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

bs_bs_banner doi:10.1111/jog.12519 J. Obstet. Gynaecol. Res. Vol. 41, No. 2: 178 187, 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], 1.206 2.203) and Val660Leu (TT vs GT; OR, 1.524; 95% CI, 1.013 2.293). 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 225 500 newly diagnosis cases and 140 200 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 9 2014. Accepted: June 5 2014. 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 430030, China. Emails: gumpc@126.com and dma@tjh.tjmu.edu.cn Funding: This study was supported by grants from the National Science Foundation of China (nos. 81272859, 81230038, 81000979 and 81272422). 178 2014 The Authors

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 2012. 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

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 1000 3 500 but <1000 2 200 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. 17 27, 29 33 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 1995 2010. There were eleven studies of Caucasians, one of Asians, and nine studies of mixed races. Quality scores for individual studies ranged 6 15. Deviation from HWE of genotype frequencies among the controls was detected in three studies. 21 23 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 = 0.923 1.842, P = 0.132), homozygote model (OR = 1.741, 95% CI = 0.567 5.344, P = 0.332), heterozygous model (OR = 1.568, 95% CI = 0.471 5.222, P = 0.464 for T 2T 2 vs T 1T 2; OR = 1.056, 95% CI = 0.845 1.320, P = 0.630 for T 1T 2 vs T 1T 1) and recessive model (OR = 1.695, 95% CI = 0.547 5.246, P = 0.360). However, under the dominant model, a significantly increased risk was found (OR = 1.504, 95% CI = 1.206 2.203, 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 = 0.814 1.443, P = 0.357), homozygote model (OR = 1574, 95% CI = 0.735 3.371, P = 0.243), heterozygous model (OR = 1.000, 95% CI = 0.715 1.399, P = 0.999 for GT vs GG), recessive model (OR = 1.366, 95% CI = 0.918 2.034, P = 0.124) and dominant model (OR = 1.312, 95% CI = 0.872 1.978, P = 0.194). Limiting the analysis to the Caucasians or to the studies 180 2014 The Authors

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 309/397 11 Mckenna-1 1995 Ireland Unknown Unknown Unknown Blood 41/83 6 Mckenna-2 1995 Germany Unknown Unknown Unknown Blood 26/101 6 Agoulnik-1 2004 USA Caucasian Hospital Blood donors Tissue 84/440 9 Agoulnik-2 2004 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 67/443 12 Terry 2005 USA Caucasian Cancer registry Population Blood 896/939 15 Pearce 2005 USA Caucasian Cancer registry Population Blood 267/396 14 Tong 2001 Australia Unknown Hospital Volunteers Blood 226/194 11 Spurdle 2001 Australia Caucasian Hospital Population Blood or tissue 551/298 13 Agoulnik-1 2004 USA Caucasian Hospital Blood donors Tissue 84/440 9 Agoulnik-2 2004 USA Asian Hospital Hospital Blood 114/512 8 +331G/A polymorphism Ludwig 2009 Poland Caucasian Hospital Blood donors Blood 215/352 12 Delort 2008 France Unknown Unknown Unknown Blood 51/1000 9 Risch 2006 USA Mix Cancer registry Population Buccal cells 487/533 15 Romano 2006 The Netherlands Caucasian Hospital Volunteers Tissue 52/379 11 Terry 2005 USA Caucasian Cancer registry Population Blood 920/960 15 Berchuck-1 2004 USA and Australia Caucasian Cancer registry Population Blood 438/504 14 Berchuck-2 2004 USA and Australia Unknown Hospital Volunteers Blood 535/298 13 Jakubowska 2010 Poland Caucasian Cancer registry Cancer registry Blood 144/279 10 2014 The Authors 181

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 2008 80 57 12 11 126 34 282 221 61 0 503 61 0.042 Lancater 2003 309 219 80 10 518 100 397 285 95 17 665 129 0.016 Mckenna-1 1995 41 26 15 0 67 15 83 58 21 4 137 29 0.264 Mckenna-2 1995 26 17 8 1 42 10 101 88 12 1 188 14 0.427 Agoulnik-1 2004 84 45 39 440 342 98 Yes Agoulnik-2 2004 114 112 2 512 510 2 No Lancaster 1998 96 76 15 5 167 25 101 79 18 4 176 26 0.039 Manolitsas 1997 231 173 52 6 398 64 220 162 54 4 378 62 0.838 Val660Leu polymorphism Total GG GT TT G T Total GG GT TT G T HWE (P) Romano 2006 67 42 24 1 108 26 443 347 87 9 781 105 0.206 Terry 2005 896 648 223 25 1519 273 939 612 298 29 1522 356 0.314 Pearce 2005 267 173 82 12 428 106 396 279 111 6 669 123 0.174 Tong 2001 226 167 50 9 384 68 194 141 52 1 334 54 0.098 Spurdle 2001 551 395 144 12 934 168 298 203 90 5 496 100 0.16 Agoulnik-1 2004 84 45 39 440 342 98 Yes Agoulnik-2 2004 114 112 2 512 510 2 No +331G/A polymorphism Total GG GA AA G A Total GG GA AA G A HWE (P) Ludwig 2009 215 183 32 0 398 32 352 312 39 1 663 41 0.851 Delort 2008 51 48 3 0 99 3 1000 916 80 4 1912 88 0.121 Risch 2006 487 426 59 2 911 63 533 489 44 0 1022 44 0.32 Romano 2006 52 49 9 0 107 9 379 339 37 3 715 43 0.087 Terry 2005 920 831 87 2 1 749 91 960 868 91 1 1827 93 0.381 Berchuck-1 2004 438 400 37 1 837 39 504 445 58 1 948 60 0.532 Berchuck-2 2004 535 483 48 4 1 014 56 298 266 30 2 562 34 0.267 Jakubowska 2010 144 130 13 1 273 15 279 239 40 0 518 40 0.197 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 = 1.013 2.293, 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 = 0.870 2.041, 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 = 0.883 1.216). 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 182 2014 The Authors

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 1.304 (0.923 1.842) 0.011 0.132 0.267 HWE 1.302 (0.768 2.208) 0.012 0.340 0.406 T 2T 2 vs T 1T 1 All 1.741 (0.567 5.344) 0.020 0.332 0.238 HWE 1.136 (0.413 3.128) 0.325 0.805 0.916 T 2T 2 vs T 1T 2 All 1.568 (0.471 5.222) 0.014 0.464 0.388 HWE 0.942 (0.340 2.610) 0.357 0.908 0.533 T 1T 2 vs T 1T 1 All 1.056 (0.845 1.320) 0.173 0.630 0.375 HWE 1.521 (0.721 3.207) 0.047 0.270 0.128 T 2T 2 vs T 1T 1 + T 1T 2 All 1.695 (0.547 5.246) 0.017 0.360 0.268 HWE 1.082 (0.398 2.943) 0.334 0.877 0.832 T 2T 2 + T 1T 2 vs T 1T 1 All 1.504 (1.026 2.203) 0.002 0.036 0.229 HWE 1.842 (0.911 3.728) 0.001 0.089 0.606 Val660Leu polymorphism T vs G All 1.083 (0.814 1.443) 0.001 0.584 0.061 Caucasian 1.088 (0.771 1.536) 0.000 0.631 0.096 Quality score, 10 1.083 (0.814 1.443) 0.001 0.584 0.061 TT vs GG All 1.574 (0.735 3.371) 0.064 0.243 0.247 Caucasian 1.142 (0.752 1.733) 0.128 0.534 0.560 Quality score, 10 1.574 (0.735 3.371) 0.064 0.243 0.247 TT vs GT All 1.524 (1.013 2.293) 0.161 0.043 0.548 Caucasian 1.332 (0.870 2.041) 0.336 0.187 0.917 Quality score, 10 1.524 (1.013 2.293) 0.161 0.043 0.548 GT vs GG All 1.000 (0.715 1.399) 0.001 0.999 0.104 Caucasian 1.058 (0.702 1.594) 0.000 0.787 0.044 Quality score, 10 1.000 (0.715 1.399) 0.001 0.999 0.104 TT vs GG + GT All 1.366 (0.918 2.034) 0.094 0.124 0.304 Caucasian 1.198 (0.791 1.814) 0.193 0.393 0.669 Quality score, 10 1.366 (0.918 2.034) 0.094 0.124 0.304 TT + GT vs GG All 1.312 (0.871 1.978) 0.000 0.194 0.053 Caucasian 1.333 (0.817 2.173) 0.000 0.250 0.020 Quality score, 10 1.045 (0.749 1.458) 0.001 0.794 0.070 +331G/A polymorphism A vs G All 1.036 (0.883 1.216) 0.131 0.664 0.704 Caucasian 0.967 (0.795 1.176) 0.269 0.737 0.893 Quality score, 10 1.047 (0.890 1.231) 0.102 0.578 0.991 AA vs GG All 1.674 (0.696 4.025) 0.955 0.250 0.406 Caucasian 1.529 (0.465 5.027) 0.881 0.484 0.840 Quality score, 10 1.647 (0.661 4.107) 0.914 0.284 0.478 AA vs GA All 1.604 (0.664 3.876) 0.909 0.293 0.657 Caucasian 1.449 (0.453 4.631) 0.688 0.532 0.841 Quality score, 10 1.559 (0.626 3.886) 0.853 0.340 0.756 GA vs GG All 1.021 (0.789 1.307) 0.076 0.869 0.831 Caucasian 0.973 (0.709 1.334) 0.091 0.863 0.847 Quality score, 10 1.035 (0.798 1.344) 0.052 0.793 0.954 AA vs GG + GA All 1.666 (0.694 4.000) 0.951 0.253 0.431 Caucasian 1.521 (0.466 4.969) 0.863 0.488 0.842 Quality score, 10 1.637 (0.658 4.072) 0.909 0.289 0.506 AA + GA vs GG All 1.028 (0.810 1.304) 0.093 0.823 0.777 Caucasian 0.959 (0.783 1.175) 0.151 0.690 0.860 Quality score, 10 1.043 (0.813 1.339) 0.067 0.739 0.973 CI, confidence interval; HWE, Hardy Weinberg equilibrium; OR, odds ratio. 2014 The Authors 183

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 = 0.229 for dominant model of Alu insertion polymorphism; P = 0.548 for heterozygous model comparison, TT vs GT of Val660Leu 184 2014 The Authors

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 = 0.704 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

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: 69 90. 2. Fathalla MF. Incessant ovulation a factor in ovarian neoplasia? Lancet 1971; 2: 163. 3. Cramer DW, Welch WR. Determinants of ovarian cancer risk. II. Inferences regarding pathogenesis. J Natl Cancer Inst 1983; 71: 717 721. 4. Rao BR, Slotman BJ. Endocrine factors in common epithelial ovarian cancer. Endocr Rev 1991; 12: 14 26. 5. Wooster R, Weber BL. Breast and ovarian cancer. N Engl J Med 2003; 348: 2339 2347. 6. 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