Skip to main content
Download PDF

p.Arg72Pro polymorphism of P53 and breast cancer risk: a meta-analysis of case-control studies

BMC Medical Genetics volume 21, Article number: 206 (2020) Cite this article

Abstract

Background

The effect of the p.Arg72Pro variant of the P53 gene on the risk of development ofbreast cancer remains variable in populations. However, the use ofstrategies such aspoolingage-matched controls with disease may provide a consistent meta-analysis. Our goal was to perform a meta-analysis in order to assess the association of p.Arg72Pro variant of P53 gene with the risk of breast cancer.

Methods

Databases such as PubMed, Genetics Medical Literature, Harvard University Library, Web of Science and Genesis Library were used to search articles. Case-control studies with age-matched on breast cancer havingevaluated the genotype frequencies of the TP53 p.Arg72Pro polymorphism were selected. The fixed and random effects (Mantel-Haenszel) were calculated using pooled odds ratio of 95% CI to determine the risk of disease. Inconsistency was calculated to determine heterogeneity among the studies. The publication bias was estimated using the funnel plot.

Results

Twenty-one publications with 7841 cases and 8876 controls were evaluated in this meta-analysis. Overall, our results suggested that TP53 p.Arg72Pro was associated with the risk of breast cancer for the dominant model (OR = 1.09, 95% CI = 1.02–1.16, P = 0.01) and the additive model (OR = 1.09, 95% CI = 1.01–1.17, P = 0.03), but not for the recessive model (OR = 1.07, 95% CI = 0.97–1.18, P = 0.19). According to the ethnic group analysis, Pro allele was associated with the risk of breast cancer in Caucasians for the dominant model and additive model (P = 0.02), and Africans for the recessive model and additive model (P = 0.03).

Conclusions

This meta-analysis found a significant association between TP53 p.Arg72Pro polymorphism and the risk of breast cancer. Individuals carrying at least one Pro allele were more likely to have breast cancer than individuals harboring the Arg allele.

Peer Review reports

Background

Breast cancer is a multifactorial disease which constitutes a major public health problem [1]. In 2018, the World Health Organization reported that 2.09 million new cases of breast cancer were detected [1] compared to 1.38 million cases in 2008 [2]. It is the leading cause of death in women around the world. It should be noted that the incidence of breast cancer differs among different populations around the world [1]. Over the past decades, major advances have been made in understanding the pathology of breast cancer at the molecular level, including the involvement of certain genes associated with the development of the disease such as BRCA1, BRCA2 and P53 which produce tumor suppressor proteins and participate in damaged DNA repair [3,4,5]. P53 plays a key role in the regulation of cell proliferation and apoptosis. The P53 protein is essential for maintaining the integrity of the cell and its components. In human cancers, mutated P53 produces abnormal proteins that alter or inhibit transcriptional regulation [6]. As a result, the stress response, cell cycle as well as apoptosis are affected. Inactivation or mutation of P53 gene would lead in linkage disequilibrium in the DNA sequence, which, associated with chromosomal aberrations induce the appearance of genomic instability and later the development of cancer [7, 8]. The molecular signature of human cancers shows that this gene is frequently observed in its mutated form [9]. P53 has been mapped on chromosome 17p13 and contains 11 exons. Several single nucleotide polymorphisms (SNP) have been identified and the most studied variant is the substitution of Arginine by Proline at position 72 in exon 4. Studies carried out on different populations around the world have shown that this SNP is associated with the development of numerous diseases including cancers [10, 11]. It should be noted that, many association studies have examined the relationship between the SNP p.Arg72Pro of P53 gene and the risk of breast cancer, however, the reports from these studies remain conflicting as some studies have shown that p.Arg72Pro is associated with the risk of breast cancer, while others found no association. Menzel et al. 2004 [12] and Akkiprik et al. 2009 [13] in their investigations showed a link between p.Arg72Pro and the risk of breast cancer. However, other authors who carried out a case-control study in which participants ages were not matched in a similar population, have concluded that p.Arg72Pro was not associated with the risk of breast cancer [14]. These different results with diverging conclusions can be explained by a very strong heterogeneity in allele and genotype distribution of p.Arg72Pro of the P53 gene. This heterogeneity may be related not only to the geographic and ethnic origin [15,16,17] but also to the study design such as non-age-matched case-control studies. Based on these above observations, we hypothesized that the p.Arg72Pro polymorphism of P53 gene may represent a potentially important genetic marker, contributing to breast cancer susceptibility in Caucasian, Asians and Africans. The present meta-analysis included only age-matched case-control studies in order to statistically decrease the heterogeneity between the studies, to qualitatively assess the effect of p.Arg72Pro on the risk of breast cancer. We have performed an independent two-stage meta-analysis; overall and sub-group analysis.

Methods

Literature search

The Pubmed Genetics Medical Literature Database, the Harvard University Library, and the Web of Science and Genesis Library were used to identify available articles published in English. The keywords “P53”, “p.Arg72Pro” and “polymorphism” or “mutation” or “gene” and “breast cancer”cited in the genetic association studies were used to detect and select scientific manuscripts in these databases. We also reviewed references cited in these studies to identify additional articles that were not identified by our research in the databases.

Inclusion criteria

The inclusion criteria included: (1) published case-control studies as an original article to evaluate the association between p.Arg72Pro of the P53 gene and the risk of breast cancer, (2) full manuscript available, (3) case-control study with age-matched, (4) distribution of genotype respecting Hardy-Weinberg equilibrium (HWE) in controls, (5) availability of the three genotypic frequencies (Arg/Arg, Arg/Pro and Pro/Pro) in the case and control groups. (6) Study no influencing the pooled odd ratio (OR) values. Three investigators independently evaluated each study to determine eligibility.

Data extraction

The data were collected by an investigator and verified by a second investigator to reach consensus on all points. First author, year of publication, country, ethnicity of study population, sample size, age-matched, distribution of genotype andalleles, as well as the recalculation of HWE in controls were extracted from the eligible studies. A third reviewer made a contradictory assessment to reconcile the assumptions. The data of controls evaluated with p.Arg72Pro variant were included in this meta-analysis.

Statistical analysis

Chi2 analysis with a significance level of P < 0.05 was used to evaluate whether p.Arg72Pro polymorphism distribution of the P53 gene in controls fits HWE. The association between the p.Arg72Pro and the risk of breast cancer was evaluated by the Odd ratio (OR) of 95% CI. We evaluated the strength of association between the p.Arg72Pro polymorphism of P53 gene and the risk of breast cancer using different genetic models, including the dominant (Pro/Pro + Arg/Pro vs. Arg/Arg), recessive (Pro/Pro vs. Arg/Arg + Arg/Pro) and the additive (Pro vs. Arg). Heterogeneity among the studies was assessed by I2 statistical test [18, 19]. If I2 > 50% (presence of heterogeneity), the random effects model was used to calculate the overall OR, otherwise in case of lack of heterogeneity, the fixed effects method was used. We also have examined the funnel plot to determine publication bias [20]. All statistical analyses were performed with Review Manager Software version 5.1.

Results

Characteristic of eligible studies

Figure 1 summarizes the process of selecting studies that the inclusion criteria. In sum, 21 eligible age-matched case-control studies were selected for the pooled OR analyses. Genotype distribution of the control population that met HWE was a minimum requirement for studies to be retained for the meta-analysis. Out of the 21 studies (7841cases and8876 controls), eleven were Caucasians [12,13,14, 23,24,25,26,27,28, 36, 37], nine were Asians [21, 29,30,31,32,33,34,35, 38] and one was African [22] (Table 1).

Fig. 1
figure1

Flow diagram of the studies evaluated for meta-analysis

Full size image
Table 1 Genotypes distribution of TP53 p.Arg72Pro in breast cancer cases and controls
Full size table

Quantitative analysis

Table 2 shows pooled ORs and heterogeneity testresults of the association between the TP53 p.Arg72Pro polymorphism and the risk of breast cancer. Overall, a slightly association of TP53 p.Arg72Pro polymorphism with the risk of breast cancer was observed for the dominant (OR = 1.09, 95% CI = 1.02–1.16, P = 0.01, Fig. 2) and additive (OR = 1.09, 95% CI = 1.01–1.17, P = 0.03, Fig. 3) models, but not for the recessive model (OR = 1.07, 95% CI = 0.97–1.18, P = 0.19, Fig. 4). In the subgroup analyzes, except the recessive model (OR = 1.18, 95% CI = 0.96–1.44; P = 0.12), we noted a moderate association of p. Arg72Pro with the risk of breast cancer for the dominant (OR = 1.09, 95% CI = 1.01–1.17, P = 0.02) and additive (OR = 1.07, 95% CI = 1.01–1.14, P = 0.02) models in Caucasians (Fig. 5). When considering the Asian population, the different genetic models showed no trend (recessive: OR = 1.01, 95% CI = 0.87–1.17, P = 0.88; dominant: OR = 1.06, 95% CI = 0.94–1.20; P = 0.33; additive; OR = 1.06, 95% CI = 0.91–1.23, P = 0.46) (Fig. 6). The only eligible African study showed that the TP53 p.Arg72Pro polymorphism is highly associated with the risk of breast cancer as well in the recessive model (OR = 2.14, 95% CI = 1.08–4.23, P = 0.03) than in the additive model (OR = 1.49, 95% CI = 1.03–2.16, P = 0.03).

Table 2 Distribution of TP53 p.Arg72Pro polymorphism according to the different genetic models
Full size table
Fig. 2
figure2

Forest plots of the association between breast cancer and TP53 p.Arg72Pro polymorphism for the dominant model. The black diamond denotes the pooled OR; blue squares indicate the OR in each study with square sizes inversely proportional to the standard error of the OR; and horizontal lines represent the 95% CI

Full size image
Fig. 3
figure3

Forest plots of the association between breast cancer and TP53 p.Arg72Pro polymorphism for the recessive model. The black diamond denotes the pooled OR; blue squares indicate the OR in each study with square sizes inversely proportional to the standard error of the OR; and horizontal lines represent the 95% CI

Full size image
Fig. 4
figure4

Forest plots of the association between breast cancer and TP53 p.Arg72Pro polymorphism for the additive model. The black diamond denotes the pooled OR; blue squares indicate the OR in each study with square sizes inversely proportional to the standard error of the OR; and horizontal lines represent the 95% CI

Full size image
Fig. 5
figure5

Forest plots of the association between breast cancer and TP53 p.Arg72Pro polymorphism for the a dominant model, b recessive model and c additive model in Caucasians. The black diamond denotes the pooled OR; blue squares indicate the OR in each study with square sizes inversely proportional to the standard error of the OR; and horizontal lines represent the 95% CI

Full size image
Fig. 6
figure6

Forest plots of the association between breast cancer and TP53 p.Arg72Pro polymorphism for the a dominant model, b recessive model and c additive model in Asians. The black diamond denotes the pooled OR; blue squares indicate the OR in each study with square sizes inversely proportional to the standard error of the OR; and horizontal lines represent the 95% CI

Full size image

Sensitive analysis

To maintain the stability of the meta-analysis after the non-inclusion of deviant studies of HWE, we evaluated the influence of each study on pooled OR. After the exclusion of studies [39,40,41,42],no study has shown a significant influence of pooled OR effect and p-values for the different genetic models (Table 2).

Sources of heterogeneity

To avoid large heterogeneity, we excluded studies in which the distribution of genotypes deviated from the HWE equilibrium. The sensitivity analysis overall, showed moderate heterogeneity (I2 < 50%) in the recessive and dominant models. We noted the same tendency of heterogeneity when considering all the data (I2 = 47%, P = 0.01) and among Asians (I2 = 54%, P = 0.03) for the additive model (Table 2). In addition, we compared the pooled OR of the fixed and random effects, no statistically significant difference was found between the two effects, which supports strongly the consistency of the present study’s data.

Publication Bias

The publication bias was assessed using the funnel plot. After the exclusion of studies deviating from HWE and those influencing the pooled ORs values, no significant publication bias was found in the different genetic models (Fig. 7).

Fig. 7
figure7

Funnel plots of dominant a, recessive b and additive c model precision by OR

Full size image

Discussion

Like other multifactorial diseases, the causes of breast cancer are not known. However, several factors combine their effects for the development of the disease. These factors are of clinical, biological, environmental and genetic origin [43, 44]. From a genomic point of view, it has been reported that genetic polymorphisms of the p53 gene can influence the development of cancers [45]. However, the mechanism by which these polymorphisms affect cancer development remain unknown. Functionally, these polymorphisms alter alternative splicing and thus affecting mRNA stability and protein synthesis. The normal P53 gene produces a protein that plays a key role in DNA repair, cell cycle control and apoptosis [45]. Through this physiological role P53 acts as a guardian of the genome, preventing the malignant transformation of normal cells. In the event of a mutation, the function of p53 is impaired, leading to the appearance of malignant cells and later cancerous disease [46,47,48]. It has been reported that the R72 variant of the P53 mutant in addition to influencing the onset of cancer is also associated with a bad prognosis through the rapid onset of metastasis [49].

In the present meta-analysis, we examined the relationship between TP53 p.Arg72Pro polymorphism and the risk of breast cancer. Overall, our findings showed that the dominant and additive models were associated with an increased risk of breast cancer for the carriers of 72Pro allele. These results corroborate with the data reported from two recent meta-analyzes, the first covering eleven studies with 950 cases and 882 controls in the Asian population [50] and the second performed on the Indian population which covered seven studies with 1249 cases and 1838 controls [51]. These authors in their analysis showed that the dominant and the additive models were associated with the risk of breast cancer. Contrary to our results, other meta-analyzes found conflicting results [52, 53]. The works of Zhuo et al. 2009, Francisco et al. 2011, Ma et al. 2011 and Concalves et al. 2014 also reported a decreased risk of breast cancer with the different genetic models applied [54,55,56,57]. These differences might be explained by the samples size, types of allelic variant and eligible studies included. In the subgroup analysis, our meta-analysis revealed a high risk of breast cancer with TP53 p.Arg72Pro in Caucasians (dominant model and additive model) and Africans (recessive and additive models). These trends were consistent with previous studies [12, 13, 22] but inconsistent with the findings of other studies [52, 53]. However, Jafrin et al. 2020 concluded that TP53 p.Arg/Pro was associated with the risk of breast cancer in the South Asian population. The difference between the studies could be explained by the ethnicity and study design. The effects of ethnicity may be due to several factors, allelic heterogeneity, gene-gene and gene-environment interaction and linkage disequilibrium [58,59,60,61]. In the previous meta-analyzes, the selection criteria of studies were not sufficiently robust such as inclusion of the age-unmatched case-control studies and the inclusion of studies with control groups not satisfying HWE [62,63,64,65,66,67,68,69,70,71,72,73,74,75]. The major advantage of the present meta-analysis was the inclusion of a large number of samples, including very selective criteria to measure the strength of the association between this polymorphism in exon 4 of TP53 gene and the risk of breast cancer using different genetic models. However, several limitations need to be highlighted, sample size and small number of case-control age-matched studies in ethnic groups.

Conclusion

In the light of this meta-analysis, we noticed that individuals carrying at least one Pro allele of the P53 gene are more likely to have breast cancer with dominant and additive models than individuals harboring the wild-type Arg allele. Our study further strengthened and confirmed the hypothesis that the P53 gene is usually mutated in about half of breast cancer cases. For the stability and homogeneity of results from meta-analysis, future similar studies should take into account selection criteria for articles such as no deviation from HWE in the control group and the matching of cases and controls according to age.

Availability of data and materials

The dataset analyzed for thisstudyis available from the Additional file 1.

Abbreviations

Arg :

Arginine

CI:

Confidence interval

Fig.:

Figure

HWE:

Hady-Weinberg Equilibrium

I2 :

Inconsistency

N:

Number

OR:

Odd ratio

P:

P value

Pro :

Proline

SNP:

Single nucleotide polymorphisms

References

  1. 1.

    Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424.

    Google Scholar 

  2. 2.

    Ferlay J, Shin H-R, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010;127:2893–917.

    CAS  PubMed  Article  Google Scholar 

  3. 3.

    Kaur RP, Vasudeva K, Kumar R, Munshi A. Role of p53 gene in breast Cancer: focus on mutation Spectrum and therapeutic strategies. Curr Pharm Des. 2018;24:3566–75.

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Gallardo-Alvarado LN, Tusié-Luna MT, Tussié-Luna MI, et al. Prevalence of germline mutations in the TP53 gene in patients with early-onset breast cancer in the Mexican population. BMC Cancer. 2019;19:118.

    PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Diakite B, Kassogue Y, Dolo G, Kassogue O, Keita ML, Joyce B, et al. Association of PIN3 16-bp duplication polymorphism of TP53 with breast cancer risk in Mali and a meta-analysis. BMC Med Genet. 2020;21:142.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

    Aubrey BJ, Strasser A. Kelly GLTumor-suppressor functions of the TP53 pathway. Cold Spring HarbPerspect Med. 2016;6:a026062.

    Article  CAS  Google Scholar 

  7. 7.

    Damineni S, Rao VR, Kumar S, et al. Germline mutations of TP53 gene in breast cancer. Tumour Biol. 2014;35:9219–27.

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Mhawech P, Kinkel K, Vlastos G, Pelte M-F. Ovarian carcinomas in endometriosis: an immunohistochemical and comparative genomic hybridization study. Int J GynecolPathol. 2002;21:401–6.

    Article  Google Scholar 

  9. 9.

    Sagne C, Marcel V, Amadou A, Hainaut P, Olivier M, Hall J. A meta-analysis of cancer risk associated with the TP53 intron 3 duplication polymorphism (rs17878362): geographic and tumor-specific effects. Cell Death Dis. 2013;4:e492.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Kung CP, Leu JI, Basu S, et al. The P72R polymorphism of p53 predisposes to obesity and metabolic dysfunction. Cell Rep. 2016;14:2413–25.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Zhou Y, Li N, Zhuang W, Liu G-J, Wu T-X, Yao X, et al. P53 codon 72 polymorphism and gastric cancer: a meta-analysis of the literature. Int J Cancer. 2007;121:1481–6.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  12. 12.

    Menzel H-J, Sarmanova J, Soucek P, Berberich R, Grünewald K, Haun M, et al. Association of NQO1 polymorphism with spontaneous breast cancer in two independent populations. Br J Cancer. 2004;90:1989–94.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Akkiprik M, Sonmez O, Gulluoglu BM, Caglar HB, Kaya H, Demirkalem P, et al. Analysis of p53 gene polymorphisms and protein over-expression in patients with breast cancer. Pathol Oncol Res. 2009;15:359–68.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  14. 14.

    Krivokuca AM, Malisic EJ, Dobricic JD, Brotto KV, Cavic MR, Jankovic RN, et al. RAD51 135G>C and TP53 Arg72Pro polymorphisms and susceptibility to breast cancer in Serbian women. Familial Cancer. 2014;13(2):173–80.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  15. 15.

    Dahabreh IJ, Schmid CH, Lau J, Varvarigou V, Murray S, Trikalinos TA. Genotype misclassification in genetic association studies of the rs1042522 TP53 (Arg72Pro) polymorphism: a systematic review of studies of breast, lung, colorectal, ovarian, and endometrial cancer. Am J Epidemiol. 2013;177:1317–25.

    PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Bisof V, Salihović MP, Narancić NS, Skarić-Jurić T, Jakić-Razumović J, Janićijević B, et al. TP53 gene polymorphisms and breast cancer in Croatian women: a pilot study. Eur J Gynaecol Oncol. 2010;31:539–44.

    CAS  PubMed  Google Scholar 

  17. 17.

    Habyarimana T, Attaleb M, Mugenzi P, Mazarati JB, Bakri Y, El Mzibri M. Association of p53 codon 72 polymorphism with breast Cancer in a Rwandese population. Pathobiology. 2018;85:186–91.

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7:177–88.

    CAS  Article  Google Scholar 

  19. 19.

    Higgins JPT. In: Green S, editor. Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011]: The Cochrane Collaboration; 2011. Available from http://handbook.cochrane.org.

  20. 20.

    Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315:629–34.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Alshatwi AA, Hasan TN, Shafi G, Alsaif MA, Al-Hazzani AA, Alsaif AA. A single-nucleotide polymorphism in the TP53 and MDM-2 gene modifies breast cancer risk in an ethnic Arab population. Fundam Clin Pharmacol. 2012;26:438–43.

    CAS  PubMed  Article  Google Scholar 

  22. 22.

    Ayoubi SE, Elkarroumi M, El Khachibi M, HassaniIdrissi H, Ayoubi H, Ennachit S, et al. The 72Pro variant of the tumor protein 53 is associated with an increased breast Cancer risk in the Moroccan population. Pathobiology. 2018;85:247–53.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  23. 23.

    Buyru N, Tigli H, Dalay N. P53 codon 72 polymorphism in breast cancer. Oncol Rep. 2003;10:711–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Cherdyntseva NV, Denisov EV, Litviakov NV, Maksimov VN, Malinovskaya EA, Babyshkina NN, et al. Crosstalk between the FGFR2 and TP53 genes in breast cancer: data from an association study and epistatic interaction analysis. DNA Cell Biol. 2012;31:306–16.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  25. 25.

    Costa S, Pinto D, Pereira D, Rodrigues H, Cameselle-Teijeiro J, Medeiros R, et al. Importance of TP53 codon 72 and intron 3 duplication 16bp polymorphisms in prediction of susceptibility on breast cancer. BMC Cancer. 2008;8:32.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  26. 26.

    Cox DG, Deer D, Guo Q, Tworoger SS, Hankinson SE, Hunter DJ, et al. The p53 Arg72Pro and MDM2–309 polymorphisms and risk of breast cancer in the nurses’ health studies. Cancer Causes Control. 2007;18:621–5.

    PubMed  Article  PubMed Central  Google Scholar 

  27. 27.

    Denisov EV, Cherdyntseva NV, Litvyakov NV, Slonimskaya EM, Malinovskaya EA, Voevoda MI, et al. TP53 mutations and Arg72Pro polymorphism in breast cancers. Cancer Genet Cytogenet. 2009;192:93–5.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  28. 28.

    Ebner F, Schremmer-Danninger E, Rehbock J. The role of TP53 and p21 gene polymorphisms in breast cancer biology in a well specified and characterized German cohort. J Cancer Res Clin Oncol. 2010;136:1369–75.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  29. 29.

    Hossain A, Murshid GMM, MNH Z, Islam F, Sultana R, Sultana T, et al. TP53 codon 72 polymorphism and breast cancer risk in Bangladeshi population. Breast Cancer. 2017;24:571–8.

    PubMed  Article  PubMed Central  Google Scholar 

  30. 30.

    Isakova J, Talaibekova E, Aldasheva N, Vinnikov D, Aldashev A. The associationof polymorphic markers Arg399Gln of XRCC1 gene, Arg72Pro of TP53 gene and T309Gof MDM2 gene with breast cancer in Kyrgyz females. BMC Cancer. 2017;17(1):758.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  31. 31.

    Katiyar S, Thelma BK, Murthy NS, Hedau S, Jain N, Gopalkrishna V, et al. Polymorphism of the p53 codon 72 Arg/pro and the risk of HPV type 16/18-associated cervical and oral cancer in India. Mol Cell Biochem. 2003;252:117–24.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  32. 32.

    Li T, Lu ZM, Guo M, Wu QJ, Chen KN, Xing HP, et al. p53 codon 72 polymorphism (C/G) and the risk of human papillomavirus-associated carcinomas in China. Cancer. 2002;15(95):2571–6.

    Article  CAS  Google Scholar 

  33. 33.

    Ma H, Hu Z, Zhai X, Wang S, Wang X, Qin J, et al. Joint effects of single nucleotide polymorphisms in P53BP1 and p53 on breast cancer risk in a Chinese population. Carcinogenesis. 2006;27:766–71.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  34. 34.

    Sharma S, Sambyal V, Guleria K, Manjari M, Sudan M, Uppal MS, et al. TP53 polymorphisms in sporadic north Indian breast cancer patients. Asian Pac J Cancer Prev. 2014;15:6871–9.

    PubMed  Article  PubMed Central  Google Scholar 

  35. 35.

    Song F, Zheng H, Liu B, Wei S, Dai H, Zhang L, et al. An miR-502-binding site single-nucleotide polymorphism in the 3′-untranslated region of the SET8 gene is associated with early age of breast cancer onset. Clin Cancer Res. 2009;15:6292–300.

    CAS  PubMed  Article  Google Scholar 

  36. 36.

    Sprague BL, Trentham-Dietz A, Garcia-Closas M, Newcomb PA, Titus-Ernstoff L, Hampton JM, et al. Genetic variation in TP53 and risk of breast cancer in a population-based case control study. Carcinogenesis. 2007;28:1680–6.

    CAS  PubMed  Article  Google Scholar 

  37. 37.

    Wang-Gohrke S, Becher H, Kreienberg R, Runnebaum IB, Chang-Claude J. Intron 3 16 bp duplication polymorphism of p53 is associated with an increased risk for breast cancer by the age of 50 years. Pharmacogenetics. 2002;12:269–72.

    CAS  PubMed  Article  Google Scholar 

  38. 38.

    Zhang W, Jin MJ, Chen K. Association of p53 polymor-phisms and its haplotypes with susceptibility of breast cancer. Zhejiang Da XueXue Bao Yi Xue Ban. 2007;36:561–6.

    CAS  Google Scholar 

  39. 39.

    Arfaoui A, Douik H, Kablouti G, Chaaben AB, Handiri N, Zid Z, et al. Role of p53 Codon72 SNP in breast Cancer risk and Anthracycline resistance. AnticancerRes. 2015;35:1763–9.

    Google Scholar 

  40. 40.

    Faghani M, Nikbahkt M, Salehi M, Rabbani M, Talebi A, Soleima-ni B, et al. Study of p53 polymorphism at codon 72 in patients of breast cancer in Isfahan. J Isfahan Med School. 2007;25:26–33.

    Google Scholar 

  41. 41.

    Chabnaz S, Ahmed MU, Islam MS, Islam MR, Al-Mamun MM, Islam MS, et al. Breast cancer risk in relation to TP53 codon 72 and CDH1 gene polymorphisms inthe Bangladeshi women. Tumour Biol. 2016;37:7229–37.

    Article  CAS  Google Scholar 

  42. 42.

    Yadav P, Masroor M, Tanwer K, Mir R, Javid J, Ahmad I, et al. Clinical significance of TP53 (R72P) and MDM2 (T309G) polymorphisms in breast cancer patients. Clin Transl Oncol. 2016;18:728–34.

    CAS  PubMed  Article  Google Scholar 

  43. 43.

    Veronesi U, Boyle P, Goldhirsch A, Orecchia R, Viale G. Breast cancer. Lancet. 2005;365:1727–41.

    PubMed  Article  Google Scholar 

  44. 44.

    Yager JD, Davidson NE. Estrogen carcinogenesis in breast cancer. N Engl J Med. 2006;354:270–82.

    CAS  PubMed  Article  Google Scholar 

  45. 45.

    Stracquadanio G, Wang X, Wallace MD, Grawenda AM, Zhang P, Hewitt J, et al. The importance of p53 pathway genetics in inherited and somatic cancer genomes. Nat Rev Cancer. 2016;16:251–65.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Candeias MM, Malbert-Colas L, Powell DJ, Daskalogianni C, Maslon MM, Naski N, et al. P53 mRNA controls p53 activity by managing Mdm2 functions. Nat Cell Biol. 2008;10:1098–105.

    CAS  PubMed  Article  Google Scholar 

  47. 47.

    Dumont P, Leu JI, Della Pietra AC 3rd, George DL, Murphy M. The codon 72 polymorphic variants of p53 have markedly different apoptotic potential. NatGenet. 2003;33:357–65.

    CAS  Google Scholar 

  48. 48.

    Rohaly G, Chemnitz J, Dehde S, Nunez AM, Heukeshoven J, Deppert W, et al. A novel human p53 isoform is an essential element of the ATR-intra-S phase checkpoint. Cell. 2005;122:21–32.

    CAS  PubMed  Article  Google Scholar 

  49. 49.

    Basu S, Gnanapradeepan K, Barnoud T, Kung CP, Tavecchio M, et al. Mutant p53 controls tumor metabolism and metastasis by regulating PGC-1α. Genes Dev. 2018;32:230–43.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Jafrin S, Aziz MA, Anonna SN, Akter T, Naznin NE, Reza S, et al. Association of TP53 codon 72 Arg>pro polymorphism with breast and lung Cancer risk in the south Asian population: a meta-analysis. Asian Pac J Cancer Prev. 2020;21:1511–9.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  51. 51.

    Akhter N, Dar SA, Chattopadhyay S, Haque S, Anwer R, Wahid M, et al. Impact of p53 arg72pro SNP on breast Cancer risk in north Indian population. Curr Genomics. 2018;19(5):395–410..

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Khan MH, Khalil A, Rashid H. Evaluation of the p53 Arg72Pro polymorphism and its association with cancer risk: aHuGE review and meta-analysis. Genet Res (Camb). 2015;97:e7.

    Article  CAS  Google Scholar 

  53. 53.

    Hou J, Jiang Y, Tang W, Jia S. p53 codon 72 polymorphism and breast cancer risk: A meta-analysis. Exp Ther Med. 2013;5:1397–402 2013.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Zhuo W, Zhang Y, Xiang Z, Cai L, Chen Z. Polymorphisms of TP53 codon 72 with breast carcinoma risk: evidence from 12226 cases and 10782 controls. J Exp Clin Cancer Res. 2009;28:115.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  55. 55.

    Francisco G, Menezes PR, Eluf-Neto J, Chammas R. Arg72Pro TP53 polymorphism and cancer susceptibility: a comprehensive meta-analysis of 302 case-control studies. Int J Cancer. 2011;129:920–30.

    CAS  PubMed  Article  Google Scholar 

  56. 56.

    Ma Y, Yang J, Liu Z, Zhang P, Yang Z, Wang Y, et al. No significant association between the TP53 codon 72 polymorphism and breast cancer risk: a meta-analysis of 21 studies involving 24,063 subjects. Breast Cancer Res Treat. 2011;125:201–5.

    PubMed  Article  Google Scholar 

  57. 57.

    Gonçalves ML, Borja SM, Cordeiro JABL, Saddi VA, Ayres FM, Vilanova-Costa CAST, et al. Association of the TP53 codon 72 polymorphism and breast cancer risk: a meta-analysis. Springerplus. 2014;3:749.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  58. 58.

    Thomas D. Gene–environment-wide association studies: emerging approaches. Nat Rev Genet. 2010;11:259–72.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  59. 59.

    Wood AR, Hernandez DG, Nalls MA, Yaghootkar H, Gibbs JR, Harries LW, et al. Allelic heterogeneity and more detailed analyses of known loci explain additional phenotypic variation and reveal complex patterns of association. Hum Mol Genet. 2011;20:4082–92.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    Rosenberg NA, Huang L, Jewett EM, Szpiech ZA, Jankovic I, Boehnke M. Genome-wide association studies in diverse populations. Nat Rev Genet. 2010;11:356–66..

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. 61.

    Murphy ME, Liu S, Yao S, Huo D, Liu Q, Dolfi SC. At al. A functionally significant SNP in TP53 and breastcancer risk in African-American women. NPJ. Breast Cancer. 2017;3:5.

    PubMed  Google Scholar 

  62. 62.

    Ohayon T, Gershoni-Baruch R, Papa MZ, Distelman Menachem T, Eisenberg Barzilai S, Friedman E. The R72P P53 mutation is associated with familial breast cancer in Jewish women. Br J Cancer. 2005;92:1144–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. 63.

    Henríquez-Hernández LA, Murias-Rosales A, Hernández González A, Cabrera De León A, Díaz-Chico BN, Mori De Santiago M, et al. Gene polymorphisms in TYMS, MTHFR, p53 and MDR1 as risk factors for breast cancer: a case-control study. Oncol Rep. 2009;22:1425–33.

    PubMed  Article  CAS  Google Scholar 

  64. 64.

    Papadakis EN, Dokianakis DN, Spandidos DA. p53 codon 72 polymorphism as a risk factor in the development of breast cancer. Mol Cell Biol Res Commun. 2000;3:389–92.

    CAS  PubMed  Article  Google Scholar 

  65. 65.

    Noma C, Miyoshi Y, Taguchi T, Tamaki Y, Noguchi S. Association of p53 genetic polymorphism (Arg72Pro) with estrogen receptor positive breast cancer risk in Japanese women. Cancer Lett. 2004;210:197–203.

    CAS  PubMed  Article  Google Scholar 

  66. 66.

    Damin APS, Frazzon APG, Damin DC, Roehe A, Hermes V, Zettler C, et al. Evidence for an association of TP53 codon 72 polymorphism with breast cancer risk. Cancer Detect Prev. 2006;30:523arf–9.

    Article  CAS  Google Scholar 

  67. 67.

    Mahasneh AA, Abdel-Hafiz SS. Polymorphism of p53 gene in Jordanian population and possible associations with breast cancer and lung adenocarcinoma. Saudi Med J. 2004;25:1568–73.

    PubMed  PubMed Central  Google Scholar 

  68. 68.

    Nordgard SH, Alnaes GIG, Hihn B, Lingjaerde OC, Liestøl K, Tsalenko A, et al. Pathway based analysis of SNPs with relevance to 5-FU therapy: relation to intratumoral mRNA expression and survival. Int J Cancer. 2008;123:577–85.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  69. 69.

    Rajkumar T, Samson M, Rama R, Sridevi V, Mahji U, Swaminathan R, et al. TGFbeta1 (Leu10Pro), p53 (Arg72Pro) can predict for increased risk for breast cancer in south Indian women and TGFbeta1 pro (Leu10Pro) allele predicts response to neo-adjuvant chemo-radiotherapy. Breast Cancer Res Treat. 2008;112(1):81–7.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  70. 70.

    Singh V, Rastogi N, Mathur N, Singh K, Singh MP. Association of polymorphism in MDM-2 and p53 genes with breast cancer risk in Indian women. Ann Epidemiol. 2008;18:48–57.

    PubMed  Article  Google Scholar 

  71. 71.

    Alawadi S, Ghabreau L, Alsaleh M, Abdulaziz Z, Rafeek M, Akil N, et al. P53 gene polymorphisms and breast cancer risk in Arab women. Med Oncol 2011;28:709–15..

  72. 72.

    Aoki MN, da Silva do Amaral Herrera AC, Amarante MK, do Val Carneiro JL, MHP F, MAE W. CCR5 and p53 codon 72 gene polymorphisms: implications in breast cancer development. Int J Mol Med. 2009;23:429–35.

    CAS  PubMed  Google Scholar 

  73. 73.

    Hrstka R, Beranek M, Klocova K, Nenutil R, Vojtesek B. Intronic polymorphisms in TP53 indicate lymph node metastasis in breast cancer. Oncol Rep. 2009;22:1205–11.

    CAS  PubMed  Google Scholar 

  74. 74.

    Kazemi M, Salehi Z, Chakosari RJ. TP53 codon 72 polymorphism and breast cancer in northern Iran. Oncol Res. 2009;18:25–30.

    CAS  PubMed  Article  Google Scholar 

  75. 75.

    Suresh K, Venkatesan R, Chandirasekar R, Kumar BL, Sasikala K. Association of Trp53 arg72pro polymorphic variants with breast cancer – a case control study in south Indian population. Biol Med. 2011;3:15–22.

    CAS  Google Scholar 

Download references

Acknowledgments

The authors thank Harvard University, Boston University, Northwestern University, and University of New Mexico (HBNU) Consortium, Global Health, Fogarty International Center and the NIH for their training support. Wealso thankthe Faculty of Medicine and Odontostomatology of the Universitédes Sciences, Techniques et Technologies de Bamako, the University Clinical Research Center (UCRC-Mali), Intelligence Center of Excellence Mali (ICER-Mali) particularly Prof Cheick Fantamady Traore, Prof. Mamadou Diakite and Dr. Mamadou Coulibaly for logistical support.

Funding

Research reported in this publication was supported by the HBNU Consortium, Fogarty International Center and the National Institutes of Health under Award Number D43 TW010543. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Author notes

  1. Lifang Hou and MamoudouMaiga are Last author.

Affiliations

  1. Faculty of Medicine and Odontostomatology, 1805, Université des Sciences, des Techniques et des Technologies Sciences de Bamako (USTTB), Hamdallaye ACI, 2000, Bamako, Mali

    Brehima Diakite, Yaya Kassogue, Guimogo Dolo, Oumar Kassogue, Cheick B Traore, Bakarou Kamate, Seydou Doumbia & Mamoudou Maiga

  2. Teaching Hospital Center of Point G, 333, Bamako, Mali

    Brehima Diakite, Yaya Kassogue, Guimogo Dolo, Oumar Kassogue, Mamadou L Keita, Cheick B Traore, Bakarou Kamate & Seydou Doumbia

  3. Preventive Medicine Department, Cancer Epidemiology and Prevention, Northwestern University, Chicago, IL, 60611, USA

    Brehima Diakite, Yaya Kassogue, Jun Wang, Etienne Dembele, Robert L Murphy, Lifang Hou & Mamoudou Maiga

  4. Institute for Global Health, Northwestern University, IL60611, Chicago, USA

    Jun Wang, Etienne Dembele, Robert L Murphy, Lifang Hou & Mamoudou Maiga

  5. Department of Radiology, College of Medicine, University of Illinois at Chicago, Chicago, IL, 60612, USA

    Erin Neuschler

  6. Hassan II University Aïn chock, 20000, Casablanca,19, Rue Tarik Ibnou Ziad,, Morocco

    Sellama Nadifi

Authors
  1. Brehima Diakite
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yaya Kassogue
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guimogo Dolo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Erin Neuschler
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Oumar Kassogue
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mamadou L Keita
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Cheick B Traore
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Bakarou Kamate
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Etienne Dembele
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Sellama Nadifi
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Robert L Murphy
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Seydou Doumbia
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Lifang Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Mamoudou Maiga
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors read and approved the final manuscript. Study concept and design: BD, YK, GD, JW, EN, OK, MLK, CBT, BK, ED, SN,SD, LH, MM. Acquisition, analysis and interpretationof data: BD, YK, GD,MM. Drafting of the manuscript: BD with assistance from by YK, MM. Critical revision of the manuscript for important intellectualcontent: JW, EN, SN, GD, SD. Obtaining supervision: LH, RLM.

Corresponding author

Correspondence to Brehima Diakite.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Additional file 1.

Availability of all data and references with PubMed accession numbers.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Diakite, B., Kassogue, Y., Dolo, G. et al. p.Arg72Pro polymorphism of P53 and breast cancer risk: a meta-analysis of case-control studies. BMC Med Genet 21, 206 (2020). https://doi.org/10.1186/s12881-020-01133-8

Download citation

Keywords

  • P53 gene
  • p.Arg/pro polymorphism
  • Breast cancer
  • Meta-analysis

BMC Medical Genetics

ISSN: 1471-2350

Contact us