Research Article - (2020) Volume 14, Issue 3
Background: The loss of function in TP53 gene is an early event of carcinogenesis and is now considered as a full etiological factor in oral squamous cell carcinoma (OSCC). The most common polymorphism in this gene that is associated to OSCC and has been extensively studied as a potential risk factor for the development of malignancies is the single nucleotide polymorphism (SNP) encoding either proline or arginine at residue 72. Today, despite the multiplicity of publications and approaches used, the contradiction of the results have not remove the ambiguity of the possible impact of this polymorphism in OSCC. So we aimed this meta-analysis to investigate the distribution of TP53 codon 72 genotypes and alleles in patients with OSCC and healthy matched controls, by using an ethnicity subgroups analysis.
Method: A literature search was conducted to identify studies concerning TP53 codon 72 polymorphism and OSCC risk. Retrieved publications from 2000 to 2014 were classified by ethnicity, according to the sampling collection continent. Allelic frequencies were estimated by using genotypic data and Hardy-Weinberg Equilibrium and distributions were checked with Chi-2 test. Statistical tests for contrast models of association were performed with Proline allele as reference stratum.
Results: Arg allele distribution was almost similar in both cases and controls for African and Caucasian populations whereas Arg frequency was significantly greater in cases than in controls for Asians (p=0.011). After stratified statistical analyses, we’ve found again a significant association between Arginine allele and OSCC risk in the Asian subgroup (OR=1.31; 95% CI=1.09-1.58; P=0.004).
Conclusion: This current study revealed that allelic distribution of TP53 Arg72Pro polymorphism may depend on ethnicity and latitude, and highlighted that Arginine carrier in Asian populations may be considered to be predisposed to OSCC.
TP53 • Arg72Pro polymorphism • OSSC • Meta-analysis
CI: Confidence Interval • HPV: Human Papilloma Virus • HWE: Hardy-Weinberg Equilibrium • OR: Odds-Ratio • OSCC: Oral Squamous Cell Carcinoma • SNP: Single Nucleotide Polymorphism
Oral squamous cell carcinoma (OSCC) are multifactor tumors mainly correlated with tobacco smoke, alcohol abuse, betel nut chewing, high-risk human papillomavirus (HPV) infection and molecular alterations of genes involved in cell cycle regulation like TP53 . The inactivation or degradation of TP53, the main "guardian of the genome", is now considered as a full etiological factor in carcinogenesis and the main event that precedes most cancers . This gene is a tumor suppressor gene and is the most frequently mutated in human cancers. More than 50% of cancers in humans express a mutation in this gene [3-5]. The encoded protein mediates in the prevention of neoplastic transformation by repairing DNA damages after replication, or by inducing apoptosis in case DNA damages couldn’t be repaired .
In cancerous tissues, particularly in oral squamous cells, the loss of function at TP53 is generally associated with three molecular causes: the genotoxic effect of risk factors such as alcohol, tobacco, betel nut, the inhibitory effect of the TP53 antagonist gene MDM2 or the oncotic action of E6-HPV [7-9]. Now-a-days, the study of the TP53 gene signaling pathways, as well as the control of its protein stability or expression constitutes the new approaches to anticancer research [10,11].
TP53 is a sequence-specific DNA-binding transcription factor comprising four domains: a highly charged acidic region (transactivation domain), a hydrophobic proline-rich region (protein interaction domain), a central region (DNA binding domain), and a highly basic COOH-terminal region (oligomerization domain) .
The most common TP53 polymorphism that is associated to OSCC and has been extensively studied as a potential risk factor for the development of malignancies is the single nucleotide polymorphism (SNP) encoding either proline or arginine at residue 72 within the proline-rich domain [4,5,8,13]. This non-conservative amino acid change is associated with altered electrophoretic mobility in the mutant protein that contain Arg72 allele, thus suggesting a structural or conformational modification of the TP53 protein .
Several reports have described differences in functional properties of wild-type TP53, including susceptibility to malignant transformation, ubiquitinmediated degradation by high-risk HPV E6-protein, induction of apoptosis or cell cycle progression after damage [1,9,12,14,15].
The possible impact of TP53 codon 72 genotypes on malignancies development yet remains an ongoing issue of debate because of contradictory results of statistical and in silico analysis. Hou and colleagues in 2015 tried to illustrate these contradictory results through the picture bellow, showing different trends and weights of association between Arg72 allele of TP53 protein and OSCC risk through 12 studies  (Figure 1). These contradictory results could be explained by the different geographic distribution of genotypic frequency of TP53 codon 72 polymorphism. The allelic frequency is said to be depend on ethnicity and geographic latitude [17-21].
For this purpose, the aim of this meta-analysis was to investigate the distribution of TP53 codon 72 genotypes and alleles in patients with OSCC and healthy matched controls, and study the impact of this amino-acid variability on the risk of developing OSCC, by using ethnicity subgroups analysis.
Publications selection process
Were included in this meta-analysis all case-control studies published before December 2018, concerning TP53 Arg72Pro polymorphism and OSCC, with detailed genotypic frequencies in the cases and controls. We excluded all review articles, meta-analysis and case-only studies.
We carried out a comprehensive search in PubMed and Science Direct databases with the following keywords: ‘‘TP53 codon 72’’ - ‘‘Arg72Pro polymorphism’’ - ‘‘oral cancer’’ - ‘‘oral squamous cell carcinoma’’. All eligible studies were retrieved, and their references were checked for additional relevant publications.
The quality of the included studies was assessed according to the concordance of Hardy-Weinberg equilibrium (HWE) for the genotypic distribution in controls, to detect possible selection bias [16,22,23]. Studies with genotypic distribution of controls in concordance with HWE (P>0.05) were defined as high-quality studies, while studies inconsistent with HWE (P ≤ 0.05) were classified as low-quality studies.
A symetry Begg’s funnel plot that allows detecting potential publication bias in a meta-analytic study was also performed to assess the quality of publications. Publications in the inefficiency zone, which deviate from confidence funnel-shaped interval, are therefore suspected to be biased [24-30].
Allelic frequencies were estimated by using genotyping data extracted from selected publications and HWE formula. Stratified analysis was performed according to three ethnic groups and geographical latitude: Africans corresponding to black-skinned Sahelian and Sub-Saharan, Asians are populations living in East, Southeast and Central Asia and Caucasians are white European and white non-hispanic American.
We first check the distribution of Arginine and Proline alleles in ethnic subgroups by using Chi-2 test. STATA version 12.0 software (Stata Corporation, College Station, TX) was used for this statistical comparison.
Statistical tests for contrast models of association were performed with Proline allele as reference stratum. The strength of the association was estimated through an online dedicated website by the pooled odds ratio (OR) with its corresponding 95% confidence interval (95% CI) and P-value . The pooled ORs were calculated for following contrasts models, using Proline allele as reference stratum : Arg/Arg vs. Pro/Pro, Arg/Arg vs. Arg/Pro, Arg/Pro vs. Pro/Pro, Arg/Arg + Arg/Pro vs. Pro/Pro, Arg/Arg vs. Arg/Pro + Pro/Pro and Allele Arg vs. Allele Pro.
A total of 16 studies met the inclusion criteria (Figure 2). Publications characteristics are summarized in Table 1. Overall number of participants was 9024 including 4288 cases and 4736 controls.
|First author [Reference]||Country||Year of publication||Ethnicity||Genotyping Methods||Samples Size (case/control)|
|Ba S A ||Senegal||2014||African||PCR-RFLP||51/87|
|Chen X ||Texas, USA||2008||Caucasian||PCR-RFLP||326/349|
|Hamel N ||Montreal, Canada||2000||Caucasian||PCR-RFLP||163/163|
|Ji X ||Texas, USA||2008||Caucasian||PCR-RFLP||188/342|
|Kietthubthew S ||Southern Thailand||2003||Asian||PCR-RFLP||97/97|
|Lin Y C ||Taiwan||2008||Asian||PCR-RFLP||297/280|
|Lu J ||Texas, USA||2007||Caucasian||PCR-RFLP||716/719|
|Mojtahedi Z ||Iran||2010||Asian||PCR-SSP||132/123|
|Perrone F ||Italia||2007||Caucasian||Two-step PCR||77/141|
|Scheckenbach K ||Germany||2004||Caucasian||RT-PCR ; Direct sequencing||122/193|
|Shen H ||Texas, USA||2002||Caucasian||PCR-RFLP||304/333|
|Sina M ||Iran||2014||Asian||ARMS-PCR (Alleles spec.)||55/100|
|Tu H F ||Taiwan||2008||Asian||Direct sequencing||189/116|
|Twu C W ||Taiwan||2006||Asian||PCR-RFLP||53/53|
|Yang W ||China||2008||Asian||Direct sequencing||435/550|
|Yu H ||Texas, USA||2011||Caucasian||PCR-RFLP||1083/1090|
|Overall :||9024 participants||4288/4736|
Note: PCR: Polymerase Chain Reaction, ARMS-PCR: Amplification Refractory Mutation System PCR, PCR-RFLP: PCR-Restriction Fragment Length Polymorphism, PCR-SSP: PCR-Sequence Specific Primers, RT-PCR: Reverse Transcriptase PCR.
Data extracted from included studies and their quality assessment according to HWE test in controls is presented in Table 2. Two publications with genotype distribution in controls departing from HWE were classified as low quality studies while the other fourteen publications in agreement with HWE were defined as high-quality studies [1,3-5,7-9,12,13,15,25-30].
|First author [Reference]||Sample size (case/control)||Genotype & allelic frequencies in cases||Genotype & allelic frequencies in controls||P-value of HWE in controls||HWE test in controls|
% - (n)
% - (n)
% - (n)
Arg/ Pro (%)
% - (n)
% - (n)
% - (n)
Arg/ Pro (%)
|Ba S A ||51/87||6 % (3)||47 % (24)||47 % (24)||29,4/70,6||14 % (12)||38 % (33)||48 % (42)||32,8/67,2||0,159||High quality|
|Chen X ||326/349||56,1 % (183)||37,1 % (121)||6,8 % (22)||74,6/25,4||51,9 % (181)||41,2 % (144)||6,9 % (24)||72,5/27,5||0,740||High quality|
|Hamel N ||163/163||54,0 % (88)||41,7 % (68)||4,3 % (7)||74,8/25,2||58,3 % (95)||37,4 % (61)||4,3 % (7)||77,0/33,0||0,576||High quality|
|Ji X ||188/342||54,8 % (103)||39,4 % (74)||5,8 % (11)||74,5/25,5||52,4 % (179)||40,9 % (140)||6,7 % (23)||72,8/27,2||0,734||High quality|
|Kietthubthew S ||97/97||33 % (32)||45 % (44)||22 % (21)||55,5/44,5||36 % (35)||35 % (34)||29 % (28)||53,5/46,5||0,003||Low quality***|
|Lin Y C ||297/280||33,2 % (96)||52,2 % (155)||15,5 % (46)||59,3/40,7||25,7 % (72)||54,3 % (152)||20,0 % (56)||52,8/47,2||0,370||High quality|
|Lu J ||716/719||55,9 % (400)||37,7 % (270)||6,4 % (46)||74,7/25,3||54,0 % (388)||40,2 % (289)||5,8 % (42)||74,1/25,9||0,636||High quality|
|Mojtahedi Z ||132/123||37,1 % (49)||47,7 % (63)||15,2 % (20)||55,5/44,5||35,8 % (44)||51,2 % (63)||13,0 % (16)||61,4/38,6||0,422||High quality|
|Perrone F ||77/141||81,8 % (63)||10,4 % (8)||7,8 % (6)||87,0/13,0||59,6 % (84)||33,3 % (47)||7,1 % (10)||76,2/23,8||0,420||High quality|
|Scheckenbach K ||122/193||54 % (66)||1 % (1)||45 % (55)||54,5/45,5||59 % (114)||7 % (13)||34 % (66)||62,5/37,5||0,000||Low quality***|
|Shen H ||304/333||52,0 % (158)||41,1 % (125)||6,9 % (21)||72,5/27,5||52,6 % (175)||40,2 % (134)||7,2 % (24)||72,7/27,3||0,898||High quality|
|Sina M ||55/100||36,4 % (20)||45,5 % (25)||18,2 % (10)||59,1/40,9||40,0 % (40)||48,0 % (48)||12,0 % (12)||64,0/36,0||0,676||High quality|
|Tu H F||189/116||28,0 % (53)||56,1 % (106)||15,9 % (30)||56,1/43,9||35,3 % (41)||51,7 % (60)||13,0 % (15)||61,1/38,9||0,378||High quality|
|Twu C W ||53/53||22,6 % (12)||51,0 % (27)||26,4 % (14)||48,1/51,9||41,5 % (22)||45,3 % (24)||13,2 % (7)||64,1/35,9||0,879||High quality|
|Yang W ||435/550||85,7 % (373)||4,4 % (19)||9,9 % (43)||87,9/12,1||49,6 % (273)||35,8 % (197)||14,6 % (80)||67,5/32,5||0,065||High quality|
|Yu H ||1083/1090||54,8 % (593)||37,4 % (405)||7,8 % (85)||73,5/26,5||54,8 % (597)||39,3 % (428)||5,9 % (65)||74,4/25,6||0,741||High quality|
|Overall||4288/4736||53,4 % (2292)||35,8 % (1535)||10,8 % (461)||71,3/28,7||49,7 % (2352)||39,4 % (1867)||10,9 % (517)||69,4/30,6||0,469||High quality|
|Stratified by ethnicity|
|African||51/87||6 % (3)||47 % (24)||47 % (24)||29,4/70,6||14 % (12)||38 % (33)||48 % (42)||32,8/67,2||0,159||High quality|
|Asian||1258/1319||50,5 % (635)||34,9 % (439)||14,6 % (184)||67,9/32,1||40 % (527)||43,8 % (578)||16,2 % (214)||61,9/38,1||0,475||High quality|
|Caucasian||2979/3330||55,5 % (1654)||36 % (1072)||8,5 % (253)||73,5/26,5||54,5 % (1813)||37,7 % (1256)||7,8 % (261)||73,3/26,7||0,721||High quality|
Figure 3. Begg’s funnel plot analysis to detect publication bias with pseudo 95% confidence limits. Each point represents an individual study for the indicated association; the x-axis shows the results of studies expressed as odds-ratio; the y-axis shows the studies precisions; the middle solid line indicates the overall effect from the meta-analysis.
Allelic distribution: We have first classified studies in three ethnic subgroups according to the samplings continents: 1 publication with 51 cases and 87 controls was African, 7 articles with 1258 cases and 1319 controls were Asians and 8 articles with 2979 cases and 3330 controls were Caucasians [1,3-5,7-9,12,13,15,25-30].
The allelic distributions presented in Table 2 showed that in Africa and in Caucasians, the Arginine allele distribution is almost similar in both cases and controls (Africa: 29.4% in cases, 32.8% in controls; Caucasians: 73.5% in cases, 73.3% in controls) while a clear difference was noticed in Asia (67.9% in cases, 61.9% in controls). Comparison by khi-2 test of Arg allelic frequencies between cases and controls in Table 3 was statistically significant in Asian subgroups (P=0.011). We then concluded that Arginine allele of codon 72 of TP53 may be a risk factor for OSCC in Asian population.
|Ethnicity||Arg allele cases vs controls|
|Arg allele frequencies||z-value||p-value|
Contrast models analysis in subgroups: In order to validate the hypothesis aforementioned in allelic distribution and check out the effect of Arginine allele in the different ethnic groups, we tested three statistical parameters (Odds ratio, p-value and 95% CI) in contrast models of association, by using the proline allele as a reference stratum (Table 4).
|Arg/Arg vs. Pro/Pro||Arg/Arg vs. Arg/Pro||Arg/Pro vs. Pro/Pro||Arg/Arg + Arg/Pro vs. Pro/Pro||Arg/Arg vs. Arg/Pro + Pro/Pro||Allele Arg vs. Allele Pro|
|Test of association|
|Ethnicity||Number of studies||Sample size
[CI 95 %]
[CI 95 %]
[CI 95 %]
[CI 95 %]
[CI 95 %]
[CI 95 %]
We found in Asian subgroup significant associations between the TP53 Arg72Pro polymorphism and OSCC risk for 4 contrasts models : Arg/ Arg vs. Pro/Pro [OR=1.4; 95% CI=1.11-1.76; P=0.003]; Arg/Arg vs. Arg/ Pro [OR=1.59; 95% CI=1.34-1.88; P=0.000]; Arg/Arg vs. Arg/Pro + Pro/ Pro [OR=1.53; 95% CI=1.31-1.79; P=0.000] and Allele Arg vs. Allele Pro [OR=1.31; 95% CI=1.09-1.58; P=0.004].
Forest plots based on contrast models that we have tested were drawn to have an overall watch on meta-analysis results (Figure 4).
Figure 4. Forest plots of OSCC risk associated with the TP53 Arg72Pro polymorphism in all included studies, stratified by ethnicity. Boxes represent the ORs of individual studies; diamonds represent the summary ORs; horizontal lines represent the 95% CIs. a. Arg/Arg vs. Pro/Pro. b. Arg/Arg vs. Arg/Pro. c. Arg/Pro vs. Pro/Pro. d. Arg/Arg + Arg/Pro vs. Pro/Pro. e. Arg/Arg vs. Arg/Pro + Pro/Pro. f. Allele Arg vs. Allele Pro.
Oral cancer is a public health problem in many countries and ranks among the ten most frequently diagnosed cancers all over the world, according to the 2018 GLOBOCAN report . Since the identification of the TP53 codon 72 polymorphism as potential risk factor for cancers, numerous studies have been devoted to explore the genetic effect of this polymorphism and its association with oral cancer risk. Some studies claimed that this SNP is a risk factor for oral cancer while other studies detected no effect in oral carcinogenesis, showing clear discrepancy between results across the world. The different patterns noticed in allelic distribution and genotypic frequencies of this polymorphism may be due to variations in methodological approaches, sample size, laboratory performance, DNA quality and source, variation in ethnic background or environmental exposures of study populations [16,32,33].
We have performed an ethnicity-stratified meta-analysis to check the association between TP53 codon 72 polymorphism and OSCC risk. Overall, 16 publications with 9024 subjects (4288 cases and 4736 controls) were included in this study. Comparison of Arg allele frequencies between cases and controls was statistically significant only in Asian subgroup. Also, we’ve found significant associations between the TP53 Arg72Pro polymorphism and OSCC risk for four contrasts models in Asians. Hence, this polymorphism was not significantly associated with any increased or a decreased risk of OSCC in Africans and Caucasians.
Several stratified meta-analysis based on TP53 Arg72Pro polymorphism and different types of cancer have been performed to date, but evidence of the implication of this polymorphism remains unclear.
A meta-analysis by Koushik et al. in 2004 on cervical cancer risk and TP53 Arg72Pro polymorphism revealed that the homozygote Arg/Arg genotype was significantly associated with an increased susceptibility to cervical cancer . Also, a meta-analysis in Iran about breast cancer found that the risk of developing breast cancer is 1.58 higher in Iranian women with genotype Arg/ Arg (95% CI: 1.01 to 2.45) .
On the contrary, Zhuo et al. in 2009 demonstrated that the Arg allele was associated with a decreased risk of nasopharyngeal cancer . Another meta-analysis on nasopharyngeal cancer published in 2016 has meanwhile evidenced that the TP53 codon 72 polymorphism could be as a risk factor .
Concerning oral cancer risk, a meta-analysis pooling nine studies published until May 2009 (1990 cases and 2074 controls) showed no association; likewise, another one published in 2013 with seventeen studies stratified according to ethnicity confirmed no significant association in Asians, Caucasians and mixed populations [22,38]. To our knowledge, our meta-analysis is the first to investigate the association of TP53 codon 72 polymorphism with OSCC risk in the three ethnic populations, Caucasians, Asians and Africans and highlighted that Arg allele of TP53 codon 72 could be considered as a risk factor for OSCC in Asian population.
The present meta-analysis shows that Arginine allele of TP53 codon 72 polymorphism was significantly associated with OSCC risk in Asian population whereas any significant link was observed in Africans and Caucasians. Our study also confirmed that genotypes distribution of the TP53 codon 72 polymorphism may be dependent on ethnicity and geographic latitude. Further extensive studies using functional approaches are then required to confirm the real influence of Arg72Pro polymorphism on OSCC risk.
The authors thank all colleagues who participated in this work, particularly Mr. Thierno Dieng (Head of Obstetrics and Gynecological Clinic, Aristide Le Dantec Hospital, Dakar-Senegal) and Mr. Cheikh Loucoubar (Head of the Epidemiology Unit, Clinical Research and Data Science Department of Institut Pasteur de Dakar, Senegal) for their invaluable contributions during statistical analysis.
No potential conflicts of interest were disclosed.