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. 2018 Jul 30;9(16):2963–2972. doi: 10.7150/jca.25638

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

Xiaoqi Liu 1,*, Jiawen Huang 2,*, Huiran Lin 3, Lingjuan Xiong 1, Yunzi Ma 1, Haiyan Lao 1,✉
PMCID: PMC6096366  PMID: 30123365

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.

Figure 1

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Flowchart of study selection process.

Table 1.

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

Surname Year Country Ethnicity Cancer type Control Source Genotype method Case Control HWE Score
TT CT CC All TT CT CC All
Modugno 2001 USA Caucasian Prostate PB PCR 26 34 21 81 85 109 43 237 0.438 8
Massart 2001 Italy Caucasian Leiomyoma HB PCR 35 57 27 119 46 77 33 156 0.941 5
Suzuki 2003 Japan Asian Prostate PB PCR 46 43 12 101 29 59 26 114 0.702 9
Massart 2003 Italy Caucasian Leiomyoma HB PCR-RFLP 54 91 43 188 66 111 48 225 0.917 5
Iwamoto 2003 Japan Asian Endometrial HB PCR-RFLP 25 54 13 92 25 28 12 65 0.408 4
Shin 2003 Korea Asian Breast PB PCR-RFLP 75 91 35 201 64 105 26 195 0.095 8
Tanaka 2003 Japan Asian Prostate HB PCR 23 63 29 115 39 113 48 200 0.061 6
Cai 2003 China Asian Breast PB PCR-RFLP 415 516 138 1069 430 546 190 1166 0.452 12
Fukatsu 2004 Japan Asian Prostate HB PCR-RFLP 37 57 22 116 81 110 47 238 0.384 6
wedren 2004 Sweden Caucasian Breast PB PCR-RFLP 390 634 268 1292 384 651 313 1348 0.248 10
Lu 2005 China Asian Breast HB PCR-RFLP 54 65 19 138 50 69 21 140 0.723 78
Modugno 2005 USA Caucasian Breast PB PCR-RFLP 53 115 80 248 819 1810 1272 3901 0.000 6
Onland-Moret 2005 Netherlands Caucasian Breast PB PCR-RFLP 89 150 69 308 88 153 96 337 0.093 9
Low 2006 UK Caucasian Prostate PB TaqMan 13 41 21 75 49 84 25 158 0.266 2
Al-Hendy 2006 USA African Leiomyoma HB PCR-RFLP 22 34 36 92 9 9 3 21 0.760 3
Al-Hendy 2006 USA Caucasian Leiomyoma HB PCR-RFLP 21 23 17 61 57 99 1 157 0.000 2
Al-Hendy 2006 USA Hispanic Leiomyoma HB PCR-RFLP 14 23 8 45 27 18 6 51 0.284 11
Zhai 2006 China Asian HCC PB PCR-RFLP 74 117 53 244 91 116 30 237 0.457 6
Chen 2006 China Asian Leiomyoma HB PCR-RFLP 35 37 11 83 31 38 9 78 0.604 5
Denschlag 2006 Germany Caucasian Leiomyoma PB PCR 33 66 31 130 40 59 40 139 0.075 9
Hernandez 2006 USA Caucasian Prostate PB TaqMan 47 55 18 120 129 131 43 303 0.300 11
Hernandez 2006 USA Caucasian Prostate PB TaqMan 115 216 100 431 154 296 132 582 0.653 9
Hernandez 2006 USA African Prostate PB TaqMan 9 22 16 47 50 113 50 213 0.373 11
Shen 2006 China Asian Breast PB PCR-RFLP 98 120 29 247 107 124 43 274 0.480 10
Cunningham 2007 Minnesota Caucasian Prostate PB PCR 257 454 213 924 120 249 120 489 0.684 9
Berndt 2007 USA Caucasian Prostate HB PCR 121 238 111 470 152 316 135 603 0.230 9
Hsieh 2007 China Asian Leiomyoma PB PCR-RFLP 25 75 6 106 60 44 6 110 0.571 7
Hu 2007 China Asian Breast HB PCR-RFLP 39 58 16 113 49 45 19 113 0.128 7
Kadiyska 2007 Bulgaria Caucasian Colorectal HB PCR-RFLP 34 79 27 140 23 35 19 77 0.438 11
Kjaergaard 2007 Danmark Caucasian Prostate PB TaqMan 35 55 26 116 1203 1972 830 4005 0.676 11
Kjaergaard 2007 Danmark Caucasian Breast PB TaqMan 398 613 245 1256 727 1225 537 2489 0.621 7
Wang 2007 USA Caucasian Breast PB PCR 117 188 87 392 214 393 176 783 0.862 4
Onsory 2008 India Asian Prostate HB PCR-RFLP 28 54 18 100 42 48 10 100 0.487
González-Mancha 2008 Spain Caucasian Breast PB PCR-RFLP 153 209 82 444 193 361 150 704 0.435 6
Sobti 2008 India Asian Prostate HB PCR 52 77 28 157 64 90 16 170 0.050 6
Gonzalez-Zuloeta 2008 Netherlands Caucasian Breast PB PCR-RFLP 72 94 24 190 1602 1648 453 3703 0.359 6
Dunning 2009 UK Caucasian Breast PB TaqMan 1260 2164 938 4362 1318 2296 934 4548 0.253 8
Ashton 2009 Australia Caucasian Endometrial PB PCR-RLFP 39 95 57 191 96 129 65 290 0.088 11
Iwasaki 2009 Japan Asian Breast HB TaqMan 144 180 64 388 115 196 77 388 0.692 10
Iwasaki 2009 Japan Asian Breast HB TaqMan 25 39 15 79 22 43 14 79 0.374 9
Iwasaki 2009 Japan Asian Breast HB TaqMan 107 187 85 379 122 194 63 379 0.338 10
Sonestedt 2009 Sweden Caucasian Breast PB MassARRAY 158 273 108 539 316 539 218 1073 0.667 10
Beuten 2009 USA non-Hispanic Caucasians Prostate PB PCR 167 304 138 609 222 421 200 843 0.988 7
Beuten 2009 USA Hispanic Caucasians Prostate PB PCR 75 92 28 195 186 246 82 514 0.964 7
Beuten 2009 USA African Prostate PB PCR 18 41 23 82 54 105 50 209 0.940 7
Anghel 2009 Romania Caucasian Bladder HB PCR 0 6 9 15 18 48 48 114 0.309 5
Anghel 2009 Romania Caucasian Colorectal HB PCR 2 13 3 18 18 48 48 114 0.309 5
Anghel 2009 Romania Caucasian AML HB PCR 0 5 10 15 18 48 48 114 0.309 5
Anghel 2009 Romania Caucasian HCC HB PCR 2 6 4 12 18 48 48 114 0.309 5
Anghel 2009 Romania Caucasian Breast HB PCR 4 65 32 101 15 38 37 90 0.333 6
Wang JY 2010 China Asian Leiomyoma HB PCR-RFLP 24 46 22 92 51 100 42 193 0.592 6
Wang XL 2010 China Asian Leiomyoma HB PCR-RFLP 42 48 12 102 35 49 16 100 0.867 6
Gupta 2010 India Asian Prostate HB PCR-RFLP 52 77 28 157 64 90 16 170 0.049 6
Park 2010 China Asian Gallbladder PB PCR-RFLP 41 100 94 235 108 356 314 778 0.658 11
Sonoda 2010 Japan Asian Prostate HB PCR 60 89 31 180 61 87 29 177 0.828 5
Sakoda 2011 China Asian Breast PB PCR 229 290 93 612 327 427 120 874 0.298 12
Deng 2011 China Asian Breast HB PCR-RFLP 42 63 23 128 52 61 17 130 0.892 7
Wang 2011 China Asian Cervical HB PCR-RFLP 39 45 18 102 32 52 18 102 0.692 6
Sissung 2011 USA Caucasian Prostate PB TaqMan 25 75 28 128 46 60 20 126 0.952 3
de Giorgi 2011 Italy Caucasian Melanoma HB PCR-RFLP 32 49 31 112 56 98 41 195 0.876 6
Balistreri 2011 Italy Caucasian Prostate HB PCR-RFLP 37 11 2 50 84 7 0 91 0.702 4
Han 2011 China Asian Breast PB TaqMan 353 399 107 859 324 402 151 877 0.171 9
Szendroi 2011 Hungary Caucasian Prostate HB PCR-RFLP 43 122 39 204 31 47 25 103 0.392 7
Lundie 2012 USA Caucasian Endometrial PB PCR 116 184 91 391 194 369 146 709 0.223 9
Srivastava 2012 India Asian Gallbladder PB PCR-RFLP 59 218 133 410 19 110 91 220 0.075 12
Safarinejad 2012 Iran Asian Prostate PB PCR-RFLP 11 94 57 162 65 169 90 324 0.373 6
Chang 2012 China Asian Lung HB PCR-RFLP 21 60 3 84 62 132 40 234 0.034 4
Tang 2013 China Asian Breast HB MALDI-TOF 293 374 127 794 334 375 136 845 0.076 9
Jurecekova 2013 Slovak Caucasian Prostate HB PCR 78 154 79 311 81 126 49 256 1 5
Pazarbasi 2013 Turkey Caucasian Prostate HB PCR 14 14 6 34 10 7 10 27 0.012 3
Ramalhinho 2013 Portugal Caucasian Breast HB PCR-RFLP 28 60 19 107 45 60 16 121 0.566 7
Liu 2014 China Asian HCC HB PCR 34 54 19 107 57 38 10 105 0.331 6
Chattopadhyay 2014 India Asian Breast PB PCR-RFLP 157 164 39 360 136 162 62 360 0.252 11
Lu 2014 China Asian Breast HB PCR-RFLP 227 258 57 542 425 454 137 1016 0.368 5
Madeira 2014 Brazil Asian Breast HB PCR-RFLP 6 49 9 64 25 39 8 72 0.211 6
Taghizade 2014 Iran Asian Leiomyoma HB PCR-RFLP 78 133 65 276 50 74 33 157 0.563 7
Cao 2014 China Asian Breast HB PCR-RFLP 70 109 42 221 79 124 49 252 0.978 7
Lu 2015 Japan Asian Prostate HB TaqMan 67 191 94 352 80 175 97 352 0.949 7
Nyante 2015 USA Caucasian Breast PB PCR 518 984 470 1972 469 908 398 1775 0.297 11
Han 2017 China Asian Prostate HB PCR 94 102 48 244 92 112 28 232 0.492 8
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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).

Table 2.

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

Variables No. of studies Homozygous Heterozygous Allele
TT vs. CC TT vs. CT T vs. C
OR (95% CI) P het OR (95% CI) P het OR (95% CI) P het
All 80 0.92 (0.84-1.01) <0.001 0.94 (0.88-1.001) <0.001 0.95 (0.91-0.99) <0.001
Cancer type
Breast 28 1.08 (0.98-1.19) 0.001 1.01 (0.94-1.08) 0.015 1.03 (0.99-1.08) 0.004
Prostate 26 0.79 (0.66-0.94) <0.001 0.89 (0.78-1.01) 0.006 0.89 (0.82-0.98) <0.001
Leiomyoma 11 0.72 (0.49-1.06) 0.016 0.83 (0.61-1.12) 0.003 0.82 (0.68-0.98) 0.006
HCC 3 0.45 (0.28-0.71) 0.353 0.63 (0.39-1.04) 0.191 0.67 (0.47-0.95) 0.145
Endometrial 3 0.73 (0.43-1.24) 0.067 0.73 (0.40-1.35) 0.005 0.84 (0.63-1.11) 0.046
Others 9 1.26 (0.85-1.90) 0.070 1.06 (0.88-1.40) 0.203 1.06 (0.88-1.28) 0.042
Ethnicity
Asian 38 0.94 (0.80-1.10) <0.001 0.93 (0.84-1.04) <0.001 0.96 (0.89-1.03) <0.001
Caucasian 36 0.93 (0.83-1.04) <0.001 0.95 (0.88-1.04) 0.003 0.96 (0.90-1.01) <0.001
African 3 0.54 (0.30-0.98) 0.292 0.83 (0.52-1.32) 0.870 0.70 (0.49-1.001) 0.185
Hispanics 1 0.39 (0.11-1.34) - 0.41 (0.17-0.99) - 0.55 (0.30-0.99) -
Non-Hispanic Caucasian 1 1.09 (0.81-1.47) - 1.04 (0.81-1.34) - 1.04 (0.90-1.21) -
Hispanic Caucasian 1 1.18 (0.71-1.96) - 1.08 (0.75-1.55) - 1.08 (0.85-1.38) -
Control source
HB 44 1.02 (0.91-1.13) <0.001 0.99 (0.92-1.08) 0.009 0.89 (0.83-0.96) <0.001
PB 36 0.81 (0.70-0.94) <0.001 0.86 (0.78-0.96) <0.001 0.99 (0.95-1.05) <0.001
HWE
>0.05 74 0.94 (0.86-1.02) <0.001 0.94 (0.88-1.00) <0.001 0.96 (0.92-1.001) <0.001
≤0.05 6 0.74 (0.33-1.67) <0.001 0.98 (0.80-1.21) 0.672 0.90 (0.70-1.14) 0.009
Quality score
>9 17 1.07 (0.92-1.23) 0.386 1.04 (0.98-1.11) 0.327 1.03 (0.96-1.10) <0.001
≤9 63 0.86 (0.77-0.96) 0.008 0.88 (0.81-0.96) <0.001 0.92 (0.87-0.97) <0.001
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Abbreviations: Het, heterogeneity; HB, hospital based; PB, population based.

Figure 2.

Figure 2

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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.

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).

Figure 3.

Figure 3

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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.

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).

Figure 4.

Figure 4

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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.

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.

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.

Acknowledgments

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

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