RESEARCH ARTICLE

No evidence of correlation between p53 codon 72 G>C genepolymorphism and cancer risk in Indian population:a meta-analysis

Raju K. Mandal & Suraj S. Yadav & Aditya K. Panda

Received: 3 February 2014 /Accepted: 15 May 2014# International Society of Oncology and BioMarkers (ISOBM) 2014

Abstract p53 is a tumor suppressor gene, which is activatedin response to several forms of cellular stress and exertsmultiple antiproliferative functions, making it the most fre-quent target for genetic alteration in cancer. Various studieshave evaluated the association between p53 codon 72 G>C(rs1042522) polymorphism and risk of cancer. However, re-sults from the published studies remained inconclusive. Theaim of this study is to investigate the precise associationbetween this variant and a risk of cancer in a large-scalemeta-analysis. We searched the PubMed (MEDLINE) andGoogle Scholar web databases for studies regarding the asso-ciation of p53 codon 72 G>C polymorphism and risk ofcancer in the Indian population. Pooled odds ratio (OR) with95 % confidence interval (CI) were calculated by using ran-dom effect model to assess the association. Twenty studieswith 3,258 cancer cases and 4,260 healthy controls wereincluded. Overall, no significant association was detected forC allele carrier (C vs. G: OR=1.135, 95%CI=0.930 to 1.386,p=0.211) and homozygous (CC vs. GG: OR=1.200, 95 %CI=0.810 to 1.779, p=0.364), heterozygous (CG vs. GG:

OR=1.204, 95 % CI=0.921 to 1.575, p=0.175), dominant(CC+CG vs. GG: OR=1.231, 95 % CI=0.932 to 1.625, p=0.144), and recessive (CC vs. GG+GC: OR=1.078, 95 %CI=0.792 to 1.468, p=0.632) genetic models, respectively.No significant publication bias was observed by using Begg’sfunnel plot and Egger’s test. Present meta-analysis indicatedthat the p53 codon 72 G>C polymorphism was not associatedwith cancer risk. This suggests that this polymorphism maynot be an independent risk factor for cancer in the Indianpopulation.

Keywords Meta-analysis . p53 . Cancer . Polymorphism .

Susceptibility

Introduction

Cancer is a result of mutations that inhibit oncogene andtumor suppressor gene function, leading to uncontrolled cellgrowth, sustained angiogenesis, and finally, the ability toinvade and metastasize nearby tissues [1]. It is one of theleading causes of death in the world and has become a world-wide public health problem [2]. Many risk factors are associ-ated for the development of cancer, including tobacco use,alcohol, certain microbial infection, diet, exposure to radia-tion, and physical inactivity [3]. Moreover, epidemiologicalstudies suggested that environmental factors interplaying withlow-penetrance susceptibility genes significantly contribute tothe development of cancer [4]. Recently, genome-wide asso-ciation studies (GWASs) have provided a new insight into thegenetic basis of several malignancies [5]. Thus, it is anticipat-ed that the identification of a genetic polymorphism related tothe risk of cancer may improve the early diagnosis and ther-apeutic strategies of this incurable disease.

The p53 is a tumor suppressor gene (located on chromo-some 17p13) that plays a pivotal role in cell cycle arrest, DNA

R. K. Mandal (*)Department of Urology and Renal Transplantation, Sanjay GandhiPost Graduate Institute of Medical Sciences, Raibareli Road,Lucknow, Indiae-mail: dr.rajumandal@hotmail.com

S. S. YadavDepartment of Pharmacology and Therapeutics, King George’sMedical University, Lucknow, India

A. K. PandaCentre of Life Science, Central University of Jharkhand, Ranchi,India

Present Address:R. K. MandalCollege of Medicine, King Khalid University Hospital, Riyadh,Kingdom of Saudi Arabia

Tumor Biol.DOI 10.1007/s13277-014-2114-7

repair, senescence, and apoptosis [6, 7] and thereby protectsthe integrity of genome [8]. Inactivation of the p53 genethrough single-base substitution or mutation can damage itsDNA-binding properties and inhibit its normal function in cellcycle control and cell proliferation that promote carcinogene-sis or tumor progression [9, 10]. In vitro studies have showndifferent expressions of p53 that were associated withvarious types of cancer and highlighted its antitumor cellgrowth activity [11, 12]. Besides its role in tumor sup-pression, aberrant p53 expression also plays a significantrole in angiogenesis [13].

Many single nucleotide polymorphisms (SNPs) have beenidentified within the p53 gene, both in coding and noncodingregions [14]. The codon 72 polymorphism (rs1042522) islocated at exon 4 and is involved in guanine to cytosine (G>C) nucleotide exchange and arginine to proline (Arg>Pro)amino acid substitution. The arginine G72 allele increasesthe ability of p53 to locate to mitochondria, inducing apopto-sis, whereas proline allele C72 imparts a lower apoptoticpotential [15]. Studies have evaluated this polymorphism asa prognostic marker for human cancer [16].

Having known the functional significance of p53 genein cancer development, various case-control studies havebeen performed to explore the association between thep53 codon 72 G>C polymorphism and cancer suscepti-bility in the Indian population [17–36], but the results areinconclusive, partially due to the small sample size andlow statistical power in each of the published studies.Meta-analysis is a statistical tool for combining the resultsfrom individual studies where sample sizes are small andprovide more reliable results than a single study [37, 38].Larger sample sizes are always worthy for investigatingthe genetic risk factor associated with complex disease[39]. No meta-analysis has been executed for the Indianpopulation to reliably evaluate this association so far.Hence, we carried out this current meta-analysis to sum-marize all of those published studies and evaluated theassociation of p53 codon 72 G>C polymorphism withcancer risk.

Materials and methods

Publication search

The relevant studies were searched through the PubMed(MEDLINE) and Google Scholar electronic databases (lastsearch performed on December 2013). The search keywordsused were as follows: “p53 gene,” “polymorphism,” or “mu-tation” or “variation” and “cancer” and “India.”We evaluatedall the retrieved studies by reading the title and abstract. Thereference lists of all relevant articles were also searched toidentify additional studies.

Inclusion and exclusion criteria

The inclusion criteria were as follows: (a) must evaluate theassociation of p53 codon 72 G>C gene polymorphism andcancer risk in Indian population, (b) case-control design, (c)recruited histologically confirmed cancer patients and healthycontrols, and (d) provided genotype frequency of cases andcontrols. In addition, if more than one article used the samecase series, we selected the study that included the largestnumber of individuals. The major reasons for exclusion ofstudies were as follows: (a) overlapping of data, (b) case-onlystudies, (c) review articles, (d) editorials, and (e) animalstudies.

Data extraction and quality assessment

Two investigators independently extracted the data andreached a consensus on all the items according to the inclusionand exclusion criteria listed above. The major characteristicswere collected from the retrieved studies: last name of the firstauthor, year of publication, total number of cases and controls,cancer type, and genotype frequencies of cases and controls.

Statistical analysis

The strength of the association between the p53 codon 72 G>C polymorphism and cancer risk was evaluated by the pooledodds ratio (OR) with its corresponding 95 % confidenceinterval (CI). Heterogeneity between studies was performedby Q statistic and I2 test [40, 41]. A p value less than 0.05indicated the presence of heterogeneity. If heterogeneity wasstatistically significant, the random effect model [42] wasadopted; otherwise, fixed effect model [43] was used.Hardy-Weinberg equilibrium (HWE) in the controls was de-tected by a goodness-of-fit chi-square test. Potential publica-tion bias was estimated by using Begg’s funnel plots andEgger’s test. Egger’s test p value of <0.05 was considered tobe statistically significant [44]. All the statistical analyses wereperformed by comprehensive meta-analysis (CMA) V2 soft-ware (Biostat, USA). All p values were two-sided, and a pvalue of <0.05 was considered significant for any test. Toensure the reliability and accuracy of the results, two re-searchers independently entered the data in the statisticalsoftware and reached a consensus.

Results

Literature search and meta-analysis databases

A total of 85 articles were retrieved from the PubMed(MEDLINE) and Google Scholar web databases. All retrieved

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articles were examined by reading the titles, abstracts, and fulltexts for the potentially relevant publications. Studies eithershowing p53 gene polymorphism to predict survival in cancerpatients or considering p53 variants as an indicator for re-sponse to therapy were excluded. After carefully followinginclusion and exclusion criteria, a total number of 20 case-control studies concerning the association of p53 codon72 G>C polymorphism and cancer risk in the Indian popula-tion were eventually included in this meta-analysis.Information including cancer type, publication year, type ofcancer, number of cases and controls, distribution of geno-types (cases/controls), and minor allele frequency (MAF)(cases/controls) are presented in Tables 1 and 2.

Publication bias

Both Begg’s funnel plot and Egger’s test were performed toevaluate the publication bias among the included studies. Theappearance of the shape of funnel plots did not reveal anyevidence of obvious asymmetry for all genetic models. Then,Egger’s test was used to provide a statistical evidence offunnel plot. The results still did not show any evidence ofpublication bias for any of the studied genetic models(Table 3).

Table 1 Main characteristics of all 20 studies included in themeta-analysis

Authors Year Types of cancer Control Cases

Singhal et al. [17] 2013 Cervical 182 182

Tilak et al. [18] 2013 Lung 202 175

Devi et al. [19] 2013 HCC 93 93

Chauhan et al. [20] 2012 AML, ALL 199 230

Dunna et al. [21] 2012 AML, ALL 245 288

Pandith et al. [22] 2012 Kangri 200 106

Guleria et al. [23] 2011 Breast cancer 80 80

Ihsan et al. [24] 2011 Lung, gastric, oral 834 411

Mittal et al. [25] 2011 Prostate 265 177

Chauhan et al. [26] 2011 AML 202 120

Suresh et al. [27] 2010 HNC 52 47

Pandith et al. [28] 2010 Bladder 138 108

Sameer et al. [29] 2010 Colorectal 160 86

Syeed et al. [30] 2010 Breast 178 130

Sobti et al. [31] 2009 Lung 151 151

Misra et al. [32] 2009 Oral 342 308

Sreeja et al. [33] 2008 Lung 211 211

Gochhait et al. [34] 2007 Breast 333 243

Jain et al. [35] 2005 Lung 40 40

Tandle et al. [36] 2001 Oral 153 72

HCC hepatocellular carcinoma, AML acute myeloid leukemia, ALL acutelymphoblastic leukemia, HNC head and neck cancer

Table 2 Genotypic distribution of p53 codon 72 G>C (rs1042522) gene polymorphism included in the meta-analysis

Authors Year Controls Cancer cases

Genotype Minor allele Genotype Minor allele HWE

GG GC CC MAF GG GC CC MAF p value

Singhal et al. [17] 2013 30 82 70 0.60 64 88 30 0.40 0.47Tilak et al. [18] 2013 67 111 24 0.39 36 98 41 0.51 0.03Devi et al. [19] 2013 75 14 4 0.11 67 21 5 0.16 0.007Chauhan et al. [20] 2012 51 112 36 0.46 66 114 50 0.46 0.06Dunna et al. [21] 2012 79 123 43 0.42 123 111 54 0.38 0.68Pandith et al. [22] 2012 90 78 32 0.35 25 62 19 0.47 0.03Guleria et al. [23] 2011 27 32 21 0.46 11 47 22 0.56 0.08Ihsan et al. [24] 2011 191 429 214 0.51 95 232 84 0.48 0.39Mittal et al. [25] 2011 150 103 12 0.23 86 89 2 0.26 0.27Chauhan et al. [26] 2011 47 114 41 0.48 32 66 22 0.45 0.06Suresh et al. 2010 2010 17 24 11 0.44 8 25 14 0.56 0.64Pandith et al. [28] 2010 59 53 26 0.38 22 68 18 0.48 0.02Sameer et al. [29] 2010 65 63 32 0.39 10 37 39 0.66 0.02Syeed et al. [30] 2010 46 107 25 0.44 29 37 64 0.63 0.003Sobti et al. [31] 2009 42 73 36 0.48 37 73 41 0.51 0.69Misra et al. [32] 2009 85 159 98 0.51 87 155 66 0.46 0.26Sreeja et al. [33] 2008 98 76 37 0.35 70 84 57 0.46 0.001Gochhait et al. [34] 2007 76 160 97 0.53 86 109 48 0.42 0.52Jain et al. [35] 2005 6 24 10 0.55 19 17 4 0.31 0.17Tandle et al. [36] 2001 22 100 31 0.52 6 52 14 0.55 <0.0001

MAF minor allele frequency, HWE Hardy-Weinberg equilibrium

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Evaluation of heterogeneity

In order to test the heterogeneity among the studies,Q test andI2 statistics were employed. Heterogeneity was observed in allthe studied genetic models. Thus, random effect model wasused to synthesize the data (Table 3).

Association of p53 codon 72 G>C polymorphism and overallcancer susceptibility

We pooled all 20 studies together (3,258 cancer cases and4,260 controls) and evaluated the association of p53

codon 72 G>C polymorphism and overall cancer risk.Pooled OR indicated that codon 72 G>C polymorphismhas no role in increasing or decreasing the cancer risk invariant allele (C vs. G: OR=1.135, 95 % CI=0.930 to1.386, p=0.211), homozygous (CC vs. GG: OR=1.200,95 % CI=0.810 to 1.779, p=0.364), and heterozygous(CG vs. GG: OR=1.204, 95 % CI=0.921 to 1.575, p=0.175) genetic models. Similarly, no associations werefound in dominant (CC+CG vs. GG: OR=1.231, 95 %CI=0.932 to 1.625, p=0.144) and recessive (CC vs. GG+GC: OR=1.078, 95 % CI=0.792 to 1.468, p=0.632)genetic models (Figs. 1 and 2).

Table 3 Statistics to test publication bias and heterogeneity in the meta-analysis

Comparisons Egger’s regression analysis Heterogeneity analysis Model used for meta-analysis

Intercept 95 % confidence interval p value Q value pheterogeneity I2 (%)

G vs. C 3.19 −1.04 to 7.44 0.13 157.54 <0.0001 87.94 Random

GG vs. CC 1.76 −1.69 to 5.22 0.29 133.59 <0.0001 85.77 Random

GC vs. CC 2.01 −1.23 to 5.25 0.20 99.40 <0.0001 80.88 Random

GG+GC vs. CC 2.66 −0.85 to 6.17 0.12 121.02 <0.0001 84.30 Random

CC vs. GG+GC 1.00 −2.32 to 4.34 0.53 117.16 <0.0001 83.78 Random

Fig. 1 Forest plot of overall cancer risk associated with p53 codon 72 G>C gene polymorphism. The square and horizontal line represent thestudy-specific OR and 95 % CI

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Discussion

The genetic and epigenetic damage caused by environmentalcarcinogens is considered to be the development of cancer.Studies have supported the important role of genetics indetermining the molecular mechanism of cancer, and associ-ation studies are apposite for searching susceptibility genesinvolved in human malignancies [45].

Apoptosis plays a key role in maintaining tissue homeo-stasis by eliminating DNA-damaged cells, thereby protectingthe host from cancer development. Inappropriate regulation ofapoptosis is thought to be involved in carcinogenesis viaprolonging cell survival, accumulation of mutations, and en-hancing resistance to therapy [46]. The p53 gene is crucial forthe effective prevention of genetically damaged cells, eitherdirectly, by its participation in DNA repair mechanisms, orindirectly, by inducing apoptosis proteins [47]. Defects in thep53 pathways caused by mutation or polymorphism mayobstruct and deregulate the function of the p53 gene [9].Wild-type p53 demonstrated its antiproliferative effect bystimulation of p21cip1/waf1 protein that inhibits cyclin-dependent kinase activity and cell division [48]. The p53codon 72 G>C polymorphism could be a genetic risk factor

for cancer, because these two variants exhibit differentbiochemical and biological properties [49].

Due to the antiproliferative role of the p53 gene in humangenome, a large number of molecular epidemiological studieshave been conducted to examine the association of p53 codon72 G>C gene polymorphism with cancer risk, but the resultsfrom individual studies lack consensus. In order to provide thecomprehensive and robust conclusion, we performed the pres-ent meta-analysis from 20 independent case-control studies.To the best of our knowledge, this meta-analysis is the firststudy to investigate the association between p53 codon 72 G>C polymorphism and susceptibility to cancer in a large sampleof the Indian population. The overall pooled results of thismeta-analysis revealed that p53 codon 72 G>C polymor-phism did not influence an increased or decreased risk ofcancer in all genetic models. This indicates that the p53 codon72 G>C polymorphism is not a possible susceptibility factorfor cancer in the Indian population. It is possible that theanalyzed variant does not act as a primary susceptibilitypolymorphism and may be interacting with other causativegerm line polymorphisms found in linkage disequilibrium(LD) and inhibit p53 function. This finding was consistentwith that of other reported meta-analyses of hematological,

Fig. 2 Forest plot of overall cancer risk associated with p53 codon 72 G>C gene polymorphism. The square and horizontal line represent the study-specific OR and 95 % CI

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glioma, skin, and oral cancer risk [50–53]. The etiology ofcancer is very complex and partially understood, and it isknown that same polymorphism play dissimilar roles in can-cer susceptibility between different populations [54].

Some limitations of this meta-analysis should be acknowl-edged. First, in the present study, we found interstudy hetero-geneity in the overall analysis. Many factors might contributeto this heterogeneity, such as regional lifestyle varied amongdifferent parts of India [55], controls were not strictly defined;some studies used a healthy population and other selectedcancer-free hospital patients. To eliminate heterogeneity, weused a random effect model to pool the results wheneverheterogeneity was present. Second, gene-gene and gene-environment interactions were not analyzed.

The current meta-analysis has several strengths. First, sincepublication bias was not found in the present meta-analyses,this indicates that the results are statically robust. Second, weused a stringent data extraction strategy based on computer-assisted and manual searches to make a trustworthyconclusion.

Conclusion

In conclusion, this meta-analysis indicated that p53 codon72 G>C gene polymorphism was not associated with overallcancer risk. Further, larger sample size studies with the con-sideration of other p53 polymorphisms and gene-environmentand gene-gene interaction should be conducted to investigatethis association in the future.

Conflicts of interest None.

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