Next Article in Journal
Axillary Overtreatment in Patients with Breast Cancer After Neoadjuvant Chemotherapy in the Current Era of Targeted Axillary Dissection
Previous Article in Journal
Dermoscopy of Basal Cell Carcinoma Part 2: Dermoscopic Findings by Lesion Subtype, Location, Age of Onset, Size and Patient Phototype
Previous Article in Special Issue
Chemokine CXCL12 Activates CXC Receptor 4 Metastasis Signaling Through the Upregulation of a CXCL12/CXCR4/MDMX (MDM4) Axis
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Cancers
Volume 17
Issue 2
10.3390/cancers17020177
cancers-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Genetic Variations in MDM2 Gene Contribute to Renal Cell Carcinoma Susceptibility: A Genotype–Phenotype Correlation Study

by
Shu-Yu Chang
1,2,3,†,
Wen-Shin Chang
1,2,4,†,
Hou-Yu Shih
1,2,
Chao-Hsiang Chang
2,5,
Hsi-Chin Wu
2,5,
Chia-Wen Tsai
1,2,4,
Yun-Chi Wang
1,2,
Jian Gu
4,* and
Da-Tian Bau
1,2,6,*
1
Graduate Institute of Biomedical Sciences, China Medical University, Taichung 404333, Taiwan
2
Terry Fox Cancer Research Laboratory, Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan
3
Department of Nephrology, Chang-Hua Hospital, Ministry of Health and Welfare, Changhua 51341, Taiwan
4
Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
5
School of Medicine, China Medical University, Taichung 40402, Taiwan
6
Department of Bioinformatics and Medical Engineering, Asia University, Taichung 413305, Taiwan
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Cancers 2025, 17(2), 177; https://doi.org/10.3390/cancers17020177
Submission received: 3 December 2024 / Revised: 3 January 2025 / Accepted: 5 January 2025 / Published: 8 January 2025
(This article belongs to the Special Issue The 10th International MDM2 Workshop—Opening Up New Avenues for MDM2 and p53 Research)
Browse Figures
Versions Notes

Simple Summary

The objective of the current study was to investigate the contribution of mouse double minute 2 (MDM2) genotypes and phenotypes to the risk of renal cell carcinoma (RCC). Our findings indicate that the MDM2 rs2279744 G allele serves as a risk marker against RCC. In addition, it can interact with environmental and clinical risk factors, such as smoking, alcohol drinking, hypertension, and diabetes, to influence RCC risk. Furthermore, MDM2 mRNA levels were significantly higher in RCC patients compared to controls and varied among the MDM2 rs2279744 genotypes, with the GG genotype exhibiting the highest expression levels in both RCC patients and controls. These genotype-associated MDM2 mRNA levels may serve as a practical marker for early RCC detection, alongside MDM2 rs2279744 genotypes. This combined approach could offer potential benefits for personalized and precise early prediction and detection of RCC, which currently lacks effective early detection methodologies.

Abstract

Background: This study aimed to investigate the polymorphic genotypes of MDM2 rs937282, rs937283, rs2279744, and rs769412, as well as the combined effects of MDM2 genotypes and environmental factors on RCC susceptibility. Methods: A total of 135 RCC patients and 590 controls were recruited for MDM2 genotyping using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. Quantitative PCR was performed to assess MDM2 mRNA levels among 30 healthy individuals and 22 RCC patients. Results: MDM2 rs2279744, but not other polymorphisms, was significantly associated with an increased RCC risk (p = 0.0133). The MDM2 rs2279744 G allele was identified as a risk factor for RCC (odds ratio [OR] = 1.49, 95% confidence interval [CI] = 1.14–1.96, p = 0.0047). Among smokers (p = 0.0070), alcohol drinkers (p = 0.0233), individuals with hypertension (p = 0.0041), diabetes (p = 0.0225), and those with a family history of cancer (p = 0.0020), the MDM2 rs2279744 GT and GG genotypes exhibited increased RCC risks. However, this risk effect was not observed in non-smokers, non-drinkers, or individuals without hypertension, diabetes, or a family cancer history (all p > 0.05). Moreover, MDM2 mRNA levels were significantly higher in RCC patients compared to controls and varied among the rs2279744 genotypes, with GG genotype exhibiting the highest expression levels among both RCC patients and controls. Conclusions: This study highlights the association between MDM2 rs2279744 genotypes and RCC risk, suggesting that genotype-associated MDM2 mRNA levels could contribute to early RCC detection. Further studies are warranted to elucidate the detailed mechanisms underlying the role of MDM2 in RCC development.

1. Introduction

Renal cell carcinoma (RCC), the most prevalent solid tumor of the kidney, represents approximately 3% of all malignancies worldwide [1,2,3]. RCC is the most common renal malignancy and encompasses up to 21 subtypes. These subtypes are distinguishable by their unique histopathological features, genetic underpinnings, clinical progression, and therapeutic responses [4,5,6]. Numerous lifestyle factors, including sedentary behavior, obesity, insufficient vegetable consumption, tobacco use, and alcohol intake, have been identified as contributors to RCC risk [7]. RCC often progresses asymptomatically, particularly in advanced stages, leading to delayed diagnosis [8,9]. Furthermore, up to 30% of patients who undergo radical nephrectomy experience significant complications and face early recurrence [10]. Current prognostic assessments for RCC heavily rely on histological evaluations, which are time-consuming and often provide limited guidance for selecting optimal therapeutic strategies [11]. Consequently, there is an urgent need for robust molecular genomic markers to enable the early detection of RCC. Although recent studies have highlighted several hereditary factors associated with RCC susceptibility [12,13,14,15,16], the complex interplay between genetic predispositions and environmental or lifestyle factors remains poorly understood and warrants further investigation.
The murine double minute 2 (MDM2) is a gene situated on human chromosome 12q14.3-q15, spanning 34 kb and encoding a 491-amino-acid protein. The promoter region of MDM2 harbors several single nucleotide polymorphisms (SNPs), including rs937282, rs939283, and rs2279744, which have been implicated in modulating MDM2 expression and influencing cancer risk. Among these, rs2279744 is located within the second promoter-enhancer region of MDM2 and involves a T-to-G substitution at position 309 of intron 1. This SNP creates a binding site for the transcription factor Sp1, resulting in increased MDM2 expression and the subsequent suppression of the p53 pathway in cells exposed to DNA-damaging agents [17,18]. Rs2279744 is the most extensively studied MDM2 SNP regarding its role in human diseases. Beyond rs2279744, several other SNPs, such as rs937282, rs939283, rs117039649, rs3730485, and rs769412, have also been investigated for their associations with cancer susceptibility. Both rs937282 and rs939283 are located in the MDM2 promoter region, whereas rs117039649 and rs3730485 exhibit high linkage disequilibrium with rs2279744. Rs769412, in contrast, is an exonic SNP in exon 11 of the MDM2 gene.
Over recent years, the association between MDM2 SNPs and RCC risk has been explored in only three studies, involving Japanese, Chinese, and Caucasian populations [19,20,21]. Haitel et al. identified the MDM2 rs2279744 GG genotype as significantly associated with an elevated RCC risk [19]. Huang et al. confirmed similar findings, reporting an association between rs2279744 genotypes and RCC susceptibility [20]. Conversely, de Martino and colleagues found no significant relationship between the MDM2 rs2279744 and RCC risk, nor with tumor stage, grade, or histological subtype [21]. Importantly, no other SNPs in MDM2 have been evaluated in relation to RCC. From a proteomic perspective, early evidence from Haitel and colleagues demonstrated that the MDM2 protein is overexpressed in RCC tissues and serves as a marker of poor prognosis and reduced survival [22]. Later, a Japanese research group corroborated these findings, showing that individuals with the rs2279744 GG genotype exhibited the highest levels of MDM2 protein expression compared to those with GT or TT genotypes [19]. However, no studies to date have investigated the influence of MDM2 genotypes, including rs2279744, on MDM2 mRNA expression levels in RCC patients.
In this study, we comprehensively analyzed the genotypes of MDM2 promoter SNPs rs937282, rs937283, and rs2279744, along with the exonic SNP rs769412, in a cohort of Taiwanese patients with RCC. The chromosomal locations of these SNPs are illustrated in Figure 1. For the first time, we investigated the potential interactions between MDM2 genotypes and environmental or clinical risk factors associated with RCC susceptibility. Additionally, we measured MDM2 mRNA expression levels in RCC patients and healthy controls to elucidate the genotype–phenotype relationship.

2. Materials and Methods

2.1. Selected Subjects

This case–control study was conducted at China Medical University Hospital and included 135 patients diagnosed with RCC and 590 cancer-free controls, matched for age and gender. None of the participants were genetically related. RCC diagnoses, as well as tumor grades and subtypes, were confirmed through histopathological evaluation by an experienced group of surgeons and pathologists led by Drs. Wu and Chang.
The cancer-free control subjects were recruited from the Health Examination Center of China Medical University Hospital. Initially, each RCC patient was frequency-matched to 4–5 controls of the same gender and within ±2 years of age. However, individuals with incomplete demographic data regarding smoking, alcohol consumption, hypertension, diabetes, or family history of cancer were excluded. Additional exclusion criteria for controls included any symptoms suggestive of RCC, such as hematuria. After applying all these criteria, a total of 590 eligible controls were retained for analysis.
Each participant provided written informed consent, and 3–5 mL of venous blood was collected for DNA and RNA extraction and subsequential genotyping and RT-PCR. The study protocols have been approved by the Institutional Review Board of China Medical University Hospital (approval number: DMR98-IRB-209). The overall participation rate exceeded 85%. Key demographic and clinical characteristics of the RCC cases and controls are summarized and compared in Table 1.

2.2. DNA Extraction and MDM2 Genotyping Methodology

Genomic DNA was extracted from peripheral blood leukocytes of each participant utilizing a QIAamp Blood Mini Kit according to the guidance from the manufacturer (Qiagen, Valencia, CA, USA). Then, the extracted DNA was stored at −80 °C for long-term preservation. All procedures adhered to standardized protocols routinely employed in our laboratory [15,23,24,25,26].
Genotyping of MDM2 rs937282, rs937283, rs2279744, and rs769412 was performed using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) methodology. The PCR conditions included an initial denaturation step at 94 °C for 5 min, followed by 35 cycles of 94 °C for 30 s, 59 °C for 30 s, and 72 °C for 30 s, with a final extension at 72 °C for 10 min. Upon completion of the reaction, the PCR amplicons were promptly collected and verified by DNA electrophoresis to confirm the expected product sizes. The sequences of the forward and reverse primers used for each SNP are summarized in Table 2.
Once PCR verification was completed, the amplified products were subjected to enzymatic digestion. Specific restriction enzymes—Hyp188I for rs937282, Sau3AI for rs937283, MspA1I for rs2279744, and BbvCI for rs769412—were utilized for cleavage. Digestion was performed overnight to ensure complete enzymatic processing. The resulting DNA fragments were then analyzed by electrophoresis on 3.0% agarose gel, run at 100 V for 30 min. Table 2 provides an overview of the primers, restriction enzymes, and the sizes of DNA fragments before and after digestion. The accuracy of these PCR-RFLP assays was confirmed in our pilot testing of 10 samples using Sanger sequencing. The genotypes at these four SNP sites were 100% concordant between the PCR-RFLP and sequencing assays.

2.3. MDM2 RNA Level Measurement by Quantitative Real-Time PCR

A total of 52 blood samples, comprising 22 from RCC cases and 30 from non-cancer healthy subjects, were subject to RNA extraction utilizing RNeasy Kits (Qiagen, Valencia, CA, USA) following the manufacturer’s protocol. Complementary DNA (cDNA) was synthesized from 1 µg of total RNA of each sample using the RT2 First Strand Kit (Qiagen). Real-time quantitative PCR (qPCR) was performed to evaluate MDM2 RNA expression utilizing an FTC-3000 real-time PCR system (Funglyn Biotech Inc., Toronto, ON, Canada). Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was used as the internal standard for normalization. The primer sequences were designed as follows: forward 5′-GAAATCCCATCACCATC-TTCCAGG-3′ and reverse 5′-GAGCCCCAGCCTTCTCCATG-3′ for the GAPDH gene; forward 5′-TGTAAGTGAACATTCAGGTG-3′ and reverse 5′-TTCCAATAGTCAGCTAAGGA-3′ for the MDM2 gene. The qPCR reactions were performed in a total volume of 20 µL, containing IQ™ SYBR Green Supermix Master Mix (Bio-Rad, Hercules, CA, USA), 0.5 µmol/L of each primer, and 9 ng of cDNA. Each sample was analyzed independently three times to ensure accuracy. The relative MDM2 expression levels were quantified by the typical 2−ΔΔCt method, normalized to GAPDH expression, and shown as the average of three independent experiments. This methodology was the same as our previous publication [27].

2.4. Statistical Analysis

The final statistical analysis included data from 590 cancer-free controls and 135 RCC cases, all with complete genotypic and demographic information. To ensure the representativeness of the control group for the Taiwanese population and to exclude potential genotyping errors, the genotype frequencies of each MDM2 polymorphism in the control group were evaluated for Hardy–Weinberg equilibrium using a goodness-of-fit test. Pearson’s Chi-square test was applied to compare the distribution of MDM2 genotypes between the RCC case and control subjects, as well as across stratified subgroups. Differences in continuous variables, such as age and MDM2 RNA expression levels, were analyzed using the unpaired Student’s t-test. Logistic regression analysis was performed to assess the association between MDM2 genotypes and RCC risk, with results expressed as odds ratios (ORs) and 95% confidence intervals (CIs). Any p-value output below 0.05 was considered statistically significant.

3. Results

3.1. Comparison of Characteristics Between RCC Patients and Healthy Controls

The epidemiological and clinical characteristics of the 135 RCC patients and 590 cancer-free controls are summarized and compared in Table 1. Due to matching by age and gender, no significant differences were observed between the groups for these variables (p = 0.4769 and p = 0.9567, respectively). Additionally, the two groups showed no statistically significant differences in terms of smoking, alcohol consumption, or diabetes prevalence (p = 0.7519, p = 0.5491, and p = 0.1323, respectively).
As expected, the RCC group exhibited significantly higher proportions of individuals with hypertension (66.9% vs. 50.2%) and a family history of cancer (9.3% vs. 3.1%) compared to the controls (p = 0.0004 and p = 0.0005, respectively). Histopathological analysis revealed that 77.1% of RCC cases were of the clear cell subtype. Regarding tumor grade, 53.4% were classified as low-grade, while 46.6% fell into the middle- and high-grade categories (Table 1).

3.2. Association Between MDM2 Genotypes and RCC Risk in Taiwan

The genotypic frequencies of MDM2 rs937282, rs937283, rs2279744, and rs769412 were analyzed in 135 RCC patients and 590 age- and gender-matched healthy controls (Table 3). Hardy–Weinberg equilibrium testing showed that the genotype distributions of all four SNPs in the control group were consistent with expected frequencies (p-values = 0.2197, 0.6448, 0.9035, and 0.3040, respectively).
A significance in the distributions of MDM2 rs2279744 CC, CG, and GG genotypes was identified between the RCC and control groups by the trend analysis (p for trend = 0.0133) (Table 3, middle part). Noticeably, individuals carrying the heterozygous GT or homozygous GG genotypes of MDM2 rs2279744 exhibited an elevated risk of RCC, with ORs of 1.35 (95% CI = 0.79–2.30, p = 0.3346) and 2.13 (95% CI = 1.22–3.71, p = 0.0098), respectively. Under the dominant model, the combined GT + GG genotypes of MDM2 rs2279744 demonstrated a borderline association with RCC risk (OR = 1.63, 95% CI = 0.98–2.69, p = 0.0732, Table 3, middle panel). In contrast, no significance was observed between RCC risk and any of the genotypes of MDM2 rs937282, rs937283, or rs769412 (Table 3).
Further allelic frequency analysis of these four MDM2 genotypes is presented in Table 4. The results confirmed that the variant allele G of MDM2 rs2279744 was present in 61.9% of RCC patients, significantly higher than in the control group (52.1%) (OR = 1.49, 95% CI = 1.14–1.95, p = 0.0047), corroborating the findings from Table 3. Conversely, no significant association was found between RCC susceptibility and the other three SNPs (MDM2 rs937282, rs937283, and rs769412) (Table 4).

3.3. Stratified Analysis of MDM2 rs2279744 Genotypes According to Epidemiological and Clinical Risk Factors

We then conducted stratified analyses based on epidemiological and clinical risk factors, including smoking, alcohol consumption, hypertension, diabetes, and family history of cancer, according to individuals’ MDM2 rs2279744 genotypes.
First, a significant difference in the genotypic distribution of MDM2 rs2279744 was observed between RCC cases and controls among smokers (p = 0.0070) but not among non-smokers (p = 0.3513, Figure 2A). Among smokers, the frequency of the rs2279744 GG genotype was markedly higher in the RCC group compared to the control group (41.2% vs. 27.1%, Figure 2A), suggesting the effect of the GG genotype on RCC risk is only evident in smokers but not in non-smokers.
Second, the genotypic distribution differed significantly between RCC cases and controls among alcohol drinkers but not among non-drinkers (Figure 2B). Specifically, alcohol drinkers in the RCC group had a higher frequency of the GG genotype compared to controls (40.7% vs. 26.3%, Figure 2B). This suggests that the risk of the GG genotype was pronounced in alcohol drinkers but absent in non-drinkers.
Third, stratification by hypertension status revealed a significant association between the rs2279744 GG genotype and elevated RCC risk among hypertensive individuals (p = 0.0041). However, no such association was observed in non-hypertensive individuals (p = 0.9621, Figure 2C).
Fourth, an analysis stratified by diabetes status showed that the risk of the rs2279744 GG genotype was evident in diabetic individuals (p = 0.0225) but not in non-diabetic subjects (p = 0.2633, Figure 2D).
Finally, an analysis stratified by family history of cancer revealed a differential distribution of MDM2 rs2279744 genotypes only among those with a family history of cancer (p = 0.0020, Figure 2E), suggesting that the risk of the GG genotype of MDM2 rs2279744 is particularly relevant in this subgroup.

3.4. The Expression Levels of MDM2 mRNA According to Individual Genotypes

The blood levels of MDM2 in 22 RCC patients and 30 healthy controls were measured and are illustrated in Figure 3. The relative MDM2 mRNA expression was significantly elevated in RCC patients compared to healthy controls (p = 0.0001, Figure 3). Based on their MDM2 rs2279744 genotypes, these individuals were categorized into three subgroups: TT genotype, GT genotype, and GG genotype. In both the case and control groups, a significant trend was observed: MDM2 mRNA levels increased with the number of variant G alleles. The homozygous variant genotype group (GG) exhibited the highest MDM2 mRNA levels, the heterozygous variant genotype group (GT) showed intermediate levels, and the wild-type genotype group (TT) displayed the lowest MDM2 mRNA expression (Figure 3).

4. Discussion

Clinically, early-stage RCC often presents with minimal or non-specific symptoms, making its early detection particularly challenging. The identification of reliable genomic biomarkers could significantly enhance the precision of RCC risk assessment and outcome prediction. Recent studies have highlighted that MDM2 genotypes may influence serum MDM2 levels, potentially contributing to cancer susceptibility. These findings have been reported across multiple cancer types, supporting the importance of MDM2 as a potential biomarker [28,29,30]. Polymorphic variations at MDM2 rs2279744 have been associated with either increased or decreased cancer risk in various contexts, including retinoblastoma [31], oral cancer [32], esophageal cancer [33,34], lung cancer [35,36,37,38,39,40,41], breast cancer [42,43], gastric cancer [44,45,46], hepatocellular carcinoma [47,48,49], colorectal cancer [50,51,52], endometrial cancer [53,54,55,56], cervical cancer [57], prostate cancer [58], melanoma [59], and childhood acute lymphoblastic leukemia [60]. Similarly, the MDM2 rs937282 polymorphism has been linked to lung cancer [38] and breast cancer [61], while MDM2 rs937283 has been implicated in retinoblastoma [62], oral cancer [32], thyroid cancer [63], breast cancer [61,64,65], gastric cancer [66], and liver cancer [65]. In addition, MDM2 rs769412 has been associated with the risks of lung cancer [67] and breast cancer [68], while no significant correlations have been observed in oral cancer [69], lung cancer [41], or retinoblastoma [62]. Taken together, the collective evidence supports a potential link between MDM2 genotypes and RCC susceptibility, warranting further investigation into their role as predictive biomarkers in this malignancy.
In this study, we conducted a comprehensive investigation of the MDM2 SNPs rs937282, rs937283, rs2279744, and rs769412 among 135 RCC patients and 590 healthy controls from the Taiwanese population. Taiwan’s geographical isolation and genetic homogeneity, combined with distinctive lifestyle factors such as widespread traditional herbal medicine use, have contributed to a higher incidence of RCC compared to other regions.
Our findings revealed a significantly increased RCC risk associated with the heterozygous GT and homozygous GG genotypes of MDM2 rs2279744 (Table 3 and Table 4). Conversely, no significant associations were observed between RCC susceptibility and the genotypes of MDM2 rs937282, rs937283, or rs769412 (Table 3 and Table 4). These results support the hypothesis that MDM2 rs2279744 genotypes may serve as a susceptibility factor for RCC. The findings align with previous reports by Huang et al. and Hirata et al. [19,20] but contradict the negative results from de Martino et al., which were based on a Caucasian population [21]. The discrepancy may be attributed to genetic, lifestyle, and environmental differences between East Asian populations and Caucasian populations. It is worth pointing out that the frequency of the variant G allele of rs2279744 varies dramatically among different races and ethnicities, with East Asians showing by far the highest prevalence: 10.8% in Africans, 36.3% in Europeans, and 53.3% in East Asians (Japanese and Koreans) (https://www.ncbi.nlm.nih.gov/snp/rs2279744, accessed on 3 January 2025). The G allele frequency in our controls (52.1%) aligns with that of East Asians.
This current study also presents two novel findings. First, MDM2 rs2279744 genotypes were not only predictors of RCC susceptibility but also showed significant interactions with risk factors such as smoking, alcohol consumption, hypertension, diabetes, and a family history of cancer (Figure 2). Second, the G allele of MDM2 rs2279744 was associated with elevated MDM2 mRNA expression levels in both RCC patients and healthy individuals (Figure 3). The integration of MDM2 genotypes with personal behavioral and clinical factors offers potential for personalized RCC risk prediction and early detection. Additionally, the correlation between MDM2 rs2279744 and mRNA expression levels highlights the potential utility of combining DNA and RNA analyses for precise and non-invasive diagnostic strategies. Future research should validate the contribution of MDM2 genotypes, particularly rs2279744, to RCC risk in larger and more diverse populations globally. Such studies could solidify the role of MDM2 SNPs in RCC risk assessment and pave the way for their integration into clinical practice.
A detailed stratification analysis of RCC patients and controls based on epidemiological and clinical characteristics revealed that MDM2 rs2279744 genotypes significantly influenced RCC susceptibility among smokers (Figure 2A), alcohol consumers (Figure 2B), individuals with hypertension (Figure 2C), diabetes (Figure 2D), and those with a family history of cancer (Figure 2E). However, no significant association was observed for non-smokers, non-drinkers, or individuals without hypertension, diabetes, or a family history of cancer (Figure 2). A previous study by Huang and colleagues also examined the association between MDM2 rs2279744 genotypes and RCC risk, reporting that the GT and GG genotypes were linked to RCC susceptibility after adjusting for confounding factors such as age, gender, body mass index, smoking, alcohol consumption, tea and coffee drinking, and hypertension [21]. Their study cohort, which had similar genotypic frequency distributions to ours, consisted of a smaller sample size (127 cases and 254 controls) compared to the present investigation. Despite the differences in cohort size, the similarity in findings underscores the potential role of MDM2 rs2279744 in RCC risk modulation within this population.
The genotype–phenotype correlation was analyzed by measuring serum MDM2 levels in 22 RCC patients and 30 healthy controls. The results revealed significant differences in MDM2 serum levels among individuals with different MDM2 rs2279744 genotypes in both controls and cancer patients: serum MDM2 mRNA levels increased progressively with the number of variant G alleles. The homozygous variant genotype group (GG) exhibited the highest levels of MDM2 mRNA, followed by the heterozygous genotype group (GT) with intermediate levels and the wild-type genotype group (TT) with the lowest expression (Figure 3). Additionally, MDM2 mRNA levels were significantly elevated in cancer patients compared to controls. These findings align with a previous study on gastric cancer [70]. These findings suggest that the MDM2 rs2279744 genotype could serve as a convenient biomarker for assessing RCC risk and progression. Furthermore, the associated RNA expression levels, and, potentially, protein levels in future studies, may help address the challenges of early-stage RCC detection and prediction. This dual approach of genotype and phenotype assessment could significantly enhance early diagnostic capabilities for RCC in clinical practice.
The MDM2 gene encodes a protein containing several conserved functional domains, including an N-terminal p53-binding domain, a bipartite nuclear localization sequence, a nuclear export sequence, an acidic domain, and a C-terminal RING finger domain, critical for its E3 ubiquitin ligase activity [71,72]. MDM2 is a well-known negative regulator of p53 that directly binds to and inhibits p53. Overexpression of MDM2 has been reported in several cancers, including RCC [73,74]. Overexpression of MDM2 is also a worse prognosis marker for RCC [74]. The regulation of MDM2 is intricate and context-dependent at multiple levels, including genetic (gene amplification), transcriptional/post-transcriptional (e.g., SNP309, promoter CpG methylation, non-coding RNA, alternative splicing, and mRNA stability), and translation/post-translational regulation (e.g., phosphorylation, ubiquitination, and SUMOylation) [75]. SNP rs2279744 is one of the regulation mechanisms of MDM2 expression. At the molecular level, the T-to-G substitution at MDM2 rs2279744 has been reported to create an Sp1 transcription factor binding site, thereby enhancing MDM2 expression. Bond et al. [76] conducted pioneering work on the function of SNP in vitro. They confirmed the binding of Sp1 to the SNP region, and found MDM2 mRNA and protein levels were significantly higher in cell lines with homozygous (G/G) (on average 4-fold) and heterozygous genotypes at rs2279744 (T/G) (on average 1.9-fold) than cell lines with the wild-type genotype (T/T). The increase in MDM2 levels attenuates the p53 tumor suppressor pathway, thereby accelerating tumor initiation and progression [76,77]. This suggests that the GG genotype at MDM2 rs2279744 may elevate MDM2 expression, disrupt p53 function, and contribute to RCC development and progression. In addition to inhibiting p53, MDM2 exerts other oncogenic effects. For instance, siRNA-mediated downregulation of MDM2 has been shown to reduce the expression of HIF1α and HIF2α in VHL-defective RCC [78]. Mutations in VHL and the subsequent activation of HIF1α and HIF2α are key drivers of RCC development. Targeting MDM2 as a therapeutic strategy for cancers has been extensively studied, and several Phase 3 clinical trials are currently underway [79]. Although our study does not directly measure MDM2 protein levels, the observed increase in MDM2 mRNA associated with the GG genotype (Figure 3) provides indirect support for this hypothesis. Hirata et al. [19] showed that the variant G allele of rs2279744 led to increased MDM2 expression in RCC tumors; furthermore, the homozygous G/G genotype is an independent predictor of worse cancer-specific survival. Our study is the first study to link the rs2279744 genotype with RCC risk in the Taiwanese population and the first to show increased serum MDM2 levels in RCC patients as well as a significant genotype-mRNA expression in the context of RCC initiation. The SNP and serum mRNA level may facilitate identifying high-risk individuals and early detection of RCC. We could not assess the prognostic value of rs2279744 and MDM2 in our patient cohort, due to limited sample size, heterogeneous treatment, and short follow-up time. The precise molecular mechanisms underlying the interactions between MDM2 and other key regulatory proteins, such as p53 and p21, in the context of RCC remain unclear. Future research is necessary to identify other factors regulating MDM2 regulation in RCC, assess the prognostic value of rs2270744 in RCC patients, and perform high-throughput sequencing in tumor DNA/RNA to discover mutations and gene expression alterations related to RCC progression and survival.

5. Conclusions

In summary, this pilot study identifies a significant correlation between the MDM2 rs2279744 genotypes and RCC susceptibility in the Taiwanese population. Notably, this is the first study to explore the interaction between MDM2 genotypes and behavioral or clinical factors in relation to RCC risk. Our findings indicate that the MDM2 rs2279744 SNP is significantly associated with RCC susceptibility, particularly among individuals who smoke, consume alcohol, or have comorbidities such as hypertension, diabetes, or a family history of cancer. These results underscore the potential utility of MDM2 genotyping in facilitating early RCC detection, especially in high-risk individuals.

Author Contributions

Conceptualization, S.-Y.C., W.-S.C., C.-W.T., and D.-T.B.; data curation, W.-S.C., H.-Y.S., and Y.-C.W.; formal analysis, H.-Y.S., C.-W.T., Y.-C.W., and D.-T.B.; project administration, J.G. and D.-T.B.; resources, S.-Y.C., C.-H.C., and H.-C.W.; supervision, J.G.; validation, S.-Y.C., W.-S.C., and C.-W.T.; writing—original draft, W.-S.C., J.G., and D.-T.B.; writing—review and editing, W.-S.C., J.G., and D.-T.B. All authors have read and agreed to the published version of the manuscript.

Funding

This study received significant support from China Medical University Hospital (DMR-113-102) and Chang-Hua Hospital, Ministry of Health and Welfare (114-3). The funders had no involvement in the study design, data collection, statistical analysis, decision to publish, or manuscript preparation.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of China Medical University Hospital (DMR98-IRB-209).

Informed Consent Statement

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

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Bray, F.; Laversanne, M.; Sung, H.; Ferlay, J.; Siegel, R.L.; Soerjomataram, I.; Jemal, A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2024, 74, 229–263. [Google Scholar] [CrossRef] [PubMed]
  2. Bukavina, L.; Bensalah, K.; Bray, F.; Carlo, M.; Challacombe, B.; Karam, J.A.; Kassouf, W.; Mitchell, T.; Montironi, R.; O’Brien, T.; et al. Epidemiology of Renal Cell Carcinoma: 2022 Update. Eur. Urol. 2022, 82, 529–542. [Google Scholar] [CrossRef] [PubMed]
  3. Makino, T.; Kadomoto, S.; Izumi, K.; Mizokami, A. Epidemiology and Prevention of Renal Cell Carcinoma. Cancers 2022, 14, 4059. [Google Scholar] [CrossRef] [PubMed]
  4. Ferlay, J.; Colombet, M.; Soerjomataram, I.; Dyba, T.; Randi, G.; Bettio, M.; Gavin, A.; Visser, O.; Bray, F. Cancer incidence and mortality patterns in Europe: Estimates for 40 countries and 25 major cancers in 2018. Eur. J. Cancer 2018, 103, 356–387. [Google Scholar] [CrossRef]
  5. Rose, T.L.; Kim, W.Y. Renal Cell Carcinoma: A Review. JAMA 2024, 332, 1001–1010. [Google Scholar] [CrossRef] [PubMed]
  6. Alaghehbandan, R.; Siadat, F.; Trpkov, K. What’s new in the WHO 2022 classification of kidney tumours? Pathologica 2022, 115, 8–22. [Google Scholar] [CrossRef]
  7. Kabaria, R.; Klaassen, Z.; Terris, M.K. Renal cell carcinoma: Links and risks. Int. J. Nephrol. Renovasc. Dis. 2016, 9, 45–52. [Google Scholar] [CrossRef]
  8. Khaleghi Mehr, F.; Abian, N.; Abolhasani, M.; Moradi, Y. Asymptomatic ureteral metastasis of chromophobe renal cell carcinoma after radical nephrectomy: A case report and review of literature. Int. J. Surg. Case Rep. 2024, 120, 109907. [Google Scholar] [CrossRef] [PubMed]
  9. Beecroft, N.; Gauntner, T.D.; Upadhyay, R.; Wang, S.J.; Yang, Y.; Singer, E.A.; Dason, S. Active Surveillance in Metastatic Renal Cell Carcinoma. J. Kidney Cancer VHL 2024, 11, 27–38. [Google Scholar] [CrossRef] [PubMed]
  10. Lim, K.; Riveros, C.; Ranganathan, S.; Xu, J.; Patel, A.; Slawin, J.; Ordonez, A.; Aghazadeh, M.; Morgan, M.; Miles, B.J.; et al. Morbidity and mortality of multivisceral resection with radical nephrectomy for locally advanced renal cell carcinoma: An analysis of the National Surgical Quality Improvement Program (NSQIP) database. Urol. Oncol. 2023, 41, 209.e1–209.e9. [Google Scholar] [CrossRef]
  11. Maffezzoli, M.; Buti, S. Prognostication for surgically treated papillary renal cell carcinoma: Which model is the optimal choice? Future Oncol. 2024, 20, 107–111. [Google Scholar] [CrossRef] [PubMed]
  12. Tsai, C.W.; Chang, W.S.; Xu, Y.; Huang, M.; Tamboli, P.; Wood, C.G.; Bau, D.T.; Gu, J. Prognostic significance of circulating insulin growth-like factor 1 and insulin growth-like factor binding protein 3 in renal cell carcinoma patients. Am. J. Cancer Res. 2022, 12, 852–860. [Google Scholar]
  13. Chen, M.; Tsai, C.W.; Chang, W.S.; Xiong, G.Y.; Xu, Y.; Bau, D.T.; Gu, J. High circulating insulin-like growth factor-1 reduces the risk of renal cell carcinoma: A Mendelian randomization study. Carcinogenesis 2021, 42, 826–830. [Google Scholar] [CrossRef] [PubMed]
  14. Liao, C.H.; Tsai, C.L.; Chang, S.Y.; Lin, Y.H.; Wang, Y.C.; Huang, W.C.; Mong, M.C.; Yang, Y.C.; Wu, W.T.; Chen, J.C.; et al. Impacts of Matrix Metalloproteinase 9 Genotypes on Renal Cell Carcinoma. Vivo 2023, 37, 2452–2458. [Google Scholar] [CrossRef] [PubMed]
  15. Liao, C.H.; Chien, W.C.; Chang, S.Y.; Lin, Y.H.; Wang, Y.C.; Huang, W.C.; Mong, M.C.; Yang, Y.C.; Wu, W.T.; Chen, J.C.; et al. Associations of Matrix Metalloproteinase-8 Genotypes to Renal Cell Carcinoma in Taiwan. Anticancer Res. 2024, 44, 1931–1938. [Google Scholar] [CrossRef]
  16. Chang, W.S.; Shen, T.C.; Yeh, W.L.; Yu, C.C.; Lin, H.Y.; Wu, H.C.; Tsai, C.W.; Bau, D.T. Contribution of Inflammatory Cytokine Interleukin-18 Genotypes to Renal Cell Carcinoma. Int. J. Mol. Sci. 2019, 20, 1563. [Google Scholar] [CrossRef]
  17. Alhopuro, P.; Ylisaukko-Oja, S.K.; Koskinen, W.J.; Bono, P.; Arola, J.; Jarvinen, H.J.; Mecklin, J.P.; Atula, T.; Kontio, R.; Makitie, A.A.; et al. The MDM2 promoter polymorphism SNP309T-->G and the risk of uterine leiomyosarcoma, colorectal cancer, and squamous cell carcinoma of the head and neck. J. Med. Genet. 2005, 42, 694–698. [Google Scholar] [CrossRef] [PubMed]
  18. Knappskog, S.; Lonning, P.E. Effects of the MDM2 promoter SNP285 and SNP309 on Sp1 transcription factor binding and cancer risk. Transcription 2011, 2, 207–210. [Google Scholar] [CrossRef] [PubMed]
  19. Hirata, H.; Hinoda, Y.; Kikuno, N.; Kawamoto, K.; Suehiro, Y.; Tanaka, Y.; Dahiya, R. MDM2 SNP309 polymorphism as risk factor for susceptibility and poor prognosis in renal cell carcinoma. Clin. Cancer Res. 2007, 13, 4123–4129. [Google Scholar] [CrossRef] [PubMed]
  20. Huang, C.Y.; Su, C.T.; Chu, J.S.; Huang, S.P.; Pu, Y.S.; Yang, H.Y.; Chung, C.J.; Wu, C.C.; Hsueh, Y.M. The polymorphisms of P53 codon 72 and MDM2 SNP309 and renal cell carcinoma risk in a low arsenic exposure area. Toxicol. Appl. Pharmacol. 2011, 257, 349–355. [Google Scholar] [CrossRef] [PubMed]
  21. de Martino, M.; Taus, C.; Wessely, I.S.; Lucca, I.; Hofbauer, S.L.; Haitel, A.; Shariat, S.F.; Klatte, T. The T309G murine double minute 2 gene polymorphism is an independent prognostic factor for patients with renal cell carcinoma. DNA Cell Biol. 2015, 34, 107–112. [Google Scholar] [CrossRef]
  22. Haitel, A.; Wiener, H.G.; Baethge, U.; Marberger, M.; Susani, M. mdm2 expression as a prognostic indicator in clear cell renal cell carcinoma: Comparison with p53 overexpression and clinicopathological parameters. Clin. Cancer Res. 2000, 6, 1840–1844. [Google Scholar] [PubMed]
  23. Yang, M.D.; Lin, K.C.; Lu, M.C.; Jeng, L.B.; Hsiao, C.L.; Yueh, T.C.; Fu, C.K.; Li, H.T.; Yen, S.T.; Lin, C.W.; et al. Contribution of matrix metalloproteinases-1 genotypes to gastric cancer susceptibility in Taiwan. Biomedicine 2017, 7, 10. [Google Scholar] [CrossRef]
  24. Deng, Y.; Ke, T.W.; Yueh, T.C.; Chin, Y.T.; Wang, Y.C.; Hung, Y.C.; Mong, M.C.; Yang, Y.C.; Wu, W.T.; Chang, W.S.; et al. The Contribution of DNA Ligase 4 Polymorphisms to Colorectal Cancer. Vivo 2024, 38, 127–133. [Google Scholar] [CrossRef] [PubMed]
  25. Wang, B.R.; Ma, H.H.; Chang, C.H.; Liao, C.H.; Chang, W.S.; Mong, M.C.; Yang, Y.C.; Gu, J.; Bau, D.T.; Tsai, C.W. Contribution of Matrix Metalloproteinase-2 and Matrix Metalloproteinase-9 to Upper Tract Urothelial Cancer Risk in Taiwan. Life 2024, 14, 801. [Google Scholar] [CrossRef] [PubMed]
  26. Hung, C.C.; Tsai, C.L.; Chin, Y.T.; Wang, Y.C.; Liu, C.H.; Lin, M.L.; Chen, S.S.; He, J.L.; Tsai, C.W.; Su, C.H.; et al. Impacts of Matrix Metalloproteinase-2 Promoter Genotypes on Breast Cancer Risk. Cancer Genom. Proteom. 2024, 21, 502–510. [Google Scholar] [CrossRef]
  27. Chen, C.C.; Chang, W.S.; Pei, J.S.; Kuo, C.C.; Wang, C.H.; Wang, Y.C.; Hsu, P.C.; He, J.L.; Gu, J.; Bau, D.T.; et al. Non-homologous End-joining Genotype, mRNA Expression, and DNA Repair Capacity in Childhood Acute Lymphocytic Leukemia. Cancer Genom. Proteom. 2024, 21, 144–157. [Google Scholar] [CrossRef] [PubMed]
  28. Gallegos-Arreola, M.P.; Marquez-Rosales, M.G.; Sanchez-Corona, J.; Figuera, L.E.; Zuniga-Gonzalez, G.; Puebla-Perez, A.M.; Delgado-Saucedo, J.I.; Montoya-Fuentes, H. Association of the Del1518 Promoter (rs3730485) Polymorphism in the MDM2 Gene with Breast Cancer in a Mexican Population. Ann. Clin. Lab Sci. 2017, 47, 291–297. [Google Scholar]
  29. Okamoto, K.; Tsunematsu, R.; Tahira, T.; Sonoda, K.; Asanoma, K.; Yagi, H.; Yoneda, T.; Hayashi, K.; Wake, N.; Kato, K. SNP55, a new functional polymorphism of MDM2-P2 promoter, contributes to allele-specific expression of MDM2 in endometrial cancers. BMC Med. Genet. 2015, 16, 67. [Google Scholar] [CrossRef]
  30. 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]
  31. Chen, R.; Liu, S.; Ye, H.; Li, J.; Du, Y.; Chen, L.; Liu, X.; Ding, Y.; Li, Q.; Mao, Y.; et al. Association of p53 rs1042522, MDM2 rs2279744, and p21 rs1801270 polymorphisms with retinoblastoma risk and invasion in a Chinese population. Sci. Rep. 2015, 5, 13300. [Google Scholar] [CrossRef]
  32. Chen, X.; Sturgis, E.M.; Lei, D.; Dahlstrom, K.; Wei, Q.; Li, G. Human papillomavirus seropositivity synergizes with MDM2 variants to increase the risk of oral squamous cell carcinoma. Cancer Res. 2010, 70, 7199–7208. [Google Scholar] [CrossRef] [PubMed]
  33. Boonstra, J.J.; van Marion, R.; Tilanus, H.W.; Dinjens, W.N. Functional polymorphisms associated with disease-free survival in resected carcinoma of the esophagus. J. Gastrointest. Surg. 2011, 15, 48–56. [Google Scholar] [CrossRef] [PubMed]
  34. Xiao, F.K.; Guo, S.; Yang, F.; Zhao, L.S.; Wang, L.D. MDM2 and its functional polymorphism SNP309 contribute to the development of esophageal carcinoma. J. Gene Med. 2019, 21, e3086. [Google Scholar] [CrossRef] [PubMed]
  35. Zhang, X.; Miao, X.; Guo, Y.; Tan, W.; Zhou, Y.; Sun, T.; Wang, Y.; Lin, D. Genetic polymorphisms in cell cycle regulatory genes MDM2 and TP53 are associated with susceptibility to lung cancer. Hum. Mutat. 2006, 27, 110–117. [Google Scholar] [CrossRef] [PubMed]
  36. Liu, G.; Wheatley-Price, P.; Zhou, W.; Park, S.; Heist, R.S.; Asomaning, K.; Wain, J.C.; Lynch, T.J.; Su, L.; Christiani, D.C. Genetic polymorphisms of MDM2, cumulative cigarette smoking and nonsmall cell lung cancer risk. Int. J. Cancer 2008, 122, 915–918. [Google Scholar] [CrossRef] [PubMed]
  37. Dong, J.; Ren, B.; Hu, Z.; Chen, J.; Hu, L.; Dai, J.; Jin, G.; Xu, L.; Shen, H. MDM2 SNP309 contributes to non-small cell lung cancer survival in Chinese. Mol. Carcinog. 2011, 50, 433–438. [Google Scholar] [CrossRef] [PubMed]
  38. Zheng, D.; Chen, Y.; Gao, C.; Wei, Y.; Cao, G.; Lu, N.; Hou, Y.; Jiang, X.; Wang, J. Polymorphisms of p53 and MDM2 genes are associated with severe toxicities in patients with non-small cell lung cancer. Cancer Biol. Ther. 2014, 15, 1542–1551. [Google Scholar] [CrossRef] [PubMed]
  39. Wang, X.; Jin, L.; Cui, J.; Ma, K.; Chen, X.; Li, W. Mouse double minute-2 homolog (MDM2)-rs2279744 polymorphism associated with lung cancer risk in a Northeastern Chinese population. Thorac. Cancer 2015, 6, 91–96. [Google Scholar] [CrossRef] [PubMed]
  40. Lin, Y.C.; Chang, W.S.; Shen, T.C.; Li, H.T.; Li, C.H.; Hsiau, Y.C.; Wang, Y.C.; Wu, C.N.; Gong, C.L.; Wang, Z.H.; et al. Association of Murine Double Minute 2 Genotypes and Lung Cancer Risk. Vivo 2020, 34, 1047–1052. [Google Scholar] [CrossRef] [PubMed]
  41. Reza, H.A.; Anamika, W.J.; Chowdhury, M.M.K.; Mostafa, M.G.; Uddin, M.A. A cohort study on the association of MDM2 SNP309 with lung cancer risk in Bangladeshi population. Korean J. Intern. Med. 2020, 35, 672–681. [Google Scholar] [CrossRef] [PubMed]
  42. 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]
  43. Floris, M.; Pira, G.; Castiglia, P.; Idda, M.L.; Steri, M.; De Miglio, M.R.; Piana, A.; Cossu, A.; Azara, A.; Arru, C.; et al. Impact on breast cancer susceptibility and clinicopathological traits of common genetic polymorphisms in TP53, MDM2 and ATM genes in Sardinian women. Oncol. Lett. 2022, 24, 331. [Google Scholar] [CrossRef] [PubMed]
  44. Wang, X.; Yang, J.; Ho, B.; Yang, Y.; Huang, Z.; Zhang, Z.; Zhang, G. Interaction of Helicobacter pylori with genetic variants in the MDM2 promoter, is associated with gastric cancer susceptibility in Chinese patients. Helicobacter 2009, 14, 114–119. [Google Scholar] [CrossRef]
  45. Elingarami, S.; Liu, H.; Kalinjuma, A.V.; Hu, W.; Li, S.; He, N. Polymorphisms in NEIL-2, APE-1, CYP2E1 and MDM2 Genes are Independent Predictors of Gastric Cancer Risk in a Northern Jiangsu Population (China). J. Nanosci. Nanotechnol. 2015, 15, 4815–4828. [Google Scholar] [CrossRef]
  46. Tas, A.; Atabey, M.; Caglayan, G.; Bostanci, M.E.; Sahin Bolukbasi, 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]
  47. Akkiz, H.; Sumbul, A.T.; Bayram, S.; Bekar, A.; Akgollu, E. MDM2 promoter polymorphism is associated with increased susceptibility to hepatocellular carcinoma in Turkish population. Cancer Epidemiol. 2010, 34, 448–452. [Google Scholar] [CrossRef]
  48. Di Vuolo, V.; Buonaguro, L.; Izzo, F.; Losito, S.; Botti, G.; Buonaguro, F.M.; Tornesello, M.L. TP53 and MDM2 gene polymorphisms and risk of hepatocellular carcinoma among Italian patients. Infect. Agent. Cancer 2011, 6, 13. [Google Scholar] [CrossRef] [PubMed]
  49. Ezzikouri, S.; Essaid El Feydi, A.; Afifi, R.; Benazzouz, M.; Hassar, M.; Pineau, P.; Benjelloun, S. Impact of TP53 codon 72 and MDM2 promoter 309 allelic dosage in a Moroccan population with hepatocellular carcinoma. Int. J. Biol. Markers 2011, 26, 229–233. [Google Scholar] [CrossRef] [PubMed]
  50. Wang, W.; Du, M.; Gu, D.; Zhu, L.; Chu, H.; Tong, N.; Zhang, Z.; Xu, Z.; Wang, M. MDM2 SNP309 polymorphism is associated with colorectal cancer risk. Sci. Rep. 2014, 4, 4851. [Google Scholar] [CrossRef] [PubMed]
  51. Yueh, T.C.; Hung, Y.W.; Shih, T.C.; Wu, C.N.; Wang, S.C.; Lai, Y.L.; Hsu, S.W.; Wu, M.H.; Fu, C.K.; Wang, Y.C.; et al. Contribution of Murine Double Minute 2 Genotypes to Colorectal Cancer Risk in Taiwan. Cancer Genom. Proteom. 2018, 15, 405–411. [Google Scholar] [CrossRef] [PubMed]
  52. Atabey, M.; Tas, A.; Sari, I.; Bostanci, M.E.; Topcu, O.; Silig, Y. Significant association between MDM2 T309G polymorphism and colorectal cancer. J. BUON 2019, 24, 1137–1142. [Google Scholar] [PubMed]
  53. Ashton, K.A.; Proietto, A.; Otton, G.; Symonds, I.; McEvoy, M.; Attia, J.; Gilbert, M.; Hamann, U.; Scott, R.J. Polymorphisms in TP53 and MDM2 combined are associated with high grade endometrial cancer. Gynecol. Oncol. 2009, 113, 109–114. [Google Scholar] [CrossRef]
  54. Zajac, A.; Smolarz, B.; Stachowiak, G.; Wilczynski, J.R. TP53 and MDM2 polymorphisms and the risk of endometrial cancer in postmenopausal women. Med. Oncol. 2014, 31, 286. [Google Scholar] [CrossRef]
  55. Gansmo, L.B.; Bjornslett, M.; Halle, M.K.; Salvesen, H.B.; Romundstad, P.; Hveem, K.; Vatten, L.; Dorum, A.; Lonning, P.E.; Knappskog, S. MDM2 promoter polymorphism del1518 (rs3730485) and its impact on endometrial and ovarian cancer risk. BMC Cancer 2017, 17, 97. [Google Scholar] [CrossRef] [PubMed]
  56. Wu, J.P.; Luo, X. The association between murine double minute 2 (MDM2) rs2279744 and endometrial cancer risk in a Chinese Han population. Cell Mol. Biol. 2017, 63, 128–130. [Google Scholar] [CrossRef] [PubMed]
  57. Singhal, P.; Hussain, S.; Thakur, N.; Batra, S.; Salhan, S.; Bhambani, S.; Bharadwaj, M. Association of MDM2 and p53 polymorphisms with the advancement of cervical carcinoma. DNA Cell Biol. 2013, 32, 19–27. [Google Scholar] [CrossRef] [PubMed]
  58. Sivonova, M.K.; Jurecekova, J.; Kaplan, P.; Hives, M.; Grendar, M.; Tomascova, A.; Dusenka, R.; Drobkova, H.; Evin, D.; Kliment, J. Association of MDM2 T309G (rs2279744) Polymorphism and Expression Changes With Risk of Prostate Cancer in the Slovak Population. Anticancer Res. 2020, 40, 6257–6264. [Google Scholar] [CrossRef] [PubMed]
  59. Ward, S.V.; Autuori, I.; Luo, L.; LaPilla, E.; Yoo, S.; Sharma, A.; Busam, K.J.; Olilla, D.W.; Dwyer, T.; Anton-Culver, H.; et al. Sex-Specific Associations of MDM2 and MDM4 Variants with Risk of Multiple Primary Melanomas and Melanoma Survival in Non-Hispanic Whites. Cancers 2023, 15, 2707. [Google Scholar] [CrossRef] [PubMed]
  60. Do, T.N.; Ucisik-Akkaya, E.; Davis, C.F.; Morrison, B.A.; Dorak, M.T. TP53 R72P and MDM2 SNP309 polymorphisms in modification of childhood acute lymphoblastic leukemia susceptibility. Cancer Genet. Cytogenet. 2009, 195, 31–36. [Google Scholar] [CrossRef]
  61. Bartnykaite, A.; Savukaityte, A.; Ugenskiene, R.; Dauksaite, M.; Korobeinikova, E.; Gudaitiene, J.; Juozaityte, E. Associations of MDM2 and MDM4 Polymorphisms with Early-Stage Breast Cancer. J. Clin. Med. 2021, 10, 866. [Google Scholar] [CrossRef] [PubMed]
  62. 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, 31240. [Google Scholar] [CrossRef]
  63. Zhang, F.; Xu, L.; Wei, Q.; Song, X.; Sturgis, E.M.; Li, G. Significance of MDM2 and P14 ARF polymorphisms in susceptibility to differentiated thyroid carcinoma. Surgery 2013, 153, 711–717. [Google Scholar] [CrossRef]
  64. Shebli, W.T.Y.; Alotibi, M.K.H.; Al-Raddadi, R.I.; Al-Amri, R.J.; Fallatah, E.I.Y.; Alhujaily, A.S.; Mohamed, H.S. Murine Double Minute 2 Gene (MDM2) rs937283A/G variant significantly increases the susceptibility to breast cancer in Saudi Women. Saudi J. Biol. Sci. 2021, 28, 2272–2277. [Google Scholar] [CrossRef] [PubMed]
  65. 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, 1533033818801550. [Google Scholar] [CrossRef] [PubMed]
  66. Chen, B.; Wang, J.; Chen, Y.; Gu, X.; Feng, X. The MDM2 rs937283 A > G variant significantly increases the risk of lung and gastric cancer in Chinese population. Int. J. Clin. Oncol. 2018, 23, 867–876. [Google Scholar] [CrossRef]
  67. Ahmad, D.; Bakairy, A.K.; Katheri, A.M.; Tamimi, W. MDM2 (RS769412) G>A Polymorphism in Cigarette Smokers: A Clue for the Susceptibility to Smoking and Lung Cancer Risk. Asian Pac. J. Cancer Prev. 2015, 16, 4057–4060. [Google Scholar] [CrossRef]
  68. Sapkota, Y.; Mackey, J.R.; Lai, R.; Franco-Villalobos, C.; Lupichuk, S.; Robson, P.J.; Kopciuk, K.; Cass, C.E.; Yasui, Y.; Damaraju, S. Assessing SNP-SNP interactions among DNA repair, modification and metabolism related pathway genes in breast cancer susceptibility. PLoS ONE 2014, 8, e64896. [Google Scholar] [CrossRef] [PubMed]
  69. Gazdzicka, J.; Golabek, K.; Strzelczyk, J.K.; Kiczmer, P.; Miskiewicz-Orczyk, K.; Krakowczyk, L.; Ostrowska, Z.; Misiolek, M. Selected CDKN2A and MDM2 polymorphisms in oral cavity cancer. Acta Biochim. Pol. 2020, 67, 213–218. [Google Scholar] [CrossRef] [PubMed]
  70. Bartpho, T.S.; Wattanawongdon, W.; Tongtawee, T. The mouse double minute 2 polymorphism is associated with both decreased p53 expression and poor clinicopathological outcomes of gastric cancer. J. Cancer Res. Ther. 2021, 17, 1438–1445. [Google Scholar] [CrossRef]
  71. Dang, J.; Kuo, M.L.; Eischen, C.M.; Stepanova, L.; Sherr, C.J.; Roussel, M.F. The RING domain of Mdm2 can inhibit cell proliferation. Cancer Res. 2002, 62, 1222–1230. [Google Scholar] [PubMed]
  72. Fenton, M.; Borcherds, W.; Chen, L.; Anbanandam, A.; Levy, R.; Chen, J.; Daughdrill, G. The MDMX Acidic Domain Uses Allovalency to Bind Both p53 and MDMX. J. Mol. Biol. 2022, 434, 167844. [Google Scholar] [CrossRef]
  73. 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]
  74. Li, F.; Aljahdali, I.A.M.; Zhang, R.; Nastiuk, K.L.; Krolewski, J.J.; Ling, X. Kidney cancer biomarkers and targets for therapeutics: Survivin (BIRC5), XIAP, MCL-1, HIF1α, HIF2α, NRF2, MDM2, MDM4, p53, KRAS and AKT in renal cell carcinoma. J. Exp. Clin. Cancer Res. 2021, 40, 254. [Google Scholar] [CrossRef]
  75. Klein, A.M.; de Queiroz, R.M.; Venkatesh, D.; Prives, C. The roles and regulation of MDM2 and MDMX: It is not just about p53. Genes Dev. 2021, 35, 575–601. [Google Scholar] [CrossRef] [PubMed]
  76. Bond, G.L.; Hu, W.; Bond, E.E.; Robins, H.; Lutzker, S.G.; Arva, N.C.; Bargonetti, J.; Bartel, F.; Taubert, H.; Wuerl, P.; et al. A single nucleotide polymorphism in the MDM2 promoter attenuates the p53 tumor suppressor pathway and accelerates tumor formation in humans. Cell 2004, 119, 591–602. [Google Scholar] [CrossRef]
  77. Basu, S.; Murphy, M.E. Genetic Modifiers of the p53 Pathway. Cold Spring Harb. Perspect. Med. 2016, 6, a026302. [Google Scholar] [CrossRef]
  78. Carroll, V.A.; Ashcroft, M. Regulation of angiogenic factors by HDM2 in renal cell carcinoma. Cancer Res. 2008, 68, 545–552. [Google Scholar] [CrossRef]
  79. Twarda-Clapa, A. An update patent review of MDM2-p53 interaction inhibitors (2019–2023). Expert Opin. Ther. Pat. 2024, 34, 1177–1198. [Google Scholar] [CrossRef] [PubMed]
Cancers 17 00177 g001
Figure 1. Physical map of MDM2 rs937282, rs937283, rs2279744, and rs769412 polymorphic sites on part of human chromosome 12 (12q15). The restriction enzymes and cutting points are also shown.
Figure 1. Physical map of MDM2 rs937282, rs937283, rs2279744, and rs769412 polymorphic sites on part of human chromosome 12 (12q15). The restriction enzymes and cutting points are also shown.
Cancers 17 00177 g001
Cancers 17 00177 g002
Figure 2. Genotype distributions of MDM2 rs2279744 in cases and controls as stratified by the status of (A) smoking, (B) alcohol drinking, (C) hypertension, (D) diabetes, and (E) family history of cancer. Statistically significant p-values between case and control groups are shown in red and asterisk.
Figure 2. Genotype distributions of MDM2 rs2279744 in cases and controls as stratified by the status of (A) smoking, (B) alcohol drinking, (C) hypertension, (D) diabetes, and (E) family history of cancer. Statistically significant p-values between case and control groups are shown in red and asterisk.
Cancers 17 00177 g002
Cancers 17 00177 g003
Figure 3. Serum MDM2 mRNA levels among 22 RCC patients and 30 healthy control subjects. The serum MDM2 mRNA levels were measured by the quantitative PCR methodology. The relative levels of serum mRNA MDM2 were based on folds of those carrying the MDM2 rs2279744 TT genotype for the controls. Statistically significant p-values between compared groups are shown in red.
Figure 3. Serum MDM2 mRNA levels among 22 RCC patients and 30 healthy control subjects. The serum MDM2 mRNA levels were measured by the quantitative PCR methodology. The relative levels of serum mRNA MDM2 were based on folds of those carrying the MDM2 rs2279744 TT genotype for the controls. Statistically significant p-values between compared groups are shown in red.
Cancers 17 00177 g003
Table 1. Distributions of the frequencies of selected characteristics among the renal cell carcinoma patients and healthy controls.
Table 1. Distributions of the frequencies of selected characteristics among the renal cell carcinoma patients and healthy controls.
CharacteristicCases (n = 135)Controls (n = 590)p-Value
n%n%
Age, mean ± SD59.0 ± 9.6 58.2 ± 9.9 0.4769
≤60 Years7051.7%31052.5%0.9606
>60 Years6548.3%28047.5%
Sex
Male8664.4%38064.4%0.9567
Female4935.6%21035.6%
Smoking status
Smoker5540.7%22938.8%0.7519
Non-smoker8059.3%36161.2%
Alcohol drinking status
Drinker5441.5%21736.8%0.5491
Non-drinker8158.5%37363.2%
Hypertension
Yes9166.9%29650.2%0.0004 *
No4433.1%29449.8%
Diabetes
Yes3322.0%10818.3%0.1323
No10278.0%48281.7%
Family history cancer
Yes149.3%183.1%0.0005 *
No12190.7%57296.9%
Histological type
Clear-cell10577.1%
Non-clear-cell3022.9%
Histological grade
Low7253.4%
Middle and high6346.6%
Statistically significant p-values based on chi-square test with Yates’ correction are shown in bold with a star.
Table 2. The summary of primer sequences, restriction enzyme, and DNA fragment sizes for mouse double minute 2 (MDM2) rs937282, rs937283, rs2279744, and rs769412 polymorphic sites.
Table 2. The summary of primer sequences, restriction enzyme, and DNA fragment sizes for mouse double minute 2 (MDM2) rs937282, rs937283, rs2279744, and rs769412 polymorphic sites.
PolymorphismsPrimer SequencesREPolymorphic GenotypeDNA Fragment Size (bp)
rs937282F: 5′-GGTAACAGCGACACGGAGAT-3′
R: 5′-CGCATCCGGGCATTTGTGC-3′		Hyp188IC
G		307
182 + 125
rs937283F: 5′-CGGATTAGTGCGTACGAGCG-3′
R: 5′-TCAGAGCCCAGACCCAAAAG-3′		Sau3AIG
A		202
154 + 48
rs2279744F: 5′-TTCGCAGCCTTTGTGCGGTT-3′
R: 5′-GAACGTGTCTGAACTTGACC-3′		MspA1IT
G		368
223 + 145
rs769412F: 5′-GGTTACAGAAACTGACTGTG-3′
R: 5′-CACATCTTCTTGGCTGCTAT-3′		BbvCIA
G		371
196 + 175
F and R indicate forward and reverse primers, respectively; RE: restriction enzyme.
Table 3. Associations between MDM2 genotypes and RCC risk in Taiwan.
Table 3. Associations between MDM2 genotypes and RCC risk in Taiwan.
PolymorphismGenotypeCasesControlsp-ValueOR (95% CI)
rs937282CC74 (54.8%)320 (54.2%) 1.00 (Ref)
CG47 (34.8%)221 (37.5%)0.76100.92 (0.61–1.38)
GG14 (10.4%)49 (8.3%)0.63761.24 (0.65–2.36)
Ptrend 0.6861
PHWE 0.2197
CG + GG61 (45.2%)270 (45.8%)0.97950.98 (0.67–1.42)
rs937283AA73 (54.1%)331 (56.1%) 1.00 (Ref)
AG50 (37.0%)219 (37.1%)0.94541.04 (0.70–1.54)
GG12 (8.9%)40 (6.8%)0.49421.36 (0.68–2.72)
Ptrend 0.6829
PHWE 0.6448
AG + GG62 (45.9%)259 (43.9%)0.74001.09 (0.75–1.58)
rs2279744TT21 (15.5%)136 (23.0%) 1.00 (Ref)
GT61 (45.2%)293 (49.7%)0.33461.35 (0.79–2.30)
GG53 (39.3%)161 (27.3%)0.0098 *2.13 (1.22–3.71)
Ptrend 0.0133 *
PHWE 0.9035
GT + GG114 (84.5%)454 (77.0%)0.07321.63 (0.98–2.69)
rs769412AA130 (96.3%)561 (95.1%) 1.00 (Ref)
AG5 (3.7%)28 (4.7%)0.76500.77 (0.29–2.03)
GG0 (0.0%)1 (0.2%)1.0000--
Ptrend 0.7758
PHWE 0.3040
AG + GG5 (3.7%)29 (4.9%)0.70760.74 (0.28–1.96)
RCC: renal cell carcinoma; OR: odds ratio; CI: confidence interval; p-values were calculated by Chi-square with Yates’ correction (n ≥ 5) or Fisher’s exact test (n < 5); HWE: PHWE: p-value for Hardy–Weinberg Equilibrium; Ptrend: p-value for trend analysis; statistically significant p-values are shown in bold with a star.
Table 4. Associations of MDM2 alleles with RCC risk in Taiwan.
Table 4. Associations of MDM2 alleles with RCC risk in Taiwan.
Allelic TypeCasesControlsp-ValueOR (95% CI)
rs937282
C195 (72.2%)861 (73.0%) 1.00 (Ref)
G75 (27.8%)319 (27.0%)0.86341.04 (0.77–1.40)
rs937283
A196 (72.6%)881 (74.7%) 1.00 (Ref)
G74 (27.4%)299 (25.3%)0.53251.11 (0.83–1.50)
rs2279744
T103 (38.1%)565 (47.9%) 1.00 (Ref)
G167 (61.9%)615 (52.1%)0.0047 *1.49 (1.14–1.95)
rs769412
A265 (98.1%)1150 (97.5%) 1.00 (Ref)
G5 (1.9%)30 (2.5%)0.65480.72 (0.28–1.88)
RCC: renal cell carcinoma; OR: odds ratio; CI: confidence interval; p-value was calculated by Chi-square with Yates’ correction; statistically significant p-values are shown in bold with a star.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Chang, S.-Y.; Chang, W.-S.; Shih, H.-Y.; Chang, C.-H.; Wu, H.-C.; Tsai, C.-W.; Wang, Y.-C.; Gu, J.; Bau, D.-T. Genetic Variations in MDM2 Gene Contribute to Renal Cell Carcinoma Susceptibility: A Genotype–Phenotype Correlation Study. Cancers 2025, 17, 177. https://doi.org/10.3390/cancers17020177

AMA Style

Chang S-Y, Chang W-S, Shih H-Y, Chang C-H, Wu H-C, Tsai C-W, Wang Y-C, Gu J, Bau D-T. Genetic Variations in MDM2 Gene Contribute to Renal Cell Carcinoma Susceptibility: A Genotype–Phenotype Correlation Study. Cancers. 2025; 17(2):177. https://doi.org/10.3390/cancers17020177

Chicago/Turabian Style

Chang, Shu-Yu, Wen-Shin Chang, Hou-Yu Shih, Chao-Hsiang Chang, Hsi-Chin Wu, Chia-Wen Tsai, Yun-Chi Wang, Jian Gu, and Da-Tian Bau. 2025. "Genetic Variations in MDM2 Gene Contribute to Renal Cell Carcinoma Susceptibility: A Genotype–Phenotype Correlation Study" Cancers 17, no. 2: 177. https://doi.org/10.3390/cancers17020177

APA Style

Chang, S.-Y., Chang, W.-S., Shih, H.-Y., Chang, C.-H., Wu, H.-C., Tsai, C.-W., Wang, Y.-C., Gu, J., & Bau, D.-T. (2025). Genetic Variations in MDM2 Gene Contribute to Renal Cell Carcinoma Susceptibility: A Genotype–Phenotype Correlation Study. Cancers, 17(2), 177. https://doi.org/10.3390/cancers17020177

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop