J Cancer 2018; 9(16):2963-2972. doi:10.7150/jca.25638

Review

ESR1 PvuII (rs2234693 T>C) polymorphism and cancer susceptibility: Evidence from 80 studies

Xiaoqi Liu1,*, Jiawen Huang2,*, Huiran Lin3, Lingjuan Xiong1, Yunzi Ma1, Haiyan Lao1, Corresponding address

1. Department of Pharmacy, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, Guangdong, China
2. Department of Pharmacy, The First Affiliated Hospital, Jinan University, Guangzhou 510630, Guangdong, China
3. Laboratory Animal Management Office, Public Service Platform for Science and Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, China
*These authors contributed equally to this work.

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How to cite this article:
Liu X, Huang J, Lin H, Xiong L, Ma Y, Lao H. ESR1 PvuII (rs2234693 T>C) polymorphism and cancer susceptibility: Evidence from 80 studies. J Cancer 2018; 9(16):2963-2972. doi:10.7150/jca.25638. Available from http://www.jcancer.org/v09p2963.htm

Abstract

Emerging epidemiological researches have been performed to assess the association of ESR1 PvuII (rs2234693 T>C) polymorphism with the risk of cancer, yet with conflicting conclusions. Therefore, this updated meta-analysis was performed to make a more accurate evaluation of such relationship. We adopted EMBASE, PubMed, CNKI, and WANFANG database to search relevant literature before January 2018. Odds ratios (ORs) and 95% confidence intervals (CIs) were employed to estimate the relationship strengths. In final, 80 studies (69 publications) involving 26428 cases and 43381 controls were enrolled. Our results failed to provide significant association between overall cancer risk and PvuII polymorphism under homozygous (TT vs. CC) and heterozygous (TT vs. CT) models. Statistically significant relationship was only observed for PvuII polymorphism in allele model T vs. C (OR=0.95, 95% CI=0.91-0.99). Stratification analysis by cancer type suggested that T genotype significantly decreased prostate cancer risk (TT vs. CC: OR=0.79, 95% CI=0.66-0.94; T vs. C: OR=0.89, 95% CI=0.82-0.98), Leiomyoma risk (T vs. C: OR=0.82, 95% CI=0.68-0.98), and HCC risk (TT vs. CC: OR=0.45, 95% CI=0.28-0.71; T vs. C: OR=0.67, 95% CI=0.47-0.95). Furthermore, significantly decreased risk was also found for Africans, population-based and hospital-based studies in the stratified analyses. These results suggest that ESR1 PvuII (rs2234693 T>C) polymorphism may only have little impact on cancer susceptibility. In the future, large-scale epidemical studies are warranted to verify these results.

Keywords: meta-analysis, ESR1, PvuII, polymorphism, cancer risk

Introduction

Worldwide, cancer still ranks the number one killer that threatens people's life. Approximately 14.1 million new cancer cases and 8.2 million cancer-caused deaths occurred globally in 2013 [1]. In 2018, 1,735,350 new cancer cases and 609,640 cancer deaths are projected to occur in the United States [2]. By now, the definitive etiology of cancer remains unknown. However, a myriad of evidence has suggested that cancer is a complex disease caused by both genetic and environmental factors [3, 4]. Numerous functional polymorphisms have been found to be implicated in the development of cancers [5-7].

Previous researches have reported that hormonal factors play crucial roles in the development of some cancers. Common genetic variants in hormonal-related genes were associated with cancer susceptibility [8]. Among them, estrogen receptor (ER) was the most related-hormone in cancer risk. Estrogen receptor (ER) has two forms, which is alpha and beta [9]. Estrogen receptor-α plays a critical role in mediating hormonal response in estrogen-sensitive tissues. It consists of several domains important for hormone regulation, activation of transcription and DNA binding. Evidence points to estrogen receptor-α as the main receptor correlated to initiation of cancer [10]. Estrogen receptor-α, a transcription factor, is encoded by the ESR1 gene.

The ESR1 gene, comprises of 8 exons and 7 introns, is located on chromosome 6q25.1. Several SNPs of ESR1 gene have been identified to influence the risk of cancer, but the most popular studied SNP is ESR1 PvuII (rs2234693 T>C) polymorphism [11]. Although increasing studies have been performed, the conclusions of the roles of ESR1 PvuII (T>C) polymorphism in cancer risk are conflicting. The inconsistent conclusions between ESR1 PvuII (rs2234693 T>C) polymorphism and cancer risk may be due to the limitations in the sample size of the corresponding studies or the inadequate statistical power in genetic studies with complex characteristics. Several meta-analyses regarding this issue have been performed to resolve the conflicting situation but somehow failed. With the aim to solve such embarrassment, we conducted this comprehensive meta-analysis by adopting all published articles.

Materials and methods

Publication search

We first inputted the following key words: “single nucleotide polymorphism or polymorphism or variant or SNP” and “ESR1 or ESRα or Estrogen Receptor α or Estrogen Receptor 1”, and “cancer or tumor or neoplasm or carcinoma)” in database of PubMed and EMBASE. In addition, we also searched the Chinese database CNKI and WANFANG to include more eligible studies. Further, additional studies were also manually extracted from the references of the above obtained publications. The date of the final literature search was set on January 2018. We did not set any language publication restrictions here. The article will be considered as different studies if it contains more than two ethnicities. If the searched articles have overlapping data, the largest one will be selected.

Eligibility criteria

The evaluating publications in this meta-analysis should fulfill all the following requirements: 1) unrelated case-control studies; 2) original epidemiological studies; 3) analyzing the relationship between ESR1 PvuII (rs2234693 T>C) polymorphism and cancer risk; 4) enough data to obtain odds ratios (ORs) and 95% confidence intervals (CIs); 5) articles written in English or in Chinese.

Data extraction

Two authors separately extracted data by screening all eligible studies. They collected the information regarding first author's surname, country, publication year, ethnicity, genotyping methods, the source of controls, and numbers of cases and controls with CC, CT and TT genotypes. All the disagreed information was settle down after fully discussed by the two authors.

Statistical methods

Hardy-Weinberg equilibrium (HWE) in the controls was determined using goodness-of-fit χ2 test. P<0.05 was considered as departure from HWE. Three genetic models, homozygous model (TT vs. CC), heterozygous model (TT vs. CT), and allele comparison (T vs. C), were applied to assess the association between ESR1 PvuII (rs2234693 T>C) polymorphism and cancer risk. The strength of such association was assessed by calculating ORs with the corresponding 95% CIs. Stratification analyses were also conducted by ethnicity, cancer type, source of control, and HWE in controls, in all studies. Chi square-based Q-test was adopted to monitor between-study heterogeneity. The fixed-effects model (the Mantel-Haenszel method) was chosen to estimate the pooled OR, if the studies were homogeneous (P>0.10 for the Q test). Otherwise, the random-effects model (the DerSimonian and Laird method) was used. Sensitivity analysis was conducted by excluding each study individually and re-calculating the ORs and 95% CIs. Begg's funnel plot and Egger's linear regression were used to evaluate whether there exists publication bias [12, 13]. The asymmetric plot and P value less than 0.5 was considered as the existence of publication bias. We also conducted quality assessment to detect the quality of each study using the quality assessment criteria [14]. The version 11.0 STATA software was adopted to perform all statistical analysis (Stata Corporation, College Station, TX). All the statistics were two-sided with P value of <0.05 as significant findings.

Results

Study characteristics

Our first research in databases identified 185 candidate publications. After screening the title and abstract, we kept 64 publication s in the analysis [15-78]. Moreover, we further extracted 5 articles from the references of the retrieval articles [79-83]. The flow chart of screening process was graphically shown in Figure 1. In final, 80 studies (69 publications) with 26428 cases and 43381 controls were included in the pooled analysis (Table 1). Among them, 38 studies focused on Asians, 36 on Caucasians, 3 on Africans, 1 on Hispanics and 1 on non-Hispanic Caucasians, 1 on Hispanic Caucasians. 44 studies were hospital-based design, 36 were population-based design. The controls' genotype frequencies were in agreement with HWE (P>0.05) in 74 studies, except for 6 studies.

 Figure 1 

Flowchart of study selection process.

J Cancer Image (Click on the image to enlarge.)
 Table 1 

The baseline characteristics of all qualified studies in this meta-analysis

SurnameYearCountryEthnicityCancer typeControl SourceGenotype methodCaseControlHWEScore
TTCTCCAllTTCTCCAll
Modugno2001USACaucasianProstatePBPCR2634218185109432370.4388
Massart2001ItalyCaucasianLeiomyomaHBPCR3557271194677331560.9415
Suzuki2003JapanAsianProstatePBPCR4643121012959261140.7029
Massart2003ItalyCaucasianLeiomyomaHBPCR-RFLP54914318866111482250.9175
Iwamoto2003JapanAsianEndometrialHBPCR-RFLP25541392252812650.4084
Shin2003KoreaAsianBreastPBPCR-RFLP75913520164105261950.0958
Tanaka2003JapanAsianProstateHBPCR23632911539113482000.0616
Cai2003ChinaAsianBreastPBPCR-RFLP415516138106943054619011660.45212
Fukatsu2004JapanAsianProstateHBPCR-RFLP37572211681110472380.3846
wedren2004SwedenCaucasianBreastPBPCR-RFLP390634268129238465131313480.24810
Lu2005ChinaAsianBreastHBPCR-RFLP5465191385069211400.72378
Modugno2005USACaucasianBreastPBPCR-RFLP53115802488191810127239010.0006
Onland-Moret2005NetherlandsCaucasianBreastPBPCR-RFLP891506930888153963370.0939
Low2006UKCaucasianProstatePBTaqMan134121754984251580.2662
Al-Hendy2006USAAfricanLeiomyomaHBPCR-RFLP22343692993210.7603
Al-Hendy2006USACaucasianLeiomyomaHBPCR-RFLP21231761579911570.0002
Al-Hendy2006USAHispanicLeiomyomaHBPCR-RFLP142384527186510.28411
Zhai2006ChinaAsianHCCPBPCR-RFLP741175324491116302370.4576
Chen2006ChinaAsianLeiomyomaHBPCR-RFLP3537118331389780.6045
Denschlag2006GermanyCaucasianLeiomyomaPBPCR3366311304059401390.0759
Hernandez2006USACaucasianProstatePBTaqMan475518120129131433030.30011
Hernandez2006USACaucasianProstatePBTaqMan1152161004311542961325820.6539
Hernandez2006USAAfricanProstatePBTaqMan922164750113502130.37311
Shen2006ChinaAsianBreastPBPCR-RFLP9812029247107124432740.48010
Cunningham2007MinnesotaCaucasianProstatePBPCR2574542139241202491204890.6849
Berndt2007USACaucasianProstateHBPCR1212381114701523161356030.2309
Hsieh2007ChinaAsianLeiomyomaPBPCR-RFLP25756106604461100.5717
Hu2007ChinaAsianBreastHBPCR-RFLP3958161134945191130.1287
Kadiyska2007BulgariaCaucasianColorectalHBPCR-RFLP347927140233519770.43811
Kjaergaard2007DanmarkCaucasianProstatePBTaqMan3555261161203197283040050.67611
Kjaergaard2007DanmarkCaucasianBreastPBTaqMan3986132451256727122553724890.6217
Wang2007USACaucasianBreastPBPCR117188873922143931767830.8624
Onsory2008IndiaAsianProstateHBPCR-RFLP2854181004248101000.487
González-Mancha2008SpainCaucasianBreastPBPCR-RFLP153209824441933611507040.4356
Sobti2008IndiaAsianProstateHBPCR5277281576490161700.0506
Gonzalez-Zuloeta2008NetherlandsCaucasianBreastPBPCR-RFLP7294241901602164845337030.3596
Dunning2009UKCaucasianBreastPBTaqMan1260216493843621318229693445480.2538
Ashton2009AustraliaCaucasianEndometrialPBPCR-RLFP39955719196129652900.08811
Iwasaki2009JapanAsianBreastHBTaqMan14418064388115196773880.69210
Iwasaki2009JapanAsianBreastHBTaqMan25391579224314790.3749
Iwasaki2009JapanAsianBreastHBTaqMan10718785379122194633790.33810
Sonestedt2009SwedenCaucasianBreastPBMassARRAY15827310853931653921810730.66710
Beuten2009USAnon-Hispanic CaucasiansProstatePBPCR1673041386092224212008430.9887
Beuten2009USAHispanic CaucasiansProstatePBPCR759228195186246825140.9647
Beuten2009USAAfricanProstatePBPCR1841238254105502090.9407
Anghel2009RomaniaCaucasianBladderHBPCR069151848481140.3095
Anghel2009RomaniaCaucasianColorectalHBPCR2133181848481140.3095
Anghel2009RomaniaCaucasianAMLHBPCR0510151848481140.3095
Anghel2009RomaniaCaucasianHCCHBPCR264121848481140.3095
Anghel2009RomaniaCaucasianBreastHBPCR46532101153837900.3336
Wang JY2010ChinaAsianLeiomyomaHBPCR-RFLP2446229251100421930.5926
Wang XL2010ChinaAsianLeiomyomaHBPCR-RFLP4248121023549161000.8676
Gupta2010IndiaAsianProstateHBPCR-RFLP5277281576490161700.0496
Park2010ChinaAsianGallbladderPBPCR-RFLP41100942351083563147780.65811
Sonoda2010JapanAsianProstateHBPCR6089311806187291770.8285
Sakoda2011ChinaAsianBreastPBPCR229290936123274271208740.29812
Deng2011ChinaAsianBreastHBPCR-RFLP4263231285261171300.8927
Wang2011ChinaAsianCervicalHBPCR-RFLP3945181023252181020.6926
Sissung2011USACaucasianProstatePBTaqMan2575281284660201260.9523
de Giorgi2011ItalyCaucasianMelanomaHBPCR-RFLP3249311125698411950.8766
Balistreri2011ItalyCaucasianProstateHBPCR-RFLP37112508470910.7024
Han2011ChinaAsianBreastPBTaqMan3533991078593244021518770.1719
Szendroi2011HungaryCaucasianProstateHBPCR-RFLP43122392043147251030.3927
Lundie2012USACaucasianEndometrialPBPCR116184913911943691467090.2239
Srivastava2012IndiaAsianGallbladderPBPCR-RFLP5921813341019110912200.07512
Safarinejad2012IranAsianProstatePBPCR-RFLP11945716265169903240.3736
Chang2012ChinaAsianLungHBPCR-RFLP216038462132402340.0344
Tang2013ChinaAsianBreastHBMALDI-TOF2933741277943343751368450.0769
Jurecekova2013SlovakCaucasianProstateHBPCR7815479311811264925615
Pazarbasi2013TurkeyCaucasianProstateHBPCR141463410710270.0123
Ramalhinho2013PortugalCaucasianBreastHBPCR-RFLP2860191074560161210.5667
Liu2014ChinaAsianHCCHBPCR3454191075738101050.3316
Chattopadhyay2014IndiaAsianBreastPBPCR-RFLP15716439360136162623600.25211
Lu2014ChinaAsianBreastHBPCR-RFLP2272585754242545413710160.3685
Madeira2014BrazilAsianBreastHBPCR-RFLP64996425398720.2116
Taghizade2014IranAsianLeiomyomaHBPCR-RFLP78133652765074331570.5637
Cao2014ChinaAsianBreastHBPCR-RFLP701094222179124492520.9787
Lu2015JapanAsianProstateHBTaqMan671919435280175973520.9497
Nyante2015USACaucasianBreastPBPCR518984470197246990839817750.29711
Han2017ChinaAsianProstateHBPCR941024824492112282320.4928

Abbreviations: HB, hospital based; PB, population based; PCR, polymerase chain reaction; PCR-RFLP, PCR-restriction fragment length polymorphism; HCC, hepatocarcinoma; AML, acute myeloid leukemia; HWE, Hardy-Weinberg equilibrium.

Meta-analysis results

The summary results of meta-analysis were presented in Table 2 and Figure 2. In all, no significant association between the ESR1 PvuII (rs2234693 T>C) polymorphism and cancer risk was observed under homozygous model (TT vs. CC: OR=0.92, 95% CI=0.84-1.01) and heterozygous model (TT vs. CT: OR=0.94, 95% CI=0.88-1.001). Statistically significant relationship was only observed for PvuII in allele model T vs. C (OR=0.95, 95% CI=0.91-0.99).

In subgroup analysis by cancer type, we found that the T genotype significantly decreased prostate cancer risk (TT vs. CC: OR=0.79, 95% CI=0.66-0.94; T vs. C: OR=0.89, 95% CI=0.82-0.98), Leiomyoma risk (T vs. C: OR=0.82, 95% CI=0.68-0.98), and HCC risk (TT vs. CC: OR=0.45, 95% CI=0.28-0.71; T vs. C: OR=0.67, 95% CI=0.47-0.95). However, no relationship between ESR1 PvuII polymorphism and any other types of cancer was observed. Ethnicity subgroup analysis revealed that significant association between ESR1 PvuII genotype and cancer risk was detected among African (TT vs. CC: OR=0.54, 95% CI=0.30-0.98), and Hispanics (TT vs. CT: OR=0.41, 95% CI=0.17-0.99; T vs. C: OR=0.55, 95% CI=0.30-0.99). Such association was not observed for the Asians and Caucasians. In terms of source of controls, we found that the ESR1 PvuII T genotype help to decrease cancer risk in hospital-based group (T vs. C: OR=0.89, 95% CI=0.83-0.96) and in population-based group (TT vs. CC: OR=0.81, 95% CI=0.70-0.94; TT vs. CT: OR=0.86, 95% CI=0.78-0.96). Further subgroup analysis by HWE in controls also failed to detect positive association, except for heterogenous model in HWE>0.05 subgroup (TT vs. CT: OR=0.94, 95% CI=0.88-1.00). Subgroup analysis of quality revealed that ESR1 PvuII T genotype help to decrease cancer risk in group with quality score ≤9.

Heterogeneity and sensitivity analysis

Between-study heterogeneity was first calculated by using Q test and I2 statistics. We used the random-effect model as significant heterogeneity was observed among all three genetic models (P<0.001) in the pooled analysis (TT vs. CC: P<0.001, I2 = 59.1%; TT vs. CT: P<0.001, I2 = 49.4%; T vs. C: P<0.001, I2 = 61.0%). In addition, sequential leave-one-out sensitivity analysis was adopted to evaluate the stability of the results. After removing each study, no substantial changes in pooled results were found (Figure 3).

Publication bias

The shape of Begg's funnel plots was quite symmetry (Figure 4). Moreover, statistical evidence of Egger's test also provided the none-existence of publication bias among the studies (data not shown).

Discussion

In this meta-analysis, we comprehensively evaluated the association between ESR1 PvuII (rs2234693 T>C) polymorphism with cancer susceptibility. The obtained results suggested ESR1 PvuII (rs2234693 T>C) polymorphism may influence overall cancer risk in a low impact effect manner. So far, this meta-analysis represents the most powerful investigation in elucidating the role of ESR1 PvuII (rs2234693 T>C) in cancer risk.

The polymorphism of ESR1, PvuII (rs2234693 T>C), can affect ESR1 transcription activity and further contribute to the carcinogenesis. A myriad of studies has investigated the role of ESR1 PvuII (rs2234693 T>C) polymorphisms in cancer risk. In 2001, Massart et al. claimed that the PvuII and XbaI polymorphisms in the ESR1 gene do not produce different risks of developing uterine leiomyomas [52]. In another study performed in urban Shanghai with 1069 breast cancer patients and 1166 controls, Cai et al. found that ESR1 PvuII (rs2234693 T>C) polymorphism conferred to an enhanced risk of breast cancer among subjects carrying Pp (CT) and pp (TT) genotypes [21]. Yet, AI-Hendy et al. claimed that the ESR1PvuII PP (CC) genotype contributed to a significantly increased risk of uterine leiomyomas in black and white women, but not in Hispanic women [15]. Many meta-analyses have been conducted aiming to obtain a clear association between ESR1 PvuII (rs2234693 T>C) and cancer risk. In 2010, Li et al. performed a meta-analysis regarding the association of several potentially functional SNPs in ESR1 with breast cancer risk. This analysis on 10,300 breast cancer cases and 16,620 controls in PvuII (rs2234693 T>C) polymorphism revealed a borderline significant decreased breast cancer risk for CC and CC/CT carriers (CC vs. TT: OR=0.92, 95% CI=0.86-0.99; CC/CT vs. TT: OR=0.95, 95% CI=0.89-1.00) [84]. In a meta-analysis updated to April 2014, 41 studies were included to analyze the relationship between ESR1 PvuII (rs2234693 T>C) and cancer risk. Results of the pooled analysis suggested a null relationship between PvuII (rs2234693 T>C) polymorphism and overall cancer risk. Subgroup analysis indicated that PvuII (rs2234693 T>C) polymorphism was associated with a decreased risk of gallbladder cancer, in contrast with the increased risk of prostate cancer and hepatocellular carcinoma (HCC). They also failed to observe significant association in Asian and Caucasian populations [85].

 Table 2 

Meta-analysis of the association between ESR1 PvuII polymorphism and cancer risk

VariablesNo. of studiesHomozygousHeterozygousAllele
TT vs. CCTT vs. CTT vs. C
OR (95% CI)P hetOR (95% CI)P hetOR (95% CI)P het
All800.92 (0.84-1.01)<0.0010.94 (0.88-1.001)<0.0010.95 (0.91-0.99)<0.001
Cancer type
Breast281.08 (0.98-1.19)0.0011.01 (0.94-1.08)0.0151.03 (0.99-1.08)0.004
Prostate260.79 (0.66-0.94)<0.0010.89 (0.78-1.01)0.0060.89 (0.82-0.98)<0.001
Leiomyoma110.72 (0.49-1.06)0.0160.83 (0.61-1.12)0.0030.82 (0.68-0.98)0.006
HCC30.45 (0.28-0.71)0.3530.63 (0.39-1.04)0.1910.67 (0.47-0.95)0.145
Endometrial30.73 (0.43-1.24)0.0670.73 (0.40-1.35)0.0050.84 (0.63-1.11)0.046
Others91.26 (0.85-1.90)0.0701.06 (0.88-1.40)0.2031.06 (0.88-1.28)0.042
Ethnicity
Asian380.94 (0.80-1.10)<0.0010.93 (0.84-1.04)<0.0010.96 (0.89-1.03)<0.001
Caucasian360.93 (0.83-1.04)<0.0010.95 (0.88-1.04)0.0030.96 (0.90-1.01)<0.001
African30.54 (0.30-0.98)0.2920.83 (0.52-1.32)0.8700.70 (0.49-1.001)0.185
Hispanics10.39 (0.11-1.34)-0.41 (0.17-0.99)-0.55 (0.30-0.99)-
Non-Hispanic Caucasian11.09 (0.81-1.47)-1.04 (0.81-1.34)-1.04 (0.90-1.21)-
Hispanic Caucasian11.18 (0.71-1.96)-1.08 (0.75-1.55)-1.08 (0.85-1.38)-
Control source
HB441.02 (0.91-1.13)<0.0010.99 (0.92-1.08)0.0090.89 (0.83-0.96)<0.001
PB360.81 (0.70-0.94)<0.0010.86 (0.78-0.96)<0.0010.99 (0.95-1.05)<0.001
HWE
>0.05740.94 (0.86-1.02)<0.0010.94 (0.88-1.00)<0.0010.96 (0.92-1.001)<0.001
≤0.0560.74 (0.33-1.67)<0.0010.98 (0.80-1.21)0.6720.90 (0.70-1.14)0.009
Quality score
>9171.07 (0.92-1.23)0.3861.04 (0.98-1.11)0.3271.03 (0.96-1.10)<0.001
≤9630.86 (0.77-0.96)0.0080.88 (0.81-0.96)<0.0010.92 (0.87-0.97)<0.001

Abbreviations: Het, heterogeneity; HB, hospital based; PB, population based.

 Figure 2 

Forest plot for the overall cancer susceptibility associated with the ESR1 PvuII (T>C) polymorphism under allele comparison model. Notes: The horizontal lines represent the study-specific ORs and 95% CIs, respectively. The diamond represents the pooled results of OR and 95% CI.

J Cancer Image (Click on the image to enlarge.)
 Figure 3 

Sensitivity analysis of the association between ESR1 PvuII (T>C) polymorphism and cancer susceptibility. Each point represents the recalculated OR after deleting a separate study.

J Cancer Image (Click on the image to enlarge.)
 Figure 4 

Funnel plot analysis to detect publication bias for ESR1 PvuII (T>C) polymorphism under allele comparison model. Notes: Each point represents a separate study for the indicated association.

J Cancer Image (Click on the image to enlarge.)

From then on, several new case-control studies with larger samples were available. In addition, the former meta-analysis conducted only included studies published in English. To provide a robust clarification, we performed the updated meta-analysis by involving all the eligible studies published. Overall, statistically significant relationship was only observed for PvuII in allele model T vs. C (OR=0.95, 95% CI=0.91-0.99). However, we did not detect any significant relationship between ESR1 PvuII (rs2234693 T>C) polymorphism and cancer risk in the pooled analysis under homozygous and heterozygous model. Cancer type by subgroup analysis indicated that T genotype significantly decreased prostate cancer risk, Leiomyoma risk, and HCC risk. Yet no association was observed in other types of cancers. These data suggested that the PvuII (rs2234693 T>C) polymorphism on ESR1 may function in a wide manner regarding the different cancer types. When stratified by population, no significant association between ESR1 PvuII genotype and cancer risk among African, and Hispanics was detected. Such association was observed for the Africans. The limited statistical power caused by relatively small number of studies in Africans should be considered. In this meta-analysis, several measurements were performed to enhance the credibility of our conclusion. First, we adopted every effort to expand the numbers of included studies, such as incorporating all publications written both in Chinese and in English. The relatively large number of including studies was one of the important merits of the current study. We also performed publication bias and the sensitivity analysis under the guidance of Cochrane protocol. The sensitivity analysis and publication bias analysis revealed the strength of our conclusions. Although this meta-analysis has its own merits, limitations still exist. First, we only used unadjusted estimates to determine whether there is a relationship between ESR1 PvuII (rs2234693 T>C) polymorphism and cancer risk. Adjustment analysis was absence due to the lack of patient's clinical data such as life habit, smoking and drinking status, exposing factors, and gene-environment interactions, which restrains our further analysis for confounding factors. Second, the validity of conclusion was impaired as significant between-study heterogeneity was detected in some comparisons. Such heterogeneity might result from different quality of studies, and might impair the strength of the conclusion. Third, selection bias and language bias were inevitable, as only published studies and papers written in English or Chinese were analyzed, respectively. Moreover, selection bias might also generate as most of the studies included in this meta-analysis were from candidate gene based, but not from GWAS. Fourth, the sample size of subgroup analysis was relatively small in some strata, impaired the statistical power to estimate the real association. Last, the analyzed case-control studies were mostly performed using Caucasians and Asians populations. Therefore, more trials using different population background, especially Africans, are essential to further confirm such conclusion, due to the genetic and geographical differences.

Conclusion

In conclusion, the current meta-analysis suggests that ESR1 PvuII (rs2234693 T>C) polymorphism may not be strong enough to impact the risk of cancer, based on the pooled results of the published articles. Such relationship further helps to explain the etiology of cancer. Yet, further epidemiological studies with larger sample sizes, standardized unbiased design are warranted to confirm this conclusion.

Acknowledgements

This study was supported by grants from Science and Technology Program of Guangzhou (No. 201509010012).

Competing Interests

The authors have declared that no competing interest exists.

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Author contact

Corresponding address Corresponding author: Haiyan Lao, Department of Pharmacy, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China, Tel./Fax: +86-020-83827812-36066, Email: laohaiyan2010com


Received 2018-2-21
Accepted 2018-6-9
Published 2018-7-30