Polymorphisms of progesterone receptor and ovarian cancer risk: A systemic review and meta-analysis

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1 bs_bs_banner doi: /jog J. Obstet. Gynaecol. Res. Vol. 41, No. 2: , February 2015 Polymorphisms of progesterone receptor and ovarian cancer risk: A systemic review and meta-analysis Jing Liao, Dong Ding, Chaoyang Sun, Danhui Weng, Li Meng, Gang Chen and Ding Ma Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Abstract Aim: Growing bodies of studies have investigated the associations between three progesterone receptor (PGR) polymorphisms, +331G/A, Alu insertion and Val660Leu, and susceptibility to ovarian cancer, but the results remain controversial and inconclusive. Thus, we conducted a meta-analysis to derive a more precise estimation of the associations. Methods: A total of 21 case control studies from 16 publications that included analyses of Alu insertion (981 cases, 2136 controls), Val660Leu (2205 cases, 3222 controls) and +331G/A (2842 cases, 4305 controls) polymorphisms were identified. Results: Significantly increased risks of ovarian cancer were found for Alu insertion (T 2 T 2 + T 1T 2 vs T 1T 1; odds ratio [OR], 1.504; 95% confidence interval [CI], ) and Val660Leu (TT vs GT; OR, 1.524; 95% CI, ). No significant association was found between +331G/A polymorphism and ovarian cancer. Conclusion: This meta-analysis suggests that the two polymorphisms of PGR, Alu insertion and Val660Leu, may contribute to ovarian cancer susceptibility as low-penetrance risk factors. Key words: meta-analysis, ovarian cancer, ovarian cancer risk, polymorphism, progesterone receptor. Introduction Ovarian cancer is one of the most lethal gynecological malignancies. Worldwide, there are almost newly diagnosis cases and deaths annually from ovarian cancer. 1 As a multifactorial and complex process, the etiology of this pathology is still not fully understood. The greatest risk factors are related to hormonal exposure and reproduction. Two major hypotheses have been formulated to explain the etiology of ovarian cancer. One is the incessant ovulation hypothesis which supposes that ovarian cancer develops as a result of repeated disruption of the ovarian surface and that the disruption may stimulate cellular proliferation and malignant transformation of the ovarian epithelium. 2 The other is the gonadotropin hypothesis which supposes that endogenous hormones are involved in ovarian cancer etiology. 3,4 Genetic factors also are known to influence the development of ovarian cancer. Highly penetrant mutations of BRCA1 and BRCA2 genes are present only in a minority of patients with ovarian cancer and breast cancer. Thus, low-penetrance genes may account for a larger proportion of cancer cases. 5 Received: April Accepted: June Reprint request to: Dr Gang Chen and Dr Ding Ma, Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, Hubei Province , China. s: gumpc@126.com and dma@tjh.tjmu.edu.cn Funding: This study was supported by grants from the National Science Foundation of China (nos , , and ) The Authors

2 PGR polymorphisms and ovarian cancer risk The progesterone receptor (PGR) gene is located at 11q22-q23. 6,7 PGR exists in two isoforms, PGR-A and PGR-B, produced by the single gene with two different promoters and translational start sites. 8,9 Several polymorphisms in the PGR gene have been identified. One of the polymorphisms is known as PROGINS, which consists of the Alu insertion in intron G, the Val660Leu polymorphism in exon 4 and the Hist770Hist polymorphism in exon 5. Another polymorphism is described as a G-to-A exchange at position +331 of the promoter region and this polymorphism increases the expression of the PGR-B isoform. 10 The association between these polymorphisms and ovarian cancer risk has received considerable attention. Several studies have reported the roles of the Alu insertion, the Val660Leu and the +331G/A polymorphisms in ovarian cancer risk. However, the results are still inconsistent and controversial, partially because of the possible slight effect and the relatively small sample size in each of the published studies. Therefore, we conducted this meta-analysis to clarify the effect of the three PGR polymorphisms, the Alu insertion, Val660Leu and the +331G/A polymorphisms, on ovarian cancer risk. Methods Study identification To identify all researches that examined the associations of three PGR polymorphisms, Alu insertion, Val660Leu and +331G/A, with ovarian cancer risk, we performed an extensive search of PubMed, Embase and Chinese Biomedical Database for eligible articles published up to December The following terms were used: ( ovary OR ovarian ) AND ( cancer OR cancers OR carcinoma OR carcinomas OR neoplasm OR neoplasms ) AND ( progesterone receptor OR progesterone receptors OR pgr ) AND ( polymorphism OR polymorphisms OR snp OR snps ). We evaluated relevant publications by checking their titles and abstracts and then obtained the most relevant publications for detailed examination. References of the retrieved publications were also screened for other relevant studies. Only published studies with full text articles were included. If an article reported results including different studies, each study was treated as a separate comparison in our meta-analysis. If more than one article was published by the same author using the same cases, only the most recent or complete study was selected. Inclusion and exclusion criteria The eligible studies included in this meta-analysis were required to meet the following selection criteria: (i) case control studies including ovarian cancer cases and healthy controls; (ii) evaluation of at least one of the three progesterone receptor polymorphisms and ovarian cancer risk; and (iii) describing useful genotype frequencies for estimating an odds ratio (OR) with 95% confidence interval (CI). Exclusion criteria were: (i) non-case control studies; (ii) not evaluating the association between at least one of the three polymorphisms; (iii) a control population which included malignant patients; and (iv) studies that included duplicate data. Data extraction Information was carefully extracted from all eligible publications by two investigators independently according to the inclusion criteria above. For each study, the following data were collected: surname of the first author, year of publication, country of origin, ethnicity, source of cases and controls, total numbers of cases and controls, genotyping methods, source of DNA, and genotype frequency of cases and controls. Disagreement was resolved by discussion between the two investigators. If these two investigators could not reach a consensus, a third investigator was consulted to resolve the dispute and a final majority decision was made. Quality score assessment The quality of the studies was also assessed by the same two reviewers independently according to the predefined scale for quality assessment in Table 1. Any disagreement was resolved by discussion between the two reviewers and consultation with a third reviewer. These scores have been described previously and are based on both traditional epidemiological considerations and cancer genetic issues. 11,12 Quality scores ranged 0 15 and we defined a score of less than 10 as low quality and scores of 10 or more as high quality. Statistical analysis The allelic frequency of control group in each study was calculated and assessed for Hardy Weinberg equilibrium (HWE) using the χ 2 -test. Crude OR with 95% CI were used to assess the strength of association between the three PGR polymorphisms and ovarian cancer risk. The pooled OR were calculated for Alu 2014 The Authors 179

3 J. Liao et al. Table 1 Scale for quality assessment Criteria Score Source of cases Selected from population or cancer registry 3 Selected from hospital 2 Selected from pathology archives, but without 1 description Not described 0 Source of controls Population-based 3 Blood donors or volunteers 2 Hospital-based (cancer-free patients) 1 Not described 0 Specimens of cases for determining genotypes White blood cells or normal tissues 3 Tumor tissues or exfoliated cells of tissue 0 Hardy Weinberg equilibrium in controls Hardy Weinberg equilibrium 3 Hardy Weinberg disequilibrium 0 Total sample size but < but <500 1 <200 0 insertion polymorphism under allele (T 2 vs T 1), homozygous model (T 2T 2 vs T 1T 1), heterozygous model (T 2T 2 vs T 1T 2,T 1T 2 vs T 1T 1), recessive model (T 2T 2 vs T 1T 2 + T 1T 1) and dominant model (T 2T 2 + T 1T 2 vs T 1T 1). The same contrasts were performed for the Val660Leu polymorphism and +331G/A polymorphism. P < 0.05 was considered significant. Stratified analyses were also performed by ethnicity, HWE and quality score of studies. The Q-test was used to assess the heterogeneity among the studies. For P-values of less than 0.10 or I 2 of more than 50%, the random-effects model (DerSimonian Laird method) was used. 13 Otherwise, a fixed-effect model (the Mantel Haenszel method) was used. 14 Sensitivity analysis was performed to assess the stability of the results. A single study involved in the meta-analysis was deleted each time to reflect the influence of the individual dataset on the pooled OR. The potential publication bias was estimated by visual inspection of the Begg funnel plots, in which the standard error of log (OR) of each study was plotted against its log (OR). 15 The symmetry of the funnel plot was further evaluated by Egger s linear regression test (P < 0.05 was considered indicative of significant publication bias). 16 All statistical tests were performed with STATA version 11.0 software (Stata, College Station, TX, USA). Results Study characteristics This study focused on the association between the three PGR polymorphisms and the risk of ovarian cancer. Through the published work search and selection based on the inclusion criteria, 16 articles that included 21 studies were identified finally , The main characteristics of the included studies are presented in Tables 2 and 3, which were reported in eight, 17,21 23,25,26 seven 17,27,30 33 and eight 18 20,24,29,30,32 studies, respectively. All included articles were written in English. The publishing year ranged There were eleven studies of Caucasians, one of Asians, and nine studies of mixed races. Quality scores for individual studies ranged Deviation from HWE of genotype frequencies among the controls was detected in three studies Meta-analysis result Table 4 lists the main results of this meta-analysis for the association between the three polymorphisms, Alu insertion, Val660Leu and +331G/A, and the risk of ovarian cancer. Alu insertion No association was found between the risk of ovarian cancer and the variant genotypes of Alu insertion polymorphism under allele comparison (OR = 1.304, 95% CI = , P = 0.132), homozygote model (OR = 1.741, 95% CI = , P = 0.332), heterozygous model (OR = 1.568, 95% CI = , P = for T 2T 2 vs T 1T 2; OR = 1.056, 95% CI = , P = for T 1T 2 vs T 1T 1) and recessive model (OR = 1.695, 95% CI = , P = 0.360). However, under the dominant model, a significantly increased risk was found (OR = 1.504, 95% CI = , P = 0.036) (Fig. 1). Val660Leu The overall data showed no significant association between Val660Leu polymorphism and ovarian cancer risk under allele comparison (OR = 1.083, 95% CI = , P = 0.357), homozygote model (OR = 1574, 95% CI = , P = 0.243), heterozygous model (OR = 1.000, 95% CI = , P = for GT vs GG), recessive model (OR = 1.366, 95% CI = , P = 0.124) and dominant model (OR = 1.312, 95% CI = , P = 0.194). Limiting the analysis to the Caucasians or to the studies The Authors

4 PGR polymorphisms and ovarian cancer risk Table 2 Main characteristics of studies included in the meta-analysis First author Year Country Race Case source Control source Source of DNA Sample size (case/control) Quality score Alu insertion polymorphism Leite 2008 Brazil Mix Hospital Unknown Buccal cells 80/282 6 Lancater 2003 America Unknown Cancer registry Population Blood 309/ Mckenna Ireland Unknown Unknown Unknown Blood 41/83 6 Mckenna Germany Unknown Unknown Unknown Blood 26/101 6 Agoulnik USA Caucasian Hospital Blood donors Tissue 84/440 9 Agoulnik USA Asian Hospital Hospital Blood 114/512 8 Lancaster 1998 USA Caucasian Hospital Hospital Blood 96/101 6 Manolitsas 1997 UK Unknown Unknown Unknown Unknown 231/220 9 Val660Leu polymorphism Romano 2006 The Netherlands Caucasian Hospital Volunteers Tissue 67/ Terry 2005 USA Caucasian Cancer registry Population Blood 896/ Pearce 2005 USA Caucasian Cancer registry Population Blood 267/ Tong 2001 Australia Unknown Hospital Volunteers Blood 226/ Spurdle 2001 Australia Caucasian Hospital Population Blood or tissue 551/ Agoulnik USA Caucasian Hospital Blood donors Tissue 84/440 9 Agoulnik USA Asian Hospital Hospital Blood 114/ G/A polymorphism Ludwig 2009 Poland Caucasian Hospital Blood donors Blood 215/ Delort 2008 France Unknown Unknown Unknown Blood 51/ Risch 2006 USA Mix Cancer registry Population Buccal cells 487/ Romano 2006 The Netherlands Caucasian Hospital Volunteers Tissue 52/ Terry 2005 USA Caucasian Cancer registry Population Blood 920/ Berchuck USA and Australia Caucasian Cancer registry Population Blood 438/ Berchuck USA and Australia Unknown Hospital Volunteers Blood 535/ Jakubowska 2010 Poland Caucasian Cancer registry Cancer registry Blood 144/ The Authors 181

5 J. Liao et al. Table 3 Genotype distribution and frequency of all included studies First author Year Cases Controls Alu insertion polymorphism Total T 1T 1 T 1T 2 T 2T 2 T 1 T 2 Total T 1T 1 T 1T 2 T 2T 2 T 1 T 2 HWE (P) Leite Lancater Mckenna Mckenna Agoulnik Yes Agoulnik No Lancaster Manolitsas Val660Leu polymorphism Total GG GT TT G T Total GG GT TT G T HWE (P) Romano Terry Pearce Tong Spurdle Agoulnik Yes Agoulnik No +331G/A polymorphism Total GG GA AA G A Total GG GA AA G A HWE (P) Ludwig Delort Risch Romano Terry Berchuck Berchuck Jakubowska Number of T 1T 2 + T 2T 2 for Alu insertion polymorphism. Number of GT + TT for Val660Leu polymorphism. HWE, Hardy Weinberg equilibrium. classified as high quality revealed no association between Val660Leu with ovarian cancer in the genotype models above, either. However, this meta-analysis revealed that a significantly increased risk was found for the other heterozygous model comparison, TT versus GT (OR = 1.524, 95% CI = , P = 0.043) (Fig. 2), in overall population and in the studies classified as high quality, while no association was found in this comparison when limiting the analysis to the Caucasians (OR = 1.332, 95% CI = , P = 0.187). +331G/A The meta-analysis did not reveal an association between +331G/A A allele with ovarian cancer in the overall population (OR = 1.036, 95% CI = ). Limiting the analysis to the Caucasians or to the studies classified as high quality revealed no association between +331G/A A allele with ovarian cancer, either. The same results were concluded when we compared the homozygous model, heterozygous model, recessive model and dominant model in all populations or in the subgroup analyses. Sensitivity analysis Sensitivity analyses were performed to determine whether modification of the inclusion criteria of the meta-analysis affected the final results. A single study involved in the meta-analysis was deleted each time to reflect the influence of the individual dataset on the pooled OR, and most of the corresponding pooled OR were not materially altered (data not shown), indicating that our results were statistically robust. Publication bias Begg s funnel plots and Egger s tests were performed to assess publication bias. Figure 3 shows the funnel plot of the dominant model of Alu insertion polymorphism and Figure 4 shows the funnel plot of The Authors

6 PGR polymorphisms and ovarian cancer risk Table 4 Summary results of various comparisons Subgroup OR (95% CI) Heterogeneity (P) P-value Publication bias (P) Alu insertion polymorphism T 2 vs T 1 All ( ) HWE ( ) T 2T 2 vs T 1T 1 All ( ) HWE ( ) T 2T 2 vs T 1T 2 All ( ) HWE ( ) T 1T 2 vs T 1T 1 All ( ) HWE ( ) T 2T 2 vs T 1T 1 + T 1T 2 All ( ) HWE ( ) T 2T 2 + T 1T 2 vs T 1T 1 All ( ) HWE ( ) Val660Leu polymorphism T vs G All ( ) Caucasian ( ) Quality score, ( ) TT vs GG All ( ) Caucasian ( ) Quality score, ( ) TT vs GT All ( ) Caucasian ( ) Quality score, ( ) GT vs GG All ( ) Caucasian ( ) Quality score, ( ) TT vs GG + GT All ( ) Caucasian ( ) Quality score, ( ) TT + GT vs GG All ( ) Caucasian ( ) Quality score, ( ) G/A polymorphism A vs G All ( ) Caucasian ( ) Quality score, ( ) AA vs GG All ( ) Caucasian ( ) Quality score, ( ) AA vs GA All ( ) Caucasian ( ) Quality score, ( ) GA vs GG All ( ) Caucasian ( ) Quality score, ( ) AA vs GG + GA All ( ) Caucasian ( ) Quality score, ( ) AA + GA vs GG All ( ) Caucasian ( ) Quality score, ( ) CI, confidence interval; HWE, Hardy Weinberg equilibrium; OR, odds ratio The Authors 183

7 J. Liao et al. Figure 1 Odds ratio (OR) and 95% confidence interval (CI) of individual studies and pooled data for the association between Alu insertion and ovarian cancer in overall population: T 2T 2 + T 1T 2 versus T 1T 1. Figure 2 Odds ratio (OR) and 95% confidence interval (CI) of individual studies and pooled data for the association between Val660Leu and ovarian cancer in overall population: TT versus GT. heterozygous model comparison, TT versus GT of Val660Leu polymorphism. The shapes of the funnel plots revealed no obvious asymmetry. The Egger s test was then used to assess funnel plot symmetry statistically. The results also suggested no evidence of publication bias (P = for dominant model of Alu insertion polymorphism; P = for heterozygous model comparison, TT vs GT of Val660Leu The Authors

8 PGR polymorphisms and ovarian cancer risk Figure 3 Begg s funnel plot with pseudo 95% confidence interval of publication bias for the association between Alu insertion and ovarian cancer in overall population: T 2T 2 + T 1T 2 versus T 1T 1. Figure 4 Begg s funnel plot with pseudo 95% confidence interval of publication bias for the association between Val660Leu and ovarian cancer in overall population: TT versus GT. polymorphism; P = for allele comparison of +331G/A polymorphism). Discussion There is growing evidence that progesterone may play a protective role in the etiology of ovarian cancer. 28 The physiological effects of progesterone are mediated by the progesterone receptor, a steroid-receptor of the nuclear receptor superfamily. 34 Persons with ovarian cancer that express PGR may have a better prognosis. 35 Epidemiological data support the possibility of an inverse association of progestin levels with ovarian cancer. 36 Progesterone receptors contain several sites of polymorphisms. A TaqI restriction fragment length polymorphism was first reported by McKenna et al. 26 The polymorphism is the result of a small 309-base pair Alu direct repeat insertion inherited in a Mendelian fashion. 37 This polymorphism may induce a loss of hormone binding and transcriptional activity. 38 Val660Leu polymorphism causes a change of G-to-T in exon 4 and induces an amino acid change in the hinge region of the PGR, a poorly conserved sequence between the ligand and DNA-binding domains of steroid receptors. It plays a role in receptor dimerization, nuclear localization, ligand binding and interaction with co-repressors. 39,40 These two polymorphisms together with Hist770Hist polymorphism in exon 5 are termed PROGINS. The single nucleotide polymorphism, +331G/A, has been identified in the promoter region, which creates a unique transcription start site to increase PGR-B isoform synthesis. 24 Recently, a number of molecular epidemiological studies have been conducted to examine the association between the three polymorphisms and ovarian cancer risk. Inconsistent trends have been observed in different studies. This led us to conduct this metaanalysis of all eligible case control studies published to date to investigate the association between three polymorphisms of PGR and ovarian cancer risk. This meta-analysis of the T 2T 2 + T 1T 2 genotype of the Alu insertion polymorphism revealed a significant increased risk of ovarian cancer compared with the T 1T 1 genotype in overall population. The OR of the other genotype comparisons were increased in overall population, although the difference did not reach statistical significance. This finding may be due to low statistical power owing to the low frequency of the T 2T 2 genotype. Mix ethnicities of the study population were adopted in most studies in this meta-analysis for the Alu insertion polymorphism. Thus, we could not stratify by ethnicity for most comparisons. The metaanalysis for this polymorphism showed moderate heterogeneity among the studies. The heterogeneity could be slightly decreased when one of the studies (Agoulnik-1) was removed. It is probably because of different details of designs among the studies. In this analysis, the quality scores of most studies are less than 10 and the relatively low quality studies may have effect on the final result of this polymorphism. Therefore, this result should be interpreted with caution. For the Val660Leu polymorphism, our results indicated that a significantly increased risk was found for TT versus GT in the overall population. When limiting 2014 The Authors 185

9 J. Liao et al. the analysis to the Caucasians, the same trend was revealed without statistical significance. This comparison showed no heterogeneity among the studies and most eligible studies were high quality. Thus, this result is more robust. No association was found between the +331G/A polymorphism and ovarian cancer under all genotype comparisons in overall population or in the subgroup of Caucasians. Such evidences on the functionality of PGR polymorphisms may lead to a better understanding of ovarian cancer biology. Detection for the two polymorphisms, Alu insertion polymorphism and Val660Leu polymorphism, may be one method that could contribute to ovarian cancer screening. It also could be a strong rationale for development of antineoplastic drugs interfering with PGR protein production in ovarian cancer. There are some limitations to this meta-analysis for all the three polymorphisms. First of all, in the present meta-analysis, published studies written in English were searched. It is possible that eligible or unpublished studies that met the inclusion criteria were missed. Second, the number of the included studies for each polymorphism was limited and too small for stratified analyses. It may have insufficient statistical power to explore the association. Third, controls were not uniformly defined. Although controls were selected mainly from a healthy population, a substantial number did not mention the physiological condition of this group or even whether they had benign diseases. Therefore, non-differential misclassification bias was possible because these studies might have included the control groups who had different risks of developing ovarian cancer. Finally, ovarian cancer comprises five major histological subtypes (i.e. high-grade serous, clear cell, endometrioid, mucinous and lowgrade serous) according to recent findings, but we could not obtain the correlative data from the eligible studies or their authors. Thus, we could not perform the important histotype-specific analysis in our metaanalysis and it should be required for the study of association between the polymorphisms and ovarian cancer risk in the future researches. In conclusion, despite the limitations, results of this meta-analysis indicate that the two PGR polymorphisms, Alu insertion and Val660Leu, may contribute to ovarian cancer susceptibility serving as lowpenetrance risk factors. Alu insertion and Val660Leu are two polymorphisms of PROGINS. It also indicates that PROGINS may be a risk modifier of ovarian cancer. The +331G/A polymorphism of PGR was not associated with ovarian cancer risk. Large-sample and well-designed studies based on the three PGR polymorphisms should be required to further evaluate the ovarian cancer risk, which could help us to understand the association between this polymorphism and ovarian cancer better. Additionally, more functional studies are needed to determine the role of polymorphisms of PGR in ovarian cancer development. Disclosure The authors declare that there are no conflicts of interest. References 1. Jemal A, Bray F, Center MM et al. Global cancer statistics. CA Cancer J Clin 2011; 61: Fathalla MF. Incessant ovulation a factor in ovarian neoplasia? Lancet 1971; 2: Cramer DW, Welch WR. Determinants of ovarian cancer risk. II. Inferences regarding pathogenesis. J Natl Cancer Inst 1983; 71: Rao BR, Slotman BJ. Endocrine factors in common epithelial ovarian cancer. Endocr Rev 1991; 12: Wooster R, Weber BL. Breast and ovarian cancer. N Engl J Med 2003; 348: Mattei MG, Krust A, Stropp U et al. Assignment of the human progesterone receptor to the q22 band of chromosome 11. Hum Genet 1988; 78: Rousseau-Merck MF, Misrahi M, Loosfelt H et al. Localization of the human progesterone receptor gene to chromosome 11q22-q23. Hum Genet 1987; 77: Sartorius CA, Melville MY, Hovland AR et al. A third transactivation function (AF3) of human progesterone receptors located in the unique N-terminal segment of the B-isoform. Mol Endocrinol 1994; 8: Wen DX, Xu YF, Mais DE et al. The A and B isoforms of the human progesterone receptor operate through distinct signaling pathways within target cells. Mol Cell Biol 1994; 14: De Vivo I, Hankinson SE, Colditz GA et al. A functional polymorphism in the progesterone receptor gene is associated with an increase in breast cancer risk. Cancer Res 2003; 63: Jiang DK, Ren WH, Yao L et al. Meta-analysis of association between TP53 Arg72Pro polymorphism and bladder cancer risk. Urology 2010; 76: Jiang DK, Wang WZ, Ren WH et al. TP53 Arg72Pro polymorphism and skin cancer risk: A meta-analysis. J Invest Dermatol 2011; 131: DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials 1986; 7: Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst 1959; 22: The Authors

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