Azerbaijan population) and Hashemi et al. [5] (Southeast Iranian population) found that MDM4 rs4245739 had no significant association with breast cancer clinicopathological factors, such as age, involved breast side, tumor size and type, grade of tumor, count of involved lymph nodes and stage of cancer, status of receptors. Liu with colleagues [4] reported no associations with age, age at menarche, menstrual history, or family history of breast cancer.
We have to admit that this is the first analysis of polymorphisms in the MDM2 and MDM4 genes in relation to the breast cancer clinical characteristics in Lithuania; consequently, a much larger cohort of breast cancer patients would be required to verify our findings.

5. Conclusions

This study provides basic information about the genotype frequency distributions of MDM2 rs2279744, rs937283, rs937282 and MDM4 rs1380576, rs4245739 polymorphisms in Lithuanian population. Our results suggest that heterozygous genotypes of rs937283 and rs937282, as well as rs1380576 CC and rs4245739 AA genotypes, are associated with early-stage hormonal receptor positive breast cancer and may be useful genetic markers for disease assessment. This information may improve patient stratification in the future; however, further large and functional studies are needed to assess the validity of these associations.

Supplementary Materials

The following are available online at, Table S1: Associations between genotype of each polymorphism with clinicopathological features of breast cancer. Table S2: Multivariate logistic regression analysis of the combined SNPs in MDM2 and MDM4 genes. Figure S1. Kaplan–Meier estimates of overall survival: (a) rs2279744, (b) rs937283, (c) rs937282, (d) rs1380576, (e) rs4245739.

Author Contributions

Conceptualization, A.B., A.S., R.U., E.J.; methodology, A.B., A.S., M.D.; formal analysis, A.B.; investigation, A.B., M.D.; data curation, E.K., J.G.; writing—original draft preparation, A.B., A.S.; writing—review and editing, R.U., E.K., E.J., J.G., M.D.; visualization, A.B.; supervision, R.U., E.J. All authors have read and agreed to the published version of the manuscript.


This research was supported in part by funding from Lithuanian University of Health Sciences (LSMU) and European Social Fund [project No.09.3.3-LMT-K-712] under grant agreement with the Research Council of Lithuania (LMTLT).

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of Kaunas Regional Biomedical Research Ethical Committee (protocol codes BE-2-10 and P1-BE-2-10/2014).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.


We are grateful to the patients for their participation in this research.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.


  1. Dai, X.; Li, T.; Bai, Z.; Yang, Y.; Liu, X.; Zhan, J.; Shi, B. Breast cancer intrinsic subtype classification, clinical use and future trends. Am. J. Cancer Res. 2015, 5, 2929–2943. [Google Scholar] [PubMed]
  2. Coughlin, S.S.; Ekwueme, D.U. Breast cancer as a global health concern. Cancer Epidemiol. 2009, 33, 315–318. [Google Scholar] [CrossRef]
  3. Golubnitschaja, O.; Debald, M.; Yeghiazaryan, K.; Kuhn, W.; Pešta, M.; Costigliola, V.; Grech, G. Breast cancer epidemic in the early twenty-first century: Evaluation of risk factors, cumulative questionnaires and recommendations for preventive measures. Tumor Biol. 2016, 37, 12941–12957. [Google Scholar] [CrossRef]
  4. Liu, J.; Tang, X.; Li, M.; Lu, C.; Shi, J.; Zhou, L.; Yuan, Q.; Yang, M. Functional MDM4 rs4245739 genetic variant, alone and in combination with P53 Arg72Pro polymorphism, contributes to breast cancer susceptibility. Breast Cancer Res. Treat. 2013, 140, 151–157. [Google Scholar] [CrossRef]
  5. Hashemi, M.; Sanaei, S.; Hashemi, S.M.; Eskandari, E.; Bahari, G. Association of Single Nucleotide Polymorphisms of the MDM4 Gene with the Susceptibility to Breast Cancer in a Southeast Iranian Population Sample. Clin. Breast Cancer 2018, 18, e883–e891. [Google Scholar] [CrossRef]
  6. Rudolph, A.; Chang-Claude, J.; Schmidt, M.K. Gene–environment interaction and risk of breast cancer. Br. J. Cancer 2016, 114, 125–133. [Google Scholar] [CrossRef] [Green Version]
  7. Yu, Q.; Li, Y.; Mu, K.; Li, Z.; Meng, Q.; Wu, X.; Wang, Y.; Li, L. Amplification of Mdmx and overexpression of MDM2 contribute to mammary carcinogenesis by substituting for p53 mutations. Diagn. Pathol. 2014, 9, 71. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Haupt, S.; Vijayakumaran, R.; Miranda, P.J.; Burgess, A.; Lim, E.; Haupt, Y. The role of MDM2 and MDM4 in breast cancer development and prevention. J. Mol. Cell Biol. 2017, 9, 53–61. [Google Scholar] [CrossRef] [Green Version]
  9. Karni-Schmidt, O.; Lokshin, M.; Prives, C. The roles of MDM2 and MDMX in cancer. Annu. Rev. Pathol. 2016, 11, 617–644. [Google Scholar] [CrossRef] [PubMed]
  10. Yu, H.; Wang, L.-E.; Liu, Z.; Wei, S.; Li, G.; Sturgis, E.M.; Wei, Q. Polymorphisms of MDM4 and risk of squamous cell carcinoma of the head and neck. Pharm. Genom. 2011, 21, 388–396. [Google Scholar] [CrossRef] [Green Version]
  11. Mendoza, M.; Mandani, G.; Momand, J. The MDM2 gene family. Biomol. Concepts 2014, 5, 9–19. [Google Scholar] [CrossRef] [Green Version]
  12. Gao, C.; Xiao, G.; Piersigilli, A.; Gou, J.; Ogunwobi, O.; Bargonetti, J. Context-dependent roles of MDMX (MDM4) and MDM2 in breast cancer proliferation and circulating tumor cells. Breast Cancer Res. 2019, 21, 5. [Google Scholar] [CrossRef]
  13. Li, Q.; Lozano, G. Molecular Pathways: Targeting Mdm2 and Mdm4 in Cancer Therapy. Clin. Cancer Res. 2013, 19, 34–41. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Swetzig, W.M.; Wang, J.; Das, G.M. Estrogen receptor alpha (ERα/ESR1) mediates the p53-independent overexpression of MDM4/MDMX and MDM2 in human breast cancer. Oncotarget 2016, 7, 16049–16069. [Google Scholar] [CrossRef] [Green Version]
  15. Silwal-Pandit, L.; Vollan, H.K.M.; Chin, S.-F.; Rueda, O.M.; McKinney, S.; Osako, T.; Quigley, D.A.; Kristensen, V.N.; Aparicio, S.; Børresen-Dale, A.-L.; et al. TP53 Mutation Spectrum in Breast Cancer Is Subtype Specific and Has Distinct Prognostic Relevance. Clin. Cancer Res. 2014, 20, 3569–3580. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Wang, M.-J.; Luo, Y.-J.; Shi, Z.-Y.; Xu, X.-L.; Yao, G.-L.; Liu, R.-P.; Zhao, H. The associations between MDM4 gene polymorphisms and cancer risk. Oncotarget 2016, 7, 55611–55623. [Google Scholar] [CrossRef] [Green Version]
  17. Bauer, M.; Kantelhardt, E.J.; Stiewe, T.; Nist, A.; Mernberger, M.; Politt, K.; Hanf, V.; Lantzsch, T.; Uleer, C.; Peschel, S.; et al. Specific allelic variants of SNPs in the MDM2 and MDMX genes are associated with earlier tumor onset and progression in Caucasian breast cancer patients. Oncotarget 2019, 10, 1975–1992. [Google Scholar] [CrossRef] [Green Version]
  18. Junttila, M.R.; Evan, G.I. p53—A Jack of all trades but master of none. Nat. Rev. Cancer 2009, 9, 821–829. [Google Scholar] [CrossRef]
  19. Haupt, S.; Buckley, D.L.; Pang, J.-M.B.; Panimaya, J.; Paul, P.J.; Gamell, C.; Takano, E.A.; Lee, Y.Y.; Hiddingh, S.; Rogers, T.-M.; et al. Targeting Mdmx to treat breast cancers with wild-type p53. Cell Death Dis. 2015, 6, e1821. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  20. Wade, M.; Li, Y.C.; Wahl, G.M. MDM2, MDMX and p53 in oncogenesis and cancer therapy. Nat. Rev. Cancer 2013, 13, 83–96. [Google Scholar] [CrossRef] [Green Version]
  21. Marine, J.-C. MDM2 and MDMX in Cancer and Development; Elsevier: Amsterdam, The Netherlands, 2011; Volume 94, pp. 45–75. [Google Scholar]
  22. Gansmo, L.B.; Bjørnslett, M.; Halle, M.K.; Salvesen, H.B.; Dørum, A.; Birkeland, E.; Hveem, K.; Romundstad, P.; Vatten, L.; Lønning, P.E.; et al. The MDM4 SNP34091 (rs4245739) C-allele is associated with increased risk of ovarian-but not endometrial cancer. Tumor Biol. 2016, 37, 10697–10702. [Google Scholar] [CrossRef] [Green Version]
  23. Yadav, P.; Masroor, M.; Tanwer, K.; Mir, R.; Javid, J.; Ahmad, I.; Zuberi, M.; Kaza, R.C.M.; Jain, S.K.; Khurana, N.; et al. Clinical significance of TP53 (R72P) and MDM2 (T309G) polymorphisms in breast cancer patients. Clin. Transl. Oncol. 2015, 18, 728–734. [Google Scholar] [CrossRef]
  24. Haupt, S.; Mejía-Hernández, J.O.; Vijayakumaran, R.; Keam, S.P.; Haupt, Y. The long and the short of it: The MDM4 tail so far. J. Mol. Cell Biol. 2019, 11, 231–244. [Google Scholar] [CrossRef] [PubMed]
  25. Miranda, P.J.; Buckley, D.; Raghu, D.; Pang, J.-M.B.; Takano, E.A.; Vijayakumaran, R.; Teunisse, A.F.; Posner, A.; Procter, T.; Herold, M.J.; et al. MDM4 is a rational target for treating breast cancers with mutant p53. J. Pathol. 2017, 241, 661–670. [Google Scholar] [CrossRef]
  26. Xiong, S.; Pant, V.; Zhang, Y.; Aryal, N.K.; You, M.J.; Kusewitt, D.; Lozano, G. The p53 inhibitor Mdm4 cooperates with multiple genetic lesions in tumourigenesis. J. Pathol. 2017, 241, 501–510. [Google Scholar] [CrossRef]
  27. Burgess, A.; Chia, K.M.; Haupt, S.; Thomas, D.; Haupt, Y.; Lim, E. Clinical Overview of MDM2/X-Targeted Therapies. Front. Oncol. 2016, 6, 7. [Google Scholar] [CrossRef]
  28. Portman, N.; Milioli, H.H.; Alexandrou, S.; Coulson, R.; Yong, A.; Fernandez, K.J.; Chia, K.M.; Halilovic, E.; Segara, D.; Parker, A.; et al. MDM2 inhibition in combination with endocrine therapy and CDK4/6 inhibition for the treatment of ER-positive breast cancer. Breast Cancer Res. 2020, 22, 1–17. [Google Scholar] [CrossRef] [PubMed]
  29. Gupta, A.; Shah, K.; Oza, M.J.; Behl, T. Reactivation of p53 gene by MDM2 inhibitors: A novel therapy for cancer treatment. Biomed. Pharmacother. 2019, 109, 484–492. [Google Scholar] [CrossRef] [PubMed]
  30. Liu, Y.; Wang, X.; Wang, G.; Yang, Y.; Yuan, Y.; Ouyang, L. The past, present and future of potential small-molecule drugs targeting p53-MDM2/MDMX for cancer therapy. Eur. J. Med. Chem. 2019, 176, 92–104. [Google Scholar] [CrossRef]
  31. Xu, C.; Zhu, J.; Fu, W.; Liang, Z.; Song, S.; Zhao, Y.; Lyu, L.; Zhang, A.; He, J.; Duan, P. MDM4 rs4245739 A > C polymorphism correlates with reduced overall cancer risk in a meta-analysis of 69477 subjects. Oncotarget 2016, 7, 71718–71726. [Google Scholar] [CrossRef] [Green Version]
  32. Wynendaele, J.; Böhnke, A.; Leucci, E.; Nielsen, S.J.; Lambertz, I.; Hammer, S.; Sbrzesny, N.; Kubitza, D.; Wolf, A.; Gradhand, E.; et al. An Illegitimate microRNA Target Site within the 3′ UTR of MDM4 Affects Ovarian Cancer Progression and Chemosensitivity. Cancer Res. 2010, 70, 9641–9649. [Google Scholar] [CrossRef] [Green Version]
  33. Wang, M.; Zhang, Z.; Zhu, H.; Fu, G.; Wang, S.; Wu, D.; Zhou, J.; Wei, Q.; Zhang, Z. A Novel Functional Polymorphism C1797G in the MDM2 Promoter Is Associated with Risk of Bladder Cancer in a Chinese Population. Clin. Cancer Res. 2008, 14, 3633–3640. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Chen, B.; Wang, J.; Wang, J.; Zhang, J.; Gu, X.; Feng, X. The Study of MDM2 rs937283 Variant and Cancer Susceptibility in a Central Chinese Population. Technol. Cancer Res. Treat. 2018, 17, 1–7. [Google Scholar] [CrossRef] [PubMed]
  35. Jiao, Y.; Jiang, Z.; Wu, Y.; Chen, X.; Xiao, X.; Yu, H. A Functional Polymorphism (rs937283) in the MDM2 Promoter Region is Associated with Poor Prognosis of Retinoblastoma in Chinese Han Population. Sci. Rep. 2016, 6, 1–11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Paulin, F.E.M.; O’Neill, M.; McGregor, G.; Cassidy, A.; Ashfield, A.; Ali, C.W.; Munro, A.J.; Baker, L.; A Purdie, C.; Lane, D.P.; et al. MDM2 SNP309 is associated with high grade node positive breast tumours and is in linkage disequilibrium with a novel MDM2 intron 1 polymorphism. BMC Cancer 2008, 8, 281. [Google Scholar] [CrossRef] [Green Version]
  37. Győrffy, B.; Hatzis, C.; Sanft, T.; Hofstatter, E.; Aktas, B.; Pusztai, L. Multigene prognostic tests in breast cancer: Past, present, future. Breast Cancer Res. 2015, 17, 1–7. [Google Scholar] [CrossRef] [Green Version]
  38. Nechuta, S.; Chen, W.Y.; Cai, H.; Poole, E.M.; Kwan, M.L.; Flatt, S.W.; Patterson, R.E.; Pierce, J.P.; Caan, B.J.; Shu, X.O. A pooled analysis of post-diagnosis lifestyle factors in association with late estrogen-receptor-positive breast cancer prognosis. Int. J. Cancer 2015, 138, 2088–2097. [Google Scholar] [CrossRef] [Green Version]
  39. Sotiriou, C.; Pusztai, L. Gene-Expression Signatures in Breast Cancer. N. Engl. J. Med. 2009, 360, 790–800. [Google Scholar] [CrossRef] [Green Version]
  40. Møller, H.; Henson, K.; Lüchtenborg, M.; Broggio, J.; Charman, J.; Coupland, V.H.; Davies, E.; Jack, R.H.; Sullivan, R.; Vedsted, P.; et al. Short-term breast cancer survival in relation to ethnicity, stage, grade and receptor status: National cohort study in England. Br. J. Cancer 2016, 115, 1408–1415. [Google Scholar] [CrossRef] [Green Version]
  41. Xu, B.; Xu, Z.; Cheng, G.; Min, Z.-C.; Mi, Y.; Zhang, Z.-Z.; Tao, J.; Li, P.-C.; Wang, M.-L.; Tang, J.-L.; et al. Association between polymorphisms of TP53 and MDM2 and prostate cancer risk in southern Chinese. Cancer Genet. Cytogenet. 2010, 202, 76–81. [Google Scholar] [CrossRef]
  42. Jin, L.; Xu, L.; Song, X.; Wei, Q.; Sturgis, E.M.; Li, G. Genetic Variation in MDM2 and p14ARF and Susceptibility to Salivary Gland Carcinoma. PLoS ONE 2012, 7, e49361. [Google Scholar] [CrossRef] [Green Version]
  43. Tas, A.; Atabey, M.; Caglayan, G.; Bostanci, M.E.; Bolukbasi, S.S.; Topcu, O.; Silig, Y. Investigation of the association between the MDM2 T309G polymorphism and gastric cancer. Biomed. Rep. 2017, 7, 469–473. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Bykov, V.J.N.; Eriksson, S.E.; Bianchi, J.; Wiman, K.G. Targeting mutant p53 for efficient cancer therapy. Nat. Rev. Cancer 2018, 18, 89–102. [Google Scholar] [CrossRef] [PubMed]
  45. Basu, S.; Murphy, M.E. Genetic Modifiers of the p53 Pathway. Cold Spring Harb. Perspect. Med. 2016, 6, a026302. [Google Scholar] [CrossRef]
  46. Lalonde, M.-E.; Ouimet, M.; Larivière, M.; A Kritikou, E.; Sinnett, D. Identification of functional DNA variants in the constitutive promoter region of MDM2. Hum. Genom. 2012, 6, 15. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Miedl, H.; Lebhard, J.; Ehart, L.; Schreiber, M. Association of the MDM2 SNP285 and SNP309 genetic variants with the risk, age at onset and prognosis of breast cancer in central European women: A hospital-based case-control study. Int. J. Mol. Sci. 2019, 20, 509. [Google Scholar] [CrossRef] [Green Version]
  48. Yilmaz, M.; Tas, A.; Donmez, G.; Kacan, T.; Silig, Y. Significant association of the MDM2 T309G polymorphism with breast cancer risk in a Turkish population. Asian Pac. J. Cancer Prev. 2018, 19, 1059–1062. [Google Scholar] [CrossRef] [PubMed]
  49. Purrington, K.S.; Slager, S.; Eccles, D.; Yannoukakos, D.; Fasching, P.A.; Miron, P.; Carpenter, J.; Chang-Claude, J.; Martin, N.G.; Montgomery, G.W.; et al. Genome-wide association study identifies 25 known breast cancer susceptibility loci as risk factors for triple-negative breast cancer. Carcinogenesis 2014, 35, 1012–1019. [Google Scholar] [CrossRef] [Green Version]
  50. Milne, R.L.; Kuchenbaecker, K.B.; Michailidou, K.; Beesley, J.; Kar, S.; Lindström, S.; Hui, S.; Lemaçon, A.; Soucy, P.; Lemaçon, A.; et al. Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer. Nat. Genet. 2017, 49, 1767–1778. [Google Scholar] [CrossRef] [Green Version]
  51. Pedram, N.; Pouladi, N.; Hosseinpour Feizi, M.A.; Montazeri, V.; Sakhinia, E.; Estiar, M.A. Analysis of the Association between MDM4 rs4245739 Single Nucleotide Polymorphism and Breast Cancer Susceptibility. Clin. Lab. 2016, 59, 1303–1308. [Google Scholar] [CrossRef]
Table 1. The SNP and thermal conditions for analysis.
Table 1. The SNP and thermal conditions for analysis.
Gene, SNPPrimer Sequence
F-Forward, R-Reverse
Thermal ConditionsFragments, bp
5 min of denaturation at 95 °C, then 35 cycles of 95 °C for 30 s, 63 °C for 30 s, and 72 °C for 30 s. The final cycle had a 7 min extension at 72 °C157
5 min of denaturation at 95 °C, then 35 cycles of 95 °C for 30 s, 60 °C for 30 s, and 72 °C for 30 s. The final cycle had a 7 min extension at 72 °C617
5 min of denaturation at 95 °C, then 35 cycles of 95 °C for 30 s, 57,5 °C for 30 s, and 72 °C for 30 s. The final cycle had a 7 min extension at 72 °C231
5 min of denaturation at 94 °C, then 35 cycles of 94 °C for 30 s, 56,6 °C for 45 s, and 72 °C for 30 s. The final cycle had a 10 min extension at 72 °C195
3 min of denaturation at 94 °C, then 35 cycles of 94 °C for 30 s, 51 °C for 30 s, and 72 °C for 30 s. The final cycle had a 10 min extension at 72 °C134
Nucleotide base in underlined letter is mismatched base added in the primer sequence to create restriction enzyme site.
Table 2. The SNP and restriction conditions for analysis.
Table 2. The SNP and restriction conditions for analysis.
Gene and SNPRestriction EndonucleaseRestriction ConditionsFragments, bp
MDM2 rs2279744MspA1I37 °C for 1 hT allele 157;
G allele 109 + 48
MDM2 rs937283MboI37 °C for 1–16 hA allele 437 + 87 + 86 + 7;
G allele 524 + 86 + 7
MDM2 rs937282Hpy188I37 °C for 1 hC allele 231;
G allele 182 + 49
MDM4 rs1380576BseNI (BsrI)65 °C for 1–16 hG allele 195;
C allele 172 + 23
MDM4 rs4245739MspI (HpaII)37 °C for 1–16 hC allele 110 + 24;
A allele 134
Table 3. The clinicopathological characteristics of the study group.
Table 3. The clinicopathological characteristics of the study group.
CharacteristicsFrequencies (%)
30–40 years35
41–50 years65
Pathological tumor size (pT)
Pathological lymph node involvement (N)
negative (N0)55
positive (N1)45
Estrogen receptor (ER)
Progesterone receptor (PR)
Human epidermal growth factor receptor 2 (HER2)
Tumor grade (G)
G1 and G271
T1a—>1 mm but ≤5 mm, T1b—>5 mm but ≤10 mm across, G1—well differentiated (low grade), G2—moderately differentiated (intermediate grade), G3—poorly differentiated.
Table 4. Allele and genotype distribution of MDM2 and MDM4 polymorphisms in the study group.
Table 4. Allele and genotype distribution of MDM2 and MDM4 polymorphisms in the study group.
PolymorphismAllele Frequency
(European Population Allele Frequencies from 1000 Genomes Project Database)
Genotype Frequency
MDM2 rs2279744
MDM2 rs937283
MDM2 rs937282
MDM4 rs1380576
MDM4 rs4245739
Table 5. Odds ratio for associations of rs937283 A > G and rs937282 C > G with tumor characteristics.
Table 5. Odds ratio for associations of rs937283 A > G and rs937282 C > G with tumor characteristics.
SNPGenotypePositive ERPositive HER2
OR95% CIpOR95% CIp
MDM2 rs937283AA1.00(ref.)-1.00(ref.)-
MDM2 rs937282CC1.00(ref.)-1.00(ref.)-
Table 6. Odds ratio for associations of rs1380576 G>C and rs4245739 C>A with tumor characteristics.
Table 6. Odds ratio for associations of rs1380576 G>C and rs4245739 C>A with tumor characteristics.
SNPGenotypePositive ERPositive PR
OR95% CIpOR95% CIp
MDM4 rs1380576GG1.00(ref.)-1.00(ref.)-
MDM4 rs4245739CC1.00(ref.)-1.00(ref.)-
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Bartnykaitė, A.; Savukaitytė, A.; Ugenskienė, R.; Daukšaitė, M.; Korobeinikova, E.; Gudaitienė, J.; Juozaitytė, E. Associations of MDM2 and MDM4 Polymorphisms with Early-Stage Breast Cancer. J. Clin. Med. 2021, 10, 866.

AMA Style

Bartnykaitė A, Savukaitytė A, Ugenskienė R, Daukšaitė M, Korobeinikova E, Gudaitienė J, Juozaitytė E. Associations of MDM2 and MDM4 Polymorphisms with Early-Stage Breast Cancer. Journal of Clinical Medicine. 2021; 10(4):866.

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Bartnykaitė, Agnė, Aistė Savukaitytė, Rasa Ugenskienė, Monika Daukšaitė, Erika Korobeinikova, Jurgita Gudaitienė, and Elona Juozaitytė. 2021. "Associations of MDM2 and MDM4 Polymorphisms with Early-Stage Breast Cancer" Journal of Clinical Medicine 10, no. 4: 866.

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