Association of MDM2 rs2279744 and rs937282 polymorphisms with breast cancer susceptibility and circulating MDM2 and p53 levels in Bangladeshi women: A case-control study

Abstract

Background

The MDM2–p53 signaling pathway plays a central role in tumor suppression, and genetic variants that disrupt this pathway may influence breast cancer (BC) susceptibility. However, data from South Asian populations, particularly Bangladesh, remain limited.

Methods

A case–control study was conducted in Bangladeshi women, including BC patients and healthy controls (HCs). Genotyping of MDM2 polymorphisms was performed using PCR-based methods. Circulating MDM2 and p53 protein levels were measured using enzyme-linked immunosorbent assays (ELISA). Associations between genotype, protein levels, BC status, and clinicopathological features were evaluated using appropriate statistical models.

Results

Women carrying the heterozygous TG genotype had a markedly increased risk of BC across additive, dominant, and over-dominant models, whereas the GG genotype showed a protective effect under the recessive model. In contrast, rs937282 did not show a significant association with BC risk. Circulating MDM2 levels were significantly elevated in patients compared with controls and varied by rs2279744 genotype, while circulating p53 levels showed an opposite trend. A strong inverse correlation was observed between serum MDM2 and p53 levels, suggesting a potential association with the MDM2–p53 regulatory pathway. Elevated MDM2 levels were also noted in HER2-positive and triple-positive BC subtypes.

Conclusion

Together, these findings suggest that the MDM2 rs2279744 polymorphism is associated with breast cancer susceptibility and genotype-specific differences in circulating MDM2 and p53 levels. However, the absence of functional validation limits direct causal inference.

Introduction

Breast cancer is one of the most common types of cancer and the main cause of cancer-related death in women around the world [1]. Every year, more than two million new cases are diagnosed [2,3]. Breast and cervical cancers make up almost 40% of all female cancers in Bangladesh [4,5]. This is mostly because they are diagnosed late and there are few screening options [6]. This shows how important it is to find genetic risk factors. Genetic factors are very important when it comes to breast cancer risk, even though lifestyle and reproductive factors also play a role [7,8]. In addition to high-risk mutations like BRCA1 and BRCA2, prevalent single nucleotide polymorphisms may modify gene regulation in critical pathways governing cell cycle progression, DNA repair, and apoptosis, thereby increasing susceptibility to breast cancer [8,9].

Mouse double minute 2 (MDM2) is a major controller of the tumor suppressor p53. It keeps the genome stable by controlling cell-cycle arrest, DNA repair, and apoptosis [10–12]. MDM2 normally keeps p53 from working too hard, but when it is overexpressed, it can stop p53 from working and help genetically unstable cells survive, which can lead to cancer [13–18].

Regulatory single nucleotide polymorphisms (SNPs), especially those in promoter and untranslated regions, usually change how much a gene is expressed instead of turning it on or off [19]. These kinds of variants can change transcriptional dynamics, allelic balance, and regulatory noise, which can change signaling pathways in a non-linear way that depends on the genotype [20]. The p53–MDM2 feedback loop is very tightly controlled, so even small changes in MDM2 expression can have a big effect on p53 activity. This means that heterozygous and homozygous variants may have different biological effects [15].

There are two SNPs in MDM2 that are very interesting. The first one, rs2279744 (also known as SNP309, T > G), is in the promoter region of the gene [21]. The G allele enhances the binding affinity for the transcription factor Sp1, leading to elevated MDM2 expression and diminished p53 activity [22,23]. Numerous studies conducted in Asian and European populations have demonstrated that this variant correlates with an increased risk of various cancers, including breast cancer. The magnitude and orientation of this risk, however, fluctuate based on ethnicity and study design [24,25]. The second variant, rs937282 (C > G), is found in the 5′ untranslated region (5′UTR) of MDM2 [26]. This area is important for controlling the stability and efficiency of mRNA translation. Consequently, modifications in this context may affect MDM2 protein concentrations and impact tumor biology [27]. Some studies have found that rs937282 may raise the risk of cancer, while others have found little or no link. This shows that more research is needed for specific populations [28–30].

In addition to genetic susceptibility, the levels of MDM2 and p53 proteins in the blood have been found to be possible markers of p53 pathway dysregulation [31]. In numerous malignancies, including breast cancer, elevated MDM2 levels and altered p53 concentrations in serum have been documented [32]. Nonetheless, the correlation among MDM2 polymorphisms, circulating protein concentrations, and tumor biological traits, including hormone receptor status, is not fully elucidated. Breast cancer is a diverse disease characterized by distinct molecular subtypes delineated by estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status [33]. New evidence suggests that the way oncogenic signaling pathways and the p53–MDM2 axis interact may be different in receptor-defined subtypes [34].

Due to the scarcity of data from South Asian populations, especially Bangladesh, this study sought to examine the correlation between two regulatory MDM2 polymorphisms (rs2279744 and rs937282) and breast cancer susceptibility in Bangladeshi women. We also looked at how circulating levels of MDM2 and p53 vary depending on genotype, how they relate to different types of BC receptors, and how MDM2 and p53 relate to each other. This study aims to enhance the understanding of the association between MDM2 polymorphisms, circulating MDM2 and p53 levels, and breast cancer risk in the Bangladeshi population by integrating genetic, molecular, and clinical data.

Materials and methods

Study subjects

All participants underwent thorough physical, neurological, and laboratory evaluations to confirm the absence of any significant physical or neurological conditions. HCs and BC patients were recruited from Dhaka, Bangladesh. The study included 112 women with BC and 124 age-sex matched HCs, enrolled between April 1, 2023, and June 30, 2023. This was an exploratory pilot study; therefore, the sample size was determined by feasibility and availability of eligible subjects during the study period, and an a priori power calculation was not performed. BC patients were recruited from National Cancer Research Institute Hospital in Mohakhali, Dhaka, utilizing well-equipped inpatient and outpatient facilities for assessments. A qualified oncologist diagnosed BC patients using standard diagnostic criteria and evaluated the HCs. Demographic and clinical data were collected, and genetic analysis was performed on blood samples. Randomized selection ensured unbiased sampling, while laboratory personnel were blinded to case-control status to minimize bias. Genotyping analysis was conducted at the Rufaida BioMeds Laboratory in Dhaka, Bangladesh. To ensure participant confidentiality, the authors had no access to personally identifiable information during or after the data collection.

DNA isolation & genotyping

The blood samples were collected from all the participants and filled in the EDTA containing tubes. For the SNP analysis genomic DNA was extracted from peripheral blood leukocytes with the help of commercially available genomic DNA extraction kit (Favorgen, USA) following the given protocol. In brief, blood samples were lysed and then transferred into the binding column tubes with utmost care. The collected DNA was cleaned with the help of various buffers. Then DNA was isolated by using nuclease-free water and stored at −20°C after confirmation on a 1% agarose gel (Lonza, USA). DNA concentration and purity were assessed using a UV-Vis spectrophotometer (Shimadzu Corporation, Japan), and samples with an A260/280 ratio between 1.8 and 2.0 were included for downstream analysis.

Later, a thermal cycler (miniPCR, USA) was used to perform the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay using different primers (Macrogen, Korea) and it was utilized to detect single nucleotide polymorphisms (SNPs) at different genomic sites. A summary of the thermal condition and primer details to amplify the target details are mentioned in Table 1. EmeraldAmp GT PCR Master Mix (TAKARA BIO INC, Japan) helped to perform PCR and 1% agarose gel electrophoresis confirmed the completion of the PCR. Soon after completion of PCR, MspI and BsrI restriction enzymes (TAKARA BIO INC, Japan) (at 60°C for 1 hour in both cases) were used to digest the products. MspI and BsrI restriction enzymes used to examine the SNP at rs2279744 & rs937282, respectively. All the digested products were fractionated electrophoretically on 3% agarose gel with Midori Green (Nippon Genetics, Europe) (S1 Fig). All gels were independently reviewed by two analysts; discrepancies were resolved by repeat amplification and digestion. To validate the reliability of the data, 20% of the samples were subjected to Sanger sequencing through a commercial service provider (S2 Fig), concordance with PCR-RFLP was 100%.

Table 1. Primer sequences, thermal conditions and amplified products’ size.

Gene, SNP	Primers’ sequence	Thermal condition	Fragments
MDM2 rs2279744	Forward primer: 5’-CGCGGGAGTTCAGGGTAAAG-3’ Reverse primer: R: 5’-CTGAGTCAACCTGCCCACTG-3’	5 min of denaturation at 98°C, then 40 cycles of 98°C for 40s, 55°C for 40s, and 72°C for 90s. The final cycle had a 10 min extension at 72°C	157 BP
MDM2 rs937282	Forward primer: 5’- GGTAACAGCGACACGGAGAT −3’ Reverse primer: 5’- CTCCGGGATGATGGAGTG −3’	5 min of denaturation at 98°C, then 40 cycles of 98°C for 40s, 55°C for 40s, and 72°C for 90s. The final cycle had a 10 min extension at 72°C	231 BP

Measurement of serum MDM2 & p53 level

Circulating levels of MDM2 and p53 were quantified in serum using commercially available sandwich ELISA kits according to the manufacturers’ protocols. MDM2 levels were measured using the Human MDM2 ELISA Kit (catalog no. RAB1578, Sigma-Aldrich, Darmstadt, Germany), and p53 levels were determined using the TP53 ELISA Pair Set (catalog no. SEK90001, Sino Biological, Beijing, China), which detects full-length p53 protein. For both assays, all reagents and samples were brought to room temperature before use, and all measurements were performed duplicate.

For the MDM2 assay, serum samples were diluted twofold using assay diluent, and 100 μL of diluted samples or serially diluted standards were added to the wells and incubated for 2.5 hours at room temperature with gentle shaking. After washing, biotinylated detection antibody was added and incubated for 1 hour. Then samples were kept with horseradish peroxidase–conjugated streptavidin for 45 minutes at room temperature. Plates were then washed and developed using TMB substrate for 30 minutes in the dark, and absorbance was measured immediately at 450 nm using 96 well plate spectrophotometer (BIOBASE, China).

For the p53 assay, microplates were coated overnight at 4°C with capture antibody and subsequently blocked for 1 hour at room temperature. Serum samples were diluted fourfold with sample dilution buffer, and 100 μL of diluted samples or standards were added to the wells and incubated for 2 hours at room temperature. After washing, detection antibody was applied for 1 hour, followed by substrate incubation for 30 minutes in the dark. The reaction was stopped, and absorbance was read at 450 nm.

For both assays, standard curves were generated using a four-parameter logistic regression model (R² ≥ 0.99). Concentrations of MDM2 and p53 in serum samples were calculated by interpolation from the corresponding standard curves and adjusted for sample dilution factors using GraphPad Prism 10.

Statistical analysis

Statistical analyses were performed using R (version 4.4.1) and GraphPad Prism (version 9/10). Genotype distributions were tested for Hardy–Weinberg equilibrium using the HardyWeinberg package. Categorical variables were compared using Pearson’s chi-square test. Associations between MDM2 polymorphisms (rs2279744 and rs937282) and BC risk were evaluated using multivariable logistic regression under additive, dominant, recessive, and over-dominant models, with age and BMI included as covariates. Adjusted odds ratios (aORs) with 95% confidence intervals (CIs) were estimated. Because this study investigated two pre-specified candidate SNPs under complementary genetic inheritance models, no formal correction for multiple testing (e.g., Bonferroni or false discovery rate adjustment) was applied. The findings were interpreted within the context of an exploratory candidate-gene association study.

Because circulating biomarker data were non-normally distributed, non-parametric tests were applied. Serum MDM2 and p53 levels were compared between cases and controls using the Mann–Whitney U test. Genotype- and receptor subtype–dependent differences were analyzed using the Kruskal–Wallis test followed by Dunn’s multiple comparisons test. The association between circulating MDM2 and p53 levels was assessed using Spearman’s rank correlation among BC cases. Post hoc power for primary genetic models was estimated using observed control genotype frequencies with a two-sided α of 0.05. A p-value < 0.05 was considered statistically significant.

Ethical consideration

The study received approval from the Institutional Review Board (IRB) of BRAC University (Approval No. BRACUIRB120220005). Written informed consent was obtained from all participants, who were assured of their right to withdraw from the study at any time without consequences. All procedures adhered to the principles of the Declaration of Helsinki. This study was reported in accordance with STREGA guidelines for genetic association studies.

Results

Socio-demographic and clinical characteristics

Socio-demographic and clinical characteristics are summarized in S1 and S2 Tables, respectively. Cases and controls were comparable across key variables, including age, BMI, and other socio-demographic factors (all p > 0.05), indicating well-matched groups.

Among patients, invasive ductal carcinoma was the predominant subtype. Most tumors were of moderate size, and hormone receptor positivity was common, with a subset showing lymph node involvement.

Serum MDM2 and p53 levels in BC and HCs

Circulating levels of MDM2 and p53 were significantly altered in BC patients compared with HCs (Fig 1). Serum MDM2 concentrations were markedly elevated in BC patients, whereas circulating p53 levels were significantly reduced (both p < 0.0001, Mann–Whitney U test). Lower circulating p53 levels were observed in association with specific MDM2 genotypes; however, circulating p53 concentrations do not directly reflect intracellular p53 activity or MDM2-mediated degradation (Fig 1A–B).

Fig 1. Serum MDM2 (A) & p53 (B) levels in breast cancer patients and healthy controls.

Circulating MDM2 & p53 levels were compared between breast cancer patients and healthy controls using the Mann–Whitney U test. Individual data points are shown, with boxes representing median and interquartile range. [**** p < 0.0001].

Association of MDM2 rs2279744 polymorphism with BC risk

Genotype distributions of MDM2 rs2279744 among cases and controls conformed to Hardy–Weinberg equilibrium (p > 0.05; Table 2). Multivariate logistic regression analysis adjusted for age and BMI revealed a strong association between rs2279744 and BC risk.

Table 2. Association between BC with MDM2 rs2279744 Polymorphism (Multivariate Logistic Regression Adjusted for Age and BMI).

Allele	Cases (n = 112)	HWE		Controls (n = 124)	HWE		Genetic models	aOR	95% CI	P-value
		X2	P		X2	P
TT Homozygote (157 bp)	10 (8.93%)	0.01	0.92	33 (26.613%)	0.479	0.489	Additive model 1 (TG vs. TT)	11.258	4.621 – 27.427	<0.001
							Additive model 2 (GG vs. TT)	1.962	0.882 – 4.366	0.0986
TG Heterozygote (157 bp, 109 bp and 48 bp)	58 (51.79%)			17 (13.710%)			Dominant model (TG+GG vs. TT)	3.699	1.727 – 7.924	<0.001
GG Homozygote (109 bp and 48 bp)	44 (39.29%)			74 (59.677%)			Recessive model (GG vs. TT+TG)	0.437	0.259 - 0.737	0.0018
							Over dominant model (TG vs. TT+GG)	6.760	3.594 – 12.717	<0.001

Note: Genotype frequencies were tested for Hardy-Weinberg equilibrium (HWE) using the Chi-square test (X²). Adjusted Odds Ratios (aOR), 95% Confidence Intervals (CI), and p-values were calculated using multivariate logistic regression in R. All models used the TT genotype (common homozygote) as the reference group.

p > 0.05 indicates that genotype distribution follows Hardy-Weinberg equilibrium.

Abbreviations: HWE – Hardy-Weinberg equilibrium, aOR – Adjusted Odds Ratio, CI – Confidence Interval, X² – Chi-square value

A significant association between rs2279744 and BC risk was observed. In the additive model, women carrying the heterozygous TG genotype had a markedly increased risk of BC compared with TT carriers (aOR = 11.258, 95% CI: 4.621–27.427, p < 0.001). In contrast, the GG genotype did not show a significant increase in risk in the additive model (aOR = 1.962, 95% CI: 0.882–4.366, p = 0.099).

In the dominant model, carriers of at least one G allele (TG + GG) had a significantly higher risk of BC than TT carriers (aOR = 3.699, 95% CI: 1.727–7.924, p < 0.001). Notably, the over-dominant model showed that the TG genotype alone conferred a substantially increased risk when compared with both homozygous genotypes combined (aOR = 6.760, 95% CI: 3.594–12.717, p < 0.001).

In contrast, the recessive model indicated a protective effect of the GG genotype (aOR = 0.437, 95% CI: 0.259–0.737, p = 0.001). Together, these results suggest a genotype-dependent and non-linear effect of rs2279744 on BC susceptibility (Fig 2A; Table 2).

Fig 2. Forest plot showing the association between MDM2 [(A) rs2279744 & (B) rs937282] polymorphisms and breast cancer risk under different genetic models.

Odds ratios (ORs) and 95% confidence intervals were estimated using multivariate logistic regression analysis. The vertical dashed line represents the null value (OR = 1).

Association of MDM2 rs937282 polymorphism with BC risk

Genotype frequencies of MDM2 rs937282 also followed Hardy–Weinberg equilibrium in both groups (Table 3). Unlike rs2279744, rs937282 showed no strong association with BC risk across most genetic models.

Table 3. Association between BC with MDM2 rs937282 polymorphism (Multivariate Logistic Regression Adjusted for Age and BMI).

Allele	Cases (n = 112)	HWE		Controls (n = 124)	HWE		Genetic models	aOR	95% CI	P-value
		X2	P		X2	P
CC Common homozygote (231 bp)	76 (67.857%)	0.681	0.409	88 (70.968%)	0.329	0.566	Additive model 1 (CG vs. CC)	0.712	0.293 – 1.730	0.453
							Additive model 2 (GG vs. CC)	1.801	0.924 – 3.509	0.083
CG Heterozygote (231 bp, 182 bp and 49 bp)	8 (7.142%)			18 (14.516%)			Dominant model (CG+GG vs. CC)	1.158	0.665 – 2.016	0.604
GG Rare homozygote (182 bp and 49 bp)	28 (25%)			18 (14.516%)			Recessive model (GG vs. CC+CG)	1.963	1.017 – 3.789	0.044
							Over dominant model (CG vs. CC+GG)	0.453	0.189 – 1.087	0.076

Note: Genotype frequencies were tested for Hardy-Weinberg equilibrium (HWE) using the Chi-square test (X²). Adjusted Odds Ratios (aOR), 95% Confidence Intervals (CI), and p-values were calculated using multivariate logistic regression in R. All models used the CC genotype (common homozygote) as the reference group.

p > 0.05 indicates that genotype distribution follows Hardy-Weinberg equilibrium.

Abbreviations: HWE – Hardy-Weinberg equilibrium, aOR – Adjusted Odds Ratio, CI – Confidence Interval, X² – Chi-square value

In the additive model, the CG genotype did not show a significant association with BC risk compared to the CC genotype (aOR = 0.712, 95% CI = 0.293–1.730, p = 0.453). The GG genotype, however, showed a trend toward increased risk (aOR = 1.801, 95% CI = 0.924–3.509, p = 0.083), though it did not reach statistical significance.

In the dominant model (CG + GG vs. CC), no significant association was observed (aOR = 1.158, 95% CI = 0.665–2.016, p = 0.604). Under the recessive model, the GG genotype showed a borderline association with increased risk (aOR = 1.963, 95% CI: 1.017–3.789, p = 0.044); however, given the limited statistical power and small number of GG carriers, this finding should be interpreted with caution. The over-dominant model suggested a possible protective effect of the CG genotype (aOR = 0.453, 95% CI: 0.189–1.087, p = 0.076), although this association was not statistically significant. Overall, none of these associations remained robust, indicating a limited role of rs937282 in BC susceptibility in this cohort (Fig 2B; Table 3).

Association of MDM2 genotypes with circulating MDM2 and p53 levels

Serum MDM2 and p53 concentrations differed significantly across rs2279744 genotypes (Fig 3). TG carriers exhibited the highest circulating MDM2 levels, whereas TT carriers showed the lowest levels. Conversely, circulating p53 concentrations were significantly reduced in TG carriers compared with TT and GG genotypes (p < 0.0001). These findings suggest that the rs2279744 polymorphism may be associated with altered regulation of MDM2–p53 pathway (Fig 3A–B).

Fig 3. Association of MDM2 polymorphism with serum MDM2 (A) & p53 (B) levels.

Circulating MDM2 & p53 levels were compared across rs2279744 genotypes using the Kruskal–Wallis test with Dunn’s post-hoc comparisons. Individual data points are shown, with boxes representing median and interquartile range. TG carriers exhibited the highest MDM2 levels, while TT carriers showed the lowest whereas TG carriers exhibited significantly lower p53 levels compared with TT and GG genotypes. [**** = p < 0.0001].

Correlation between circulating MDM2 and p53

Spearman’s rank correlation analysis revealed a strong inverse relationship between circulating MDM2 and p53 levels (r = −0.78, p < 0.0001). Higher MDM2 concentrations were associated with lower p53 levels, these findings suggest a potential association between MDM2 polymorphisms and variation in circulating p53 levels (Fig 4). However, the present data do not establish that the observed differences result from enhanced intracellular MDM2-mediated p53 degradation.

Fig 4. Inverse correlation between serum MDM2 and p53 levels.

The association between circulating MDM2 and p53 levels was evaluated using Spearman’s rank correlation analysis. Each point represents an individual sample. The line indicates the best-fit linear trend for visualization.

Circulating MDM2 levels across BC receptor subtypes

Circulating MDM2 levels varied significantly across BC receptor subtypes (Fig 5). Overall group differences were significant (Kruskal–Wallis p < 0.0001). HER2-positive tumors and triple-positive (ER/PR/HER2-positive) tumors exhibited the highest circulating MDM2 levels, whereas HER2-negative tumors showed the lowest levels. Compact letter display analysis confirmed statistically distinct subgroup patterns following Dunn’s multiple comparisons test (Fig 5). Overall, the combined genetic and circulating biomarker analyses suggest that MDM2 rs2279744 is associated with breast cancer susceptibility and genotype-dependent differences in circulating MDM2 and p53 levels.

Fig 5. Circulating MDM2 levels across breast cancer receptor subtypes.

Circulating MDM2 concentrations were compared among breast cancer patients stratified by estrogen receptor (ER), progesterone receptor (PR), and HER2 status. Individual data points represent serum MDM2 levels (ng/mL), with horizontal lines indicating the median for each group. Overall differences among groups were assessed using the Kruskal–Wallis test, followed by Dunn’s multiple comparisons test. Groups sharing the same letter are not significantly different from each other (adjusted P < 0.05). HER2-positive and triple-positive (ER/PR/HER2-positive) tumors exhibited the highest circulating MDM2 levels, whereas HER2-negative tumors showed the lowest levels.

Discussion

This study analyzed the association of two regulatory polymorphisms in the MDM2 gene, rs2279744 and rs937282, with BC risk in 112 cases and 124 HCs. Based on an assumed minor allele frequency of approximately 0.30 and a significance level of α = 0.05, the estimated statistical power exceeded 95% for rs2279744 due to its moderate and consistent effect sizes. In contrast, rs937282 demonstrated smaller effect sizes, with estimated power ranging from 55–70%. Thus, while the sample size was sufficient to detect strong genetic associations, weaker effects; particularly for rs937282, may require confirmation in larger cohorts.

The primary finding of this study is a significant and genotype-dependent correlation between rs2279744 and breast cancer susceptibility in Bangladeshi women, whereas rs937282 exhibited no statistically significant effect. The heightened risk was predominantly associated with the heterozygous TG genotype of rs2279744, suggesting a non-linear regulatory influence rather than a straightforward allele-dose effect. However, the underlying biological mechanism remains to be established through functional studies. The lack of a clear link for rs937282 makes the rs2279744 finding more specific and less likely that the effect seen is due to population stratification or methodological bias.

MDM2 is an important negative regulator of the tumor suppressor protein p53, which stops cells from growing out of control by controlling the cell cycle, fixing damaged DNA, and causing cell death when necessary [35]. Genetic variants that modify MDM2 transcription can disrupt the p53–MDM2 feedback loop, resulting in diminished p53 activity and enhanced survival of cells with genomic damage [36,37]. This biological framework provides strong support for investigating regulatory MDM2 variants in cancer susceptibility.

The rs2279744 polymorphism (SNP309, T > G) is located in the promoter region of MDM2 and has been shown to enhance binding of the transcription factor Sp1, resulting in increased MDM2 expression [36]. In the present study, carriers of the TG genotype showed a significantly increased risk of BC across additive, dominant, and over-dominant genetic models. In contrast, the GG genotype did not show a significant increase in risk in additive models and appeared to be protective under the recessive model. This pattern indicates that the effect of rs2279744 is not driven simply by the presence of the G allele but may reflect a non-linear pattern of genetic association across genotypes.

Importantly, our circulating biomarker analyses complement the genetic association findings. Women with TG had the highest levels of MDM2 in their blood, but their levels of p53 were lower. In addition, a significant inverse correlation was observed between circulating MDM2 and p53 concentrations. These findings suggest an association between the rs2279744 polymorphism and genotype-dependent differences in circulating MDM2 and p53 levels (Fig 6).

Fig 6. Proposed conceptual model summarizing the observed associations between the MDM2 rs2279744 polymorphism, circulating MDM2 and p53 levels, and breast cancer susceptibility.

Solid arrows represent associations observed in the present study, whereas dashed arrows indicate hypothesized biological mechanisms that require experimental validation.

The finding that the TG genotype presents a greater risk compared to the GG genotype substantiates the concept of potential allele-specific regulatory imbalance, as opposed to a linear allele-dose response. Even small increases in MDM2 expression could have a major impact on p53 function in the tightly controlled feedback loop [18]. Such non-linear regulatory effects may explain why heterozygous carriers exhibited the highest disease risk and MDM2 levels, whereas homozygous GG carriers did not consistently show the same magnitude of effect. The small variation seen between the TT and TG genotypes suggests that there may be an allele-dose effect, but confirmation in larger groups would be valuable. Although the observed heterozygote-specific effect is biologically plausible in the context of the tightly regulated MDM2–p53 feedback loop, the underlying molecular mechanism remains speculative. Therefore, this observation should be interpreted cautiously until replicated in larger independent cohorts and supported by functional studies investigating allele-specific regulatory activity. The relatively large effect estimate observed for the rs2279744 TG genotype should be interpreted with caution. Although genotyping accuracy was supported by PCR-RFLP validation, confirmation by Sanger sequencing, and Hardy–Weinberg equilibrium, the magnitude of the adjusted odds ratio may have been influenced by the modest sample size and the distribution of genotype frequencies within the study population. Consequently, the possibility of sampling variability cannot be completely excluded, and replication in larger, independent multicenter cohorts will be important to determine the stability and generalizability of this association.

These results align with previous studies in other populations, where rs2279744 has been identified as a functional SNP that alters MDM2 level [38]. The G allele has been linked to increased transcriptional activity, reduced p53-mediated apoptosis, and enhanced tumor development [39]. Studies in Chinese populations similarly reported significant associations for the TG genotype, although the effect of the GG genotype varied across genetic models [24]. Taken together, this suggests that carrying one copy of the G allele (heterozygosity) may have a particularly strong impact on disease risk, likely due to its influence on promoter activity and gene expression balance [40].

In contrast, the rs937282 polymorphism did not demonstrate a consistent association with BC risk in this study. Although the GG genotype suggested a nearly two-fold increased risk under the recessive model and the CG genotype appeared potentially protective under the over-dominant model, these findings were not robust across models. This inconsistency mirrors prior reports, where rs937282 has shown variable or modest associations depending on population and study design [41–43]. Located in the 5′UTR of MDM2, rs937282 may influence mRNA stability or translational efficiency [19,41], but its regulatory effect appears less direct than the promoter-based impact of rs2279744 [44].

In addition to genetic associations, our examination of tumor subtypes indicated increased circulating MDM2 levels in HER2-positive and triple-positive tumors. These results suggest a potential association between oncogenic receptor signaling and variation in circulating MDM2 and p53 levels; however, the underlying molecular interactions require further functional investigation. HER2-driven tumors often act aggressively, so higher MDM2 activity may help these types of tumors grow even more. So, MDM2 dysregulation may be especially important in BC phenotypes that are biologically aggressive or driven by receptors.

From a clinical perspective, the tumor characteristics observed in our cohort align with global trends. The main subtype was invasive ductal carcinoma, and most of the tumors had hormone receptors, which means they might respond to endocrine therapies [45–48]. Approximately 29% of patients were HER2-positive, comparable to rates reported in other Asian populations [49,50]. A lot of the tumors were medium to large, and a lot of them had lymph node involvement, which shows that Bangladesh needs better ways to find them early [51,52].

Importantly, cases and controls were well matched across socio-demographic variables, including age, BMI, education, socio-economic status, reproductive history, and menopausal status. This demographic balance enhances the validity of the observed genetic associations and diminishes the potential for confounding effects.

In general, our results show that the two MDM2 variants have very different effects on how likely someone is to get BC. The rs2279744 polymorphism appears to be a biologically relevant regulatory variant that may contribute to breast cancer susceptibility through its association with the MDM2–p53 pathway. However, further functional and clinical validation is required. On the other hand, rs937282 has weaker and less consistent effects. These findings highlight the importance of investigating genotype-specific genetic associations within the MDM2–p53 pathway and suggest that rs2279744 may represent a potential genetic susceptibility marker for breast cancer in South Asian populations. However, validation in larger independent cohorts and functional studies is required before clinical application.

Limitations and future directions

Several limitations should be acknowledged. First, although the study demonstrated adequate statistical power for the primary genetic analyses, the relatively modest sample size may have influenced the precision and magnitude of the observed effect estimates, particularly for rare genotypes and weaker associations such as rs937282. Therefore, these findings should be interpreted cautiously and validated in larger, multicenter cohorts. Second, functional assays were not conducted to directly evaluate allele-specific MDM2 expression or p53 activity. Furthermore, circulating p53 concentrations measured by ELISA should not be interpreted as direct indicators of intracellular p53 activity or MDM2-mediated degradation, as they may be influenced by multiple biological and tumor-related factors. Third, the design of the study may not be applicable to other settings because it was done in a single hospital. Future multicenter studies that include functional validation and long-term follow-up are necessary.

Conclusion

In summary, this research identifies MDM2 rs2279744 as a significant genetic factor influencing breast cancer risk in Bangladeshi women, particularly highlighting the pronounced impact of the heterozygous TG genotype. This risk seems to be caused by problems with the MDM2–p53 association, which is when MDM2 levels are too high and p53 levels are too low. Conversely, rs937282 exhibited no significant correlation with BC susceptibility. These results highlight the biological and clinical significance of regulatory MDM2 variants and support for additional investigation of the MDM2–p53 pathway as a prospective biomarker and therapeutic target in breast cancer.

Supporting Information

S1 Table. Demographic characteristics of BC cases and HC.

(DOCX)

S2 Table. Histopathological data of BC Cases.

(DOCX)

S1 Fig. Representative image of agarose gel electrophoresis.

(TIF)

S2 Fig. Sanger sequencing of PCR amplified genomic DNA region holding rs2279744 and rs937282.

(TIF)

S1 Checklist. STROBE checklist.

(DOCX)

Data Availability

The anonymized dataset supporting the findings of this study has been deposited in the Figshare repository and is publicly available at https://doi.org/10.6084/m9.figshare.32898884.

Funding Statement

This study was partially funded by Rufaida BioMeds (Grant ID: RBM20230409). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.