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ORIGINAL ARTICLE
Year : 2015  |  Volume : 11  |  Issue : 2  |  Page : 409-414

TP 53 polymorphisms and melanoma: A meta-analysis


1 Department of Pharmacy, The Third Hospital of Wuhan, Wuhan, Hubei, China
2 Department of Neurosurgery, TianYou Hospital, Wuhan University of Science and Technology, Wuhan, China
3 Department of Anesthesiology, Shenzhen People's Hospital, Guangdong, China

Date of Web Publication7-Jul-2015

Correspondence Address:
Junzhi Zhang
Department of Anesthesiology, Shenzhen People's Hospital, Shenzhen-518 029, Guangdong
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0973-1482.157329

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 > Abstract 

Background: p53 is a tumor suppressor encoded by the TP53 gene. It is critical in activating deoxyribonucleic acid (DNA) repair upon damage, and thus preserving genomic stability. TP53 is implicated in tumor progression. Melanoma results from transformed melanocytes in the skin. Data gathered on the association between the TP53 Arg72, Pro72 (rs1042522; G>C) polymorphism and melanoma are conflicting.
Aims: To assess the relationship between the TP53 genotype and the risk of melanoma, we performed a meta-analysis.
Materials and Methods: We searched on PubMed for studies of TP53 polymorphism published in English up to 12 th April 2014. For each study, we calculated odds ratio (OR) and 95% confidence interval (CI), assuming frequency of allele comparison, heterozygote comparison, homozygote comparison, dominant, and recessive genetic models. Seven case-control studies were carried out during the meta-analysis.
Results: The TP53 Callele was not associated with the risk of melanoma in the frequency of allele comparison (C vs G: OR = 1.031; 95% CI = 0.824-1.290; P < 0.001 for heterogeneity). The TP53 GC genotype was not associated with the risk of melanoma as compared with the GG genotype (GC vs GG: OR = 0.922; 95% CI = 0.716-1.186; P = 0.010 for heterogeneity). The TP53 CC genotype was not associated with the risk of melanoma as revealed by both the homozygote comparison and the recessive genetic model. Analysis of the dominant model also did not indicate a significant association between the TP53 polymorphism and melanoma.
Conclusions: This meta-analysis suggests that genotypes for the TP53 rs1042522 G>C polymorphism might not be associated with the risk of melanoma.

Keywords: Genetic polymorphisms, melanoma, meta-analysis, TP53


How to cite this article:
He T, Wu J, Chen Y, Zhang J. TP 53 polymorphisms and melanoma: A meta-analysis. J Can Res Ther 2015;11:409-14

How to cite this URL:
He T, Wu J, Chen Y, Zhang J. TP 53 polymorphisms and melanoma: A meta-analysis. J Can Res Ther [serial online] 2015 [cited 2019 Nov 23];11:409-14. Available from: http://www.cancerjournal.net/text.asp?2015/11/2/409/157329


 > Introduction Top


Melanoma is one of the most frequent cancers in fair-skinned populations. [1] The incidence rate of melanoma varies widely in relation to the race. White population has an approximated 10-fold greater risk of developing cutaneous melanoma than black, Asian, or Hispanic population. Melanoma is now regarded as the fifth most common cancer in men and the sixth most common cancer in women in the United States. The highest recorded incidence of melanoma worldwide is in Queensland (Australia) with an incidence equal to 55.8/105/annum for males and 41.1/105/annum for females. [2] It has high metastatic potential and is often resistant to antitumor agents and accounts for majority of skin cancer-related deaths [3] The available epidemiological evidence indicates that melanoma is relative to photoproducts and oxidative deoxyribonucleic acid (DNA) damage. [4],[5] The development of melanoma results from complex interaction between mutations in various genes and constitutional and/or inherited factors combined with environmental factors, mainly ultraviolet (UV) radiations. [6]

Tumor suppressor p53 (TP53) was discovered more than 2 decades ago to be frequently mutated in diverse types of human cancer and patients with TP53 mutations are known to have a poor outcome. [7],[8] The TP53 tumor-suppressor gene (which encode p53 in humans) was initially found to be essential for DNA-damage checkpoint, however, we now know that it responds to a broad range of cellular stresses, including oncogene activation and hypoxia. [9],[10] The TP53 gene on chromosome 17p13.1 encodes the nuclear protein TP53, which is implicated in cell proliferation, apoptosis, senescence, and DNA repair. [11],[12] Only a small fraction of the >200 naturally occurring sequence variations (single nucleotide polymorphisms (SNPs)) of TP53 in human populations are expected to cause measureable perturbation of p53 function.

A well-studied polymorphism in TP53 (rs1042522; arginine-to-proline, R/P) have been shown to be of functional significance, with the Pro72 allele being less efficient than Arg72 at inducing apoptosis, mainly due to weaker binding and ubiquitination by MDM2 of the Pro72 variant protein. [13] The proteins encoded by Arg 72 and Pro72 alleles have been reported to differ in the functional activity, such as apoptosis, transcriptional potential, and so on. [14] Polymorphism of TP53 at codon 72 affects the accumulation of mitochondrial DNA mutations, likely through the different ability of the two p53 isoforms to bind to polymerase gamma, and may contribute to in vivo accumulation of mitochondrial DNA mutations. [15] TP53 rs1042522 G > C polymorphism has been associated to susceptibility to lung cancer, skin cancer, or breast cancer and other kinds of cancers. [16]

Over the last decade, a number of case-control studies were conducted to investigate the association between the TP53 rs1042522 G > C polymorphism and incidence risk of melanoma in humans. However, the reported results in the literature were conflicting. The data about the relationship between the TP53 rs1042522 G > C polymorphism and the risk of melanoma, together with the designation of the TP53 risk allele, were contradictory and inconclusive. It was possibly due to the insufficient sizes of the cases on the trails, which compromised the power of these studies and biased the outcomes. Meta-analysis is a powerful tool for analyzing cumulative data from studies where the individual sample sizes are small and the statistical power low. There are few meta-analyses reported about this subject and more scientific investigations are in need. Therefore, we have made the meta-analysis to detect the relationship of the TP53 rs1042522 G > C polymorphism and melanoma.


 > Materials and methods Top


Identification and eligibility of relevant studies

PubMed MEDLINE searches were carried out using the search terms: 'TP53' or 'p53', 'polymorphism' or 'variant', and 'skin cancer' or 'melanoma' (last updated on 12 rd April 2014). These searches were complemented with a review of the bibliographies of the retrieved papers and review articles. All articles were published in English. To minimize heterogeneity and facilitate the interpretation of our results, the following inclusion criteria were specified: Case-control design with genotyping of melanoma. For studies that did not provide raw data of allele frequencies in the initial publication, we attempted to obtain this information by correspondence with the authors. When such information was not obtained, the studies were excluded. When the study populations overlapped, we generally retained only the study with the most extensive data for the meta-analysis to avoid duplication.

Eligible studies

We identified eight published reports of potentially eligible studies. [17],[18],[19],[20],[21],[22],[23],[24] We excluded one study of these eight studies, since the study was the meta-analysis of TP53 polymorphism and skin cancer. [21] Thus, seven studies were included in this meta-analysis [Table 1].
Table 1: Characteristics of the published studies used in the meta - analysis

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TP53 genotyping methods

In all the seven studies, genomic DNA was extracted from peripheral blood samples. Four out of seven studies used polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) analysis for the genotyping, two used the allele-specific PCR (AS-PCR) assay and the other one used TaqMan assay.

Data extraction

Data for the analyses, which included the first author's surname, year of publication, country where the study was conducted, ethnicity of the study population, genotype frequencies, and minor allele frequency (MAF) in controls, were extracted from the published articles and summarized in a consistent manner to aid comparison.

Statistical analysis

We calculated the odds ratios (ORs) that corresponded to the 95% confidence intervals (CIs) in accordance with the method of Woolf to evaluate the association between the TP53 polymorphism and melanoma. [25] Five comparisons were performed: Frequency of allele (C vs G), comparison of heterozygote (GC vs GG), comparison of homozygotes (CC vs GG), a dominant genetic model (CC + GC vs. GG), and a recessive genetic model (CC vs GC + GG). We applied two models of meta-analysis for dichotomous outcomes according to the results of heterogeneity tests among individual studies in the software Stata 11.0 (StataCorp, College Station, Texas) or Review Manager (RevMan) 5.0 (Cochrane Collaboration, 2008; www.cc-ims.net/RevMan): The fixed-effects model (Mantel-Haenszel) [26] and the random-effects model (DerSimonian and Laird). [27] The heterogeneity among studies was assessed by using the Chi-square-based Q statistic test. [28] The difference at P < 0.10 was considered significance in the Q statistic test. The random-effects model (if P < 0.10) or the fixed-effects model (if P ≥ 0.10) was used to summarize the combined OR. The significance of the pooled OR was determined by the Z-test. A value of P < 0.05 was considered significant. Publication bias was investigated with the funnel plot, in which the standard error (SE) of log (OR) for each study was plotted against the respective log (OR). An asymmetric plot suggested a possible publication bias. Funnel plot asymmetry was assessed further by Egger's linear regression method. [29] The significance of the intercept was determined by the t-test, and a P - value < 0.05 was considered significant. The χ2 goodness-of-fit test was used to evaluate whether the genotypes among the control subjects conformed to Hardy-Weinberg equilibrium (HWE). Analysis was performed using the software Stata version 11.0 and Review Manager 5.0. All P - values were two-sided.


 > Results Top


Characteristics of the published studies

In total, seven case-control articles published in English met the inclusion criteria [Figure 1]. The seven studies (1,677 cases/2,586 controls) were published between 2001 and 12 th April 2014 [Table 1]. Among those, two studies were carried out in USA and one each in Brazil, Italy, Greece, Germany, and Netherlands. Furthermore, six studies were carried out in Caucasian and one in mixed population. The genotype distributions of all control groups conformed to HWE equilibrium.
Figure 1: Searching process for the eligible studies

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Results of the meta-analysis

Main results

As shown in [Table 2], the TP53 C allele was not associated with the risk of melanoma in the frequency of allele comparison (C vs G: OR = 1.031; 95% CI = 0.824-1.290; P < 0.001 for heterogeneity; power = 6.21%) [Figure 2]. The TP53 GC genotype was not associated with the risk of melanoma as compared with the GG genotype (GC vs GG: OR = 0.922; 95% CI = 0.716-1.186; P = 0.010 for heterogeneity; power = 24.88%) [Figure 3]. The TP53 CC genotype was not associated with the risk of melanoma as revealed by both the homozygote comparison (CC vs GG: OR = 1.220; 95% CI = 0.767-1.941; P = 0.011 for heterogeneity; power = 9.00%) [Figure 4] and the recessive genetic model (CC vs. GC + GG: OR = 1.231; 95% CI = 0.842-1.800; P = 0.065 for heterogeneity; power = 14.72%) [Figure 5]. Analysis of the dominant model also did not indicate a significant association between the TP53 polymorphism and melanoma (CC + GC vs GG: OR = 0.971; 95% CI = 0.741-1.271; P = 0.002 for heterogeneity; power = 17.23%) [Figure 6].
Figure 2: Forest plot of the TP53 rs1042522 G > C polymorphism and melanoma risk in the allele (C vs G) comparison model. OR = Odds ratio, CI = Confidence interval

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Figure 3: Forest plot of the TP53 rs1042522 G > C polymorphism and melanoma risk in the heterozygote comparison model

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Figure 4: Forest plot of the TP53 rs1042522 G > C polymorphism and melanoma risk in the homozygote comparison model

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Figure 5: Forest plot of the TP53 rs1042522 G > C polymorphism and melanoma risk in the recessive genetic model

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Figure 6: Forest plot of the TP53 rs1042522 G > C polymorphism and melanoma risk in the dominant genetic model

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Table 2: Results of the meta - analysis of the association between the TP53rs1042522 G>Cpolymorphism and melanoma in 7 studies (the random - effects model (if pHeterogeneity <0.10) or thefixed-effects model (if pHeterogeneity ≥0.10) was used to summarize the combined OR)

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Publication bias

Begg's funnel plot and Egger's test were performed to determine whether there was a publication bias in the literature. The potential presence of a publication bias was evaluated using a funnel plot of the estimate of log OR for the genotype C vs G against the reciprocal of its SE [Figure 7]. The results of the frequency of allele comparison indicated no publication bias both in Begg's test (z = 1.35, P > ǀzǀ = 0.176) and Egger's test (t = −1.31, P > ǀtǀ = 0.248) [Table 2]. The potential presence of a publication bias was evaluated using a funnel plot of the estimate of log OR for the genotype GC vs GG against the reciprocal of its SE. The results for the heterozygote comparison GC vs GG indicated no publication bias in Begg's (z = 1.05, P > ǀzǀ = 0.293) and Egger's (t = −1.07, P > ǀtǀ = 0.335) test. The results of Begg's and Egger's tests for the comparison of homozygotes, recessive genetic model, and dominant model also indicated a low probability of publication bias [Table 2]. Thus, we confirmed that there was no publication bias.
Figure 7: Funnel plot of the TP53 rs1042522 G > C polymorphism and risk of melanoma for the allele (C vs G) comparison model. Egger's test: t = −1.31, P = 0.248. log = Logarithm, s. e. = Standard error

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 > Discussion Top


UV radiation is an important risk factor for skin cancers including melanoma. The TP53 is a negative feedback for murine double minute 2 (MDM2) and plays a vital role in the protection of cells from DNA damage due to UV exposure. [30]

Our meta-analysis was based on seven case-control studies that provided data on the TP53 rs1042522 G > C polymorphism and the incidence risk of melanoma including over 1,677 cases and 2,586 controls. The results of our analysis provide evidence that genotypes for the TP53 rs1042522 G > C polymorphism might not be associated with the risk of melanoma.

Bastiaens et al., were the first to investigate the association between the incidence of melanoma and the TP53 rs1042522 G > C polymorphism. [17] Subsequent studies revealed controversial findings; some studies have failed to find evidence of an association between the TP53 rs1042522 G > C polymorphism and melanoma. The purpose of this meta-analysis is to assess whether there is an association between the TP53 rs1042522 G > C polymorphism and the incidence risk of melanoma. Most of the studies included in our meta-analysis involved less than 200 cases, the statistical power was too low to allow convincing conclusions to be drawn, and as a consequence the CIs around the ORs were wide. Our meta-analysis suggests that genotypes for the TP53 rs1042522 G > C polymorphism might not be associated with the risk of melanoma.

There was significant heterogeneity for the TP53 rs1042522 G > C polymorphism among the seven case-control studies. Many factors might contribute to such heterogeneity. The seven studies were performed in different countries; however, seven studies involved Caucasians. Therefore, the results of the meta-analysis indicate that the TP53 rs1042522 G > C polymorphism does not have significant involvement in the pathogenesis of melanoma, mainly in Caucasians. Further studies based on larger sample size are still needed, especially in non-Caucasian population.

This study has some limitations. Since the number of studies included in the meta-analysis was small. Given that both positive and negative studies have been published, the publication bias concerning the TP53 rs1042522 G > C polymorphism and melanoma appeared to be low. The funnel plots were symmetrical for the TP53 rs1042522 G > C polymorphism, which indicated a lack of publication bias. Furthermore, the studies with nonsignificant findings could reduce the chance of publication bias. We searched only for articles in the Medline database using the PubMed engine and results were limited to papers published in the English. Hence, it is possible that some studies that meet the inclusion criteria were not included in the meta-analysis. Additionally, the small calculated power (<25.0%) indicated that in future, more well-designed powerful clinical studies are needed to further investigate the association between TP53 polymorphisms and the incidence of melanoma. [31],[32]

In conclusion, the results of this meta-analysis do not support association between the TP53 rs1042522 G > C polymorphism and the incidence risk of melanoma. The existence of genetic structure in the population might lead to the identification of false-positive genetic associations due to an unbalanced distribution between cases and controls. Melanoma appears to be the result of complex interactions between genetic factors and the environment. Future large-scale, population-based association studies are now required to investigate potential gene-gene and gene-environment interactions that involve the TP53 rs1042522 G > C polymorphism and affect the risk of melanoma. Such studies might eventually lead to a better and more comprehensive understanding of the association between the TP53 rs1042522 G > C polymorphism and the risk of melanoma.

 
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    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7]
 
 
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  [Table 1], [Table 2]



 

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