Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

A Meta-Analysis of the Association between ESR1 Genetic Variants and the Risk of Breast Cancer

  • Taishun Li ,

    Contributed equally to this work with: Taishun Li, Jun Zhao

    Affiliation Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, China

  • Jun Zhao ,

    Contributed equally to this work with: Taishun Li, Jun Zhao

    Affiliation National Research Institute for Family Planning, Beijing, China

  • Jiaying Yang,

    Affiliation Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, China

  • Xu Ma,

    Affiliation National Research Institute for Family Planning, Beijing, China

  • Qiaoyun Dai,

    Affiliation National Research Institute for Family Planning, Beijing, China

  • Hao Huang,

    Affiliation Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, China

  • Lina Wang,

    Affiliation Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, China

  • Pei Liu

    liupeiseu@126.com

    Affiliation National Research Institute for Family Planning, Beijing, China

A Meta-Analysis of the Association between ESR1 Genetic Variants and the Risk of Breast Cancer

  • Taishun Li, 
  • Jun Zhao, 
  • Jiaying Yang, 
  • Xu Ma, 
  • Qiaoyun Dai, 
  • Hao Huang, 
  • Lina Wang, 
  • Pei Liu
PLOS
x

Correction

17 Jan 2018: Li T, Zhao J, Yang J, Ma X, Dai Q, et al. (2018) Correction: A Meta-Analysis of the Association between ESR1 Genetic Variants and the Risk of Breast Cancer. PLOS ONE 13(1): e0191579. https://doi.org/10.1371/journal.pone.0191579 View correction

Abstract

Background

Single nucleotide polymorphisms (SNPs) in the estrogen receptor gene (ESR1) play critical roles in breast cancer (BC) susceptibility. Genome-wide association studies have reported that SNPs in ESR1 are associated with BC susceptibility; however, the results of recent studies have been inconsistent. Therefore, we performed this meta-analysis to obtain more accurate and credible results.

Methods

We pooled published literature from PubMed, EMBASE, and Web of Science and calculated odds ratios (ORs) with 95% confidence intervals (CIs) to assess the strength of associations using fixed effects models and random effects models. Twenty relevant case-control and cohort studies of the 3 related SNPs were identified.

Results

Three SNPs of the ESR1 gene, rs2077647:T>C, rs2228480:G>A and rs3798577:T>C, were not associated with increased BC risk in our overall meta-analysis. Stratified analysis by ethnicity showed that in Caucasians, the rs2228480 AA genotype was associated with a 26% decreased risk of BC compared with the GG genotype (OR = 0.740, 95% CI: 0.555–0.987). The C allele of the rs3798577:T>C variant was associated with decreased BC risk in Asians (OR = 0.828, 95% CI: 0.730–0.939), while Caucasians with this allele were found to experience significantly increased BC risk (OR = 1.551, 95% CI: 1.037–2.321). A non-significant association between rs2077647 and BC risk was identified in all of the evaluated ethnic populations.

Conclusion

Rs3798577 was associated with an increased risk of BC in Caucasian populations but a decreased risk in Asians. Rs2228480 had a large protective effect in Caucasians, while rs2077647 was not associated with BC risk.

Introduction

Breast cancer (BC) is the most common cancer and is a major cause of death in women worldwide [1]. Previous evidence has suggested that genetic variants and environmental factors may contribute to the development of BC [25]. Additionally, estrogen plays a well-known crucial role in the pathogenesis and progression of BC [6]. Estrogen stimulates breast epithelial cell growth, primarily by binding to the estrogen receptor (ER), which increases cancer risk [7]. The ER has two major forms, alpha and beta, both of which can be expressed in normal and neoplastic breast tissue. ER-alpha (ER-α), encoded by the ESR1 gene, is associated with BC risk because it acts as a transcriptional regulator by interacting with estrogen and other coactivator proteins.

The human ESR1 gene is a steroid hormone receptor gene located on chromosome 6 at 6q25.1. It contains eight exons spanning ~295 kb [8]. Many SNPs in ESR1 gene were shown to be associated with BC risk, including rs2234693, rs1801132, rs9340799, rs2077647, rs2228480 and rs3798577, and also, studies have showed that the genetic variants played important roles in the transcription and protein expression[9, 10]. Recently, several Meta-analysis showed that genetic variants at rs2234693, rs1801132 and rs9340799 loci were associated with the increased risk of BC[1114], while the effects of SNPs in rs2077647, rs2228480 and rs3798577 were also in controversy. Several studies evaluated these three SNPs and their association with BC [1534]. This review focuses on variants discovered through candidate gene studies and not genome-wide association studies (GWAS). For the three SNPs 20 eligible studies were included in our work, every single SNPs included 11 eligible studies. Two of these studies reported positive effects of rs2228480 on BC risk, while the other studies observed no association between the rs2228480 ESR1 genetic variant and BC risk. One study showed a protective effect of rs2077647 on BC risk, another study reported that ESR1 rs2077647 increased BC risk, and the remaining studies failed to replicate these associations. Three studies showed that the rs3978577 SNP, which is located in the 3’ UTR of ER-α, increased the overall risk of BC, one study provided evidence that it decreased BC risk, and the others also failed to replicate these associations.

Although rs3798577 and rs2228480 were discussed in a meta-analysis in 2010, the analysis included only 4 studies for each SNP [12]. However, the number of studies included in a meta-analysis directly influences the credibility and stability of the findings. The time of analysis is also a key factor for meta-analyses, and several new studies, which could change the results of the meta-analysis, have been conducted in the 5 years since 2010. Therefore, to more accurately assess the relationships between these three ESR1 polymorphisms and the risk of BC, a new meta-analysis that integrated more recent studies with earlier publications was conducted.

Materials and Methods

Publication search

Relevant English papers published before October 1, 2015, were identified through a search of the PubMed, Web of Science, EBSCO and EMBASE databases using the following terms: (“genetic polymorphism” or “single nucleotide polymorphism” or “SNP” or “gene mutation”) and (“breast cancer” or “breast neoplasm” or “carcinogenesis” or “breast carcinoma” or “breast tumor” or “BC” or “mammary cancer”) and (“ESR1” or “Estrogen receptor α” or “ER alpha” or “Estrogen receptor alpha” or “ERα”). Google Scholar was also used to search for relevant studies. Chinese papers were selected by searching the WanFang Data, Chongqing VIP (CQVIP), and China National Knowledge Infrastructure (CNKI) databases using the same search terms. The references of eligible articles were also inspected to find other potential studies. Only studies published in English or Chinese were included in this meta-analysis; any disagreement was resolved via discussion between two of the authors (H.H. and J.Z.). E-mail was used to contact study authors to obtain full text articles or missing data. This study was performed in accordance with the PRISMA statement checklist (S1 PRISMA Checklist) and the Meta-analysis of Genetic Association Studies checklist (S2 Checklist). The full details of the database searches used to identify the studies included in this meta-analysis have been provided in the supplementary materials (S1 Text).

Inclusion of relevant studies

The inclusion criteria were the following: (1) case-control or cohort study focused on associations between ESR1 gene polymorphisms and BC susceptibility; (2) availability of odds ratios (ORs) with 95% confidence intervals for polymorphisms and haplotypes or sufficient genotyping data to estimate these parameters; and (3) all diagnoses of BC confirmed by pathological or histological examination. Reviews, simple commentaries, case reports and meta-analyses were excluded. For overlapping studies, only the study with the largest sample was included.

Data extraction and quality assessment

The data from the published studies were extracted independently by two of the authors, and consensus was reached on all of the items. For each study, the following variables were collected: first author’s name or study organization name, year of publication, area, language, ethnicity, study methods, number of cases and controls, sources of cases and controls, allele and genotype frequencies, Hardy-Weinberg equilibrium (HWE), OR value, statistical power and minor allele frequency (MAF) in the controls. OR adjustment factors are not listed in our tables because every study used different factors for OR adjustment; therefore, it was difficult to find common factors for our meta-analysis.

The Newcastle-Ottawa Quality Assessment Scale (NOS) (S2 Text) was used independently by two authors (T.S.L. and J.Y.Y.) to evaluate the quality of the included studies (http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp). The NOS contains two different quality assessment scales for case-control studies and cohort studies. The two different forms each consist of three groupings, but the grouping items differ. The NOS identifies “high”-quality choices with a “star”, with a maximum of one “star” for each item within the “Selection” and “Exposure/Outcome” categories, and a maximum of two “stars” for “Comparability”. To obtain objective outcomes, any disagreement was discussed, and another author was consulted.

Statistical analysis

The association of the ESR1 polymorphisms with BC susceptibility was measured by ORs with 95% CIs in four genetic models, including a variant heterozygote versus wild-type homozygote model, a variant homozygote versus wild-type homozygote model, a dominant model, and a recessive model. Between-study heterogeneities were estimated using the χ2-based Q test [35], and the heterogeneity was considered significant at P<0.05. The I2 statistic was then used to quantitatively evaluate heterogeneity (I2<25%, low heterogeneity; 25%≤I2≤75%, moderate heterogeneity; I2>75%, high heterogeneity) [36]. When a significant Q test result (P<0.05) or I2>50% indicated heterogeneity among the studies, a random effects model (DerSimonian Laird method) was used to conduct the meta-analysis; otherwise, a fixed effects model (Mantel-Haenszel method) was used. To explore the sources of cross-study heterogeneity, subgroup analysis by ethnicity was performed. HWE of the genotype frequencies in the control group was assessed by the goodness-of-fit χ2 test. Sensitivity was evaluated by omitting each study one at a time to assess the influence of each study on the overall estimate [37]. Publication bias was assessed using funnel plots and Egger’s tests [38, 39]. The fail-safe number (Nfs) was also used to assess the stability of the results through comparison with the number of relevant included studies. All of the P values were two sided, with significance defined at 0.05. All analyses were performed using Review Manager software (version 5.0; Oxford, United Kingdom). The gene data for the heterogeneity analysis were download from the International HapMap Project (http://hapmap.ncbi.nlm.nih.gov/). Allele frequencies for the three polymorphisms in different populations were assessed by the goodness-of-fit χ2 test, and the linkage disequilibrium (LD) analysis was performed using Haploview software (version 4.0).

Results

Study selection and characteristics

The initial search of EMBASE, PubMed, and Web of Science yielded 1184 relevant articles, and an additional 24 records were identified through other sources. Following the deletion of duplicate results obtained from multiple databases, 368 records remained. After the titles and abstracts of the 368 articles were reviewed, 47 full-text articles were finally considered eligible. Ultimately, 20 eligible studies [1534] were included in our analysis. The excluded full-text articles are listed in the supplementary material (S1 Table). The study selection process is presented in detail in Fig 1.

thumbnail
Fig 1. Flowchart of the selection of studies included in the meta-analysis.

https://doi.org/10.1371/journal.pone.0153314.g001

The characteristics of the 20 eligible studies are presented in Table 1. Only two studies [30, 34] published in Chinese were included in this meta-analysis; some studies [22, 23, 25, 31, 33] did not provide information about genotypes. The factors for OR adjustment were primarily age, family history of BC, and age at first full-term pregnancy. Other basic information, including the first author’s name, year of publishing, study area, ethnicity of the study population, study methods, number of cases and controls, and source of cases and controls, are listed in Table 1. All of the studies indicated that the distribution of genotypes in the controls was consistent with HWE except for two studies of rs2077647 [28, 30]. Only five studies achieved statistical power greater than 80% [16, 17, 24, 29, 33]. The supplementary information includes the results of the NOS-based quality assessment of the 20 studies (S2 Table), a detailed summary of the genotype and allele frequencies (S3 Table), detailed information about the three SNPs in the four different models (S4, S5 and S6 Tables), and some additional characteristics of all of the eligible studies (S7 Table).

thumbnail
Table 1. Characteristics of all of the eligible studies of the ESR1 polymorphisms and breast cancer.

https://doi.org/10.1371/journal.pone.0153314.t001

Overall meta-analysis and stratified analyses

The evaluation of the associations of these three polymorphisms with BC risk and the stratified analyses by ethnicity are presented in Table 2.

thumbnail
Table 2. Pooled ORs of the three SNPS in the different genetic models and in different ethnic subgroups.

https://doi.org/10.1371/journal.pone.0153314.t002

For rs2228480, the eligible studies included 5758 BC patients and 8712 control subjects. The P value for heterogeneity was less than 0.05 in the dominant model and variant heterozygote versus wild-type homozygote model; therefore, the ORs were pooled in a random effects model. No significant association was found between the rs2228480 genetic variant and BC in any of the four models, and no significant effect was found in Asians. However, Caucasians carrying the rs2228480 TT genotype had a 26% decreased risk of BC compared with those with the CC genotype (OR = 0.74, 95% CI: 0.55–0.99, P = 0.040, Nfs = 3) (Table 2) (Fig 2).

thumbnail
Fig 2. Forest plot of the association between rs2228480 and breast cancer risk in different ethnicities in the variant homozygote versus wild-type homozygote model.

https://doi.org/10.1371/journal.pone.0153314.g002

The values in italics indicate P values less than <0.05, which were considered to be statistically significant. For rs2077647, the eligible studies included 6037 BC patients and 7385 control subjects. In the overall population, the Q test of heterogeneity was significant in the variant homozygote versus wild-type homozygote model, and the analysis was conducted using random effect models. There was no obvious association between the SNP and BC risk in any of the genetic models. The subgroup analysis revealed similar results in the Asian, Caucasian and mixed ethnic groups (Table 2) (Fig 3).

thumbnail
Fig 3. Forest plot of the association between rs2077647 and breast cancer risk in different ethnicities in the variant homozygote versus wild-type homozygote model.

https://doi.org/10.1371/journal.pone.0153314.g003

For rs3798577, the eligible studies included 8140 BC patients and 10386 control subjects. In the overall population, there was significant heterogeneity in all of the genetic models, so the analysis was conducted using random effect models. We failed to find a significant main effect on BC risk in any of the test models. In the ethnicity subgroup analysis, we found that among Asians, the variant C allele was associated with a decreased BC risk in all of the genetic models (CT vs. TT: OR = 0.83, 95% CI: 0.73–0.94, P = 0.019, Nfs = 11; CC vs. TT: OR = 0.72, 95% CI: 0.61–0.85, P = 0.000, Nfs = 23; (CT+CC) vs. TT: OR = 0.78, 95% CI: 0.69–0.88, P = 0.000, Nfs = 29; CC vs. (TT+CT): OR = 0.80, 95% CI: 0.70–0.93, P = 0.003, Nfs = 11). In the dominant, recessive and variant homozygote versus wild-type homozygote models, Caucasians carrying the variant C allele were found to experience significantly increased BC risk (CC vs. TT: OR = 1.55, 95% CI: 1.04–2.32, P = 0.033, Nfs = 26; (CT + CC) vs. TT: OR = 1.39, 95% CI: 1.01–1.91, P = 0.041, Nfs = 23; CC vs. (TT+CT): OR = 1.21, 95% CI: 1.00–1.47, P = 0.050, Nfs = 8). However, no significant associations were found in the mixed population. The data are presented in detail in Table 2 and Fig 4.

thumbnail
Fig 4. Forest plot of the association between rs3798577 and breast cancer risk in different ethnicities in the variant homozygote versus wild-type homozygote model.

https://doi.org/10.1371/journal.pone.0153314.g004

Publication bias

Funnel plots and Egger’s tests were used to assess the publication bias of the included studies. The funnel plots did not reveal any evidence of obvious asymmetry in the three SNPs in the variant homozygote versus wild-type homozygote model (Fig 5). Egger’s tests (all P values for Egger’s test>0.05) also showed that there was no evidence of publication bias for any of the three polymorphisms (t = -0.89, P = 0.398 for rs2228480; t = -1.40, P = 0.196 for rs2077647; and t = 0.22, P = 0.829 for rs3798577).

thumbnail
Fig 5. Funnel plot analysis to detect publication bias in the variant homozygote versus wild-type homozygote model.

a Funnel plot analysis of rs3798577; b Funnel plot analysis of rs2228480; c Funnel plot analysis of rs2077647.

https://doi.org/10.1371/journal.pone.0153314.g005

Sensitivity analysis

Sensitivity analyses were performed to evaluate the effect of each study on the pooled ORs through sequential removal of individual studies (Fig 6). No individual study significantly altered the pooled ORs for any of the three SNPs in the variant homozygote versus wild-type homozygote model, and similar results were also achieved for the other test models. Therefore, the data in this meta-analysis were relatively stable and credible. The Nfs of the positive result indicated that the results in this meta-analysis were also relatively stable and credible.

thumbnail
Fig 6. Sensitivity analysis of the meta-analysis of the association of the three ESR1 gene polymorphisms with breast cancer risk in the variant homozygote versus wild-type homozygote model.

a Sensitivity analysis of rs2228480. b Sensitivity analysis of rs3798577. c Sensitivity analysis of rs2077647. The vertical axis indicates the overall OR, and the two vertical axes indicate the 95% CI. Every hollow round indicates the pooled OR when the left study was omitted from the meta-analysis.

https://doi.org/10.1371/journal.pone.0153314.g006

Heterogeneity analysis

Heterogeneity analyses were performed to explore the reason for the heterogeneity in the associations found in the Caucasian and Asian populations. Measures of LD and allele frequencies for the three polymorphisms in the different populations comprised the two parts of this analysis.

Allele frequencies for the three polymorphisms in the different populations are listed in Table 3. The results (all P values for χ2 test >0.05) showed that there was no heterogeneity in the allele frequencies for the three polymorphisms in the different populations (χ2 = 6.971, P = 0.073 for rs2077647; χ2 = 0.643, P = 0.887 for rs2228480; and χ2 = 2.296, P = 0.513 for rs3798577).

thumbnail
Table 3. Allele frequencies in different populations for the three polymorphisms.

https://doi.org/10.1371/journal.pone.0153314.t003

The LD plots of all SNPs that were previously found to be associated with BC in different populations are presented in Fig 7. The results showed that there was heterogeneity in LD for the three polymorphisms in the different populations. In the Caucasian group, rs2228480 and rs3798577 were found to be in linkage disequilibrium. However, no linkage disequilibrium was found between rs2228480 and rs3798577 in the Asian population. The other SNPs showed the same pattern of linkage disequilibrium between Asian and Caucasian populations. The LD plots for other populations were presented as supporting information (S1 Fig).

thumbnail
Fig 7. The pattern of linkage disequilibrium in alleles of the ESR1 gene in the different populations, with their |D’|.

a CEU: CEPH (Utah residents with ancestry from northern and western Europe). b CHB+JPT: Han Chinese in Beijing, China and Japanese in Tokyo, Japan.

https://doi.org/10.1371/journal.pone.0153314.g007

Discussion

Genetic variants in the ESR1 gene have been shown to alter ER-α expression and to therefore modulate downstream signaling and BC susceptibility [41]. The ESR1 gene plays an important role in the progression of breast carcinogenesis by inducing cell proliferation, programming cell death and accumulating genetic mutations [42]. Many genetic variants in the ESR1 gene that are correlated with susceptibility have been identified.

Our findings showed that the SNPs rs2077647, rs2228480 and rs3798577 were not associated with BC risk in the four test models included in our overall meta-analysis. After the data were stratified by ethnicity, the analysis demonstrated that rs3798577 was associated with an increased risk of BC in Caucasians but had a protective effect in Asians. SNP rs2228480 also had a significant association with BC risk in Caucasians. The strength of the association of rs2228480 and rs3798577 with BC risk varied greatly across ethnic groups. An earlier study [13] indicated that the tremendous differences in genetic backgrounds between ethnicities and the different LD patterns among different ethnic populations might contribute to this phenomenon. Comparison of allele frequencies and LD patterns between the different ethnic populations were made to explore possible reasons for the observed interaction.

Comparison of allele frequencies showed that there were not heterogeneous among the different populations, but the LD plots for the rs3798577 in the different populations showed an opposite result. Hence, two potential reasons for the reversed interaction in rs3798577 between the different ethnic populations can be proposed. First, it may be caused by the differences in the function of genetic variants among different ethnic populations. Second, heterogeneity in LD for the rs3798577 in the different populations is also the possible reason.

GWAS have provided a powerful approach for identifying common disease alleles. Recent GWAS have identified several genetic susceptibility loci for BC, and low-penetrance variants in the ESR1 region associated with BC have been reported [4346]. For genetic variants in rs2228480 and rs2077647, we did not find the significant association with the increased risk of BC, which was consistent with the findings of GWAS [4749]. Our meta-analysis found that for rs3798577 the associations were diversity among different ethnic populations, but GWAS studies do not replicated it, the possible reason is that it not meet the standard of a significant result in GWAS studies. So a large population-based study needed be conducted to verify the ethnic diversity on the relationship between the genetic variant of rs3798577and BC risks.

For rs2077647:T>C, on the one hand, some studies [19, 23, 40] have shown that it has a protective effect against susceptibility to BC, but no functional implications of rs2077647 on the abundance of ESR1 mRNA or mRNA expression were detected. Furthermore, another study [40] indicated that rs2077647 did not affect exonic splicing. On the other hand, although ESR1 rs2077647:T>C is a silent coding polymorphism located in exon 1, it is unlikely to alter the protein encoded by ESR1. One research [50] indicated that one possible reasons for inter-population differences in estrogen- mediated diseases is the diversity of allele frequencies for the rs2077647 among the different ethnic populations, and the other possibility is the effects of some changes in the products of the ESR1 gene. However, the biological mechanisms underlying this phenomenon and the specific function of this SNP remain unclear.

The rs3798577:T>C polymorphism is located in the 3’ UTR of ESR1. Although the underlying biological mechanism and its functionality are not yet known, one plausible hypothesis is that rs3798577 polymorphisms might be major regulators of ER-α expression and might modify mRNA stability and ESR1 gene expression.

The rs2228480:G>A polymorphism is a silent polymorphism located in exon 8 of ESR1 and a synonymous variant. The functionality of this SNP is not yet known, but it seems to act as a regulator. Exon 8 is involved in the assembly of the C-terminal region of ER-α, which contributes to the regulation of reciprocal action between ER-α and other transcription factors [18]. Although rs2228480 does not alter amino acid sequences [16], rs2228480 has been suggested to modify the structure of mRNA, its splicing stability and the processes involved in its translation.

The present study had several strengths. Most importantly, it was the first meta-analysis conducted to evaluate the association between rs2077647 and BC risk. It was also the biggest and most recent meta-analysis of the association of rs2228480 and rs3798577 with BC risk, and it was more powerful than previous cohort and case-control studies. In addition, a subgroup analysis was conducted and demonstrated that the ESR1 rs3798577:T>C polymorphism was associated with BC risk in a manner that depended on patient ethnicity.

However, some limitations of this meta-analysis must be addressed. First, the sample size was relatively small for stratified analyses and might not have provided sufficient power to estimate the associations. Second, the overall OR was based on individual unadjusted ORs, and some important confounding factors, such as age, sex, menopausal status, and BMI, must be adjusted for. Finally, although the funnel plots and Egger’s tests showed that publication bias did not affect our results, only studies published in English or Chinese were included, which produced selection bias at the start of our study.

In conclusion, our meta-analysis indicated that the ESR1 rs3798577:T>C polymorphism might be a risk factor for BC in Asians and that the ESR1 rs3798577:T>C polymorphism and ESR1 rs2228480:A>G polymorphism had a large protective effect in Caucasians, while the ESR1 rs2077647:T>C polymorphism was not associated with BC risk. However, the functions of these SNP gene variants in the development of BC and the full mechanisms underlying their effects are still unclear. In the future, more comprehensive and well-designed studies should be conducted to re-evaluate the associations of these three SNPs and other ESR1 gene polymorphisms with BC risk.

Supporting Information

S2 Checklist. Meta-analysis of genetic association studies checklist.

https://doi.org/10.1371/journal.pone.0153314.s002

(DOCX)

S1 Fig. pdf LD plots for the different populations.

https://doi.org/10.1371/journal.pone.0153314.s003

(PDF)

S1 Table. List of excluded full-text articles.

https://doi.org/10.1371/journal.pone.0153314.s004

(XLSX)

S2 Table. NOS-based quality assessment of the 20 eligible studies.

https://doi.org/10.1371/journal.pone.0153314.s005

(DOCX)

S3 Table. Detailed genotype and allele frequency information.

https://doi.org/10.1371/journal.pone.0153314.s006

(XLSX)

S4 Table. Detailed information for SNP rs2077647 in the four different models.

https://doi.org/10.1371/journal.pone.0153314.s007

(XLSX)

S5 Table. Detailed information for SNP rs2228480 in the four different models.

https://doi.org/10.1371/journal.pone.0153314.s008

(XLSX)

S6 Table. Detailed information for SNP rs3798577 in the four different models.

https://doi.org/10.1371/journal.pone.0153314.s009

(XLSX)

S7 Table. Characteristics of the studies included in the meta-analysis of the three SNPs.

https://doi.org/10.1371/journal.pone.0153314.s010

(DOCX)

S2 Text. Newcastle—Ottawa Quality Assessment Scale.

https://doi.org/10.1371/journal.pone.0153314.s012

(DOCX)

Author Contributions

Conceived and designed the experiments: TSL. Performed the experiments: JZ QYD. Analyzed the data: TSL JYY. Contributed reagents/materials/analysis tools: HH LNW XM. Wrote the paper: TSL PL.

References

  1. 1. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65(2):87–108. Epub 2015/02/06. pmid:25651787.
  2. 2. Scholz C, Andergassen U, Hepp P, Schindlbeck C, Friedl TW, Harbeck N, et al. Obesity as an independent risk factor for decreased survival in node-positive high-risk breast cancer. Breast cancer research and treatment. 2015;151(3):569–76. pmid:25962694.
  3. 3. He B, Pan Y, Xu Y, Deng Q, Sun H, Gao T, et al. Associations of polymorphisms in microRNAs with female breast cancer risk in Chinese population. Tumour biology: the journal of the International Society for Oncodevelopmental Biology and Medicine. 2015;36(6):4575–4582. Epub 2015/01/24. pmid:25613069.
  4. 4. Schacht DV, Yamaguchi K, Lai J, Kulkarni K, Sennett CA, Abe H. Importance of a personal history of breast cancer as a risk factor for the development of subsequent breast cancer: results from screening breast MRI. AJR American journal of roentgenology. 2014;202(2):289–92. pmid:24450667.
  5. 5. Sprague BL, Gangnon RE, Hampton JM, Egan KM, Titus LJ, Kerlikowske K, et al. Variation in Breast Cancer-Risk Factor Associations by Method of Detection: Results From a Series of Case-Control Studies. American journal of epidemiology. 2015;181(12):956–69. pmid:25944893; PubMed Central PMCID: PMC4462335.
  6. 6. Zheng SL, Zheng W, Chang BL, Shu XO, Cai Q, Yu H, et al. Joint effect of estrogen receptor beta sequence variants and endogenous estrogen exposure on breast cancer risk in Chinese women. Cancer research. 2003;63(22):7624–9. Epub 2003/11/25. pmid:14633679.
  7. 7. Siddig A, Mohamed AO, Awad S, Hassan AH, Zilahi E, Al-Haj M, et al. Estrogen receptor alpha gene polymorphism and breast cancer. Annals of the New York Academy of Sciences. 2008;1138:95–107. Epub 2008/10/08. pmid:18837889.
  8. 8. Soysal SD, Kilic IB, Regenbrecht CRA, Schneider S, Muenst S, Kilic N, et al. Status of estrogen receptor 1 (ESR1) gene in mastopathy predicts subsequent development of breast cancer. Breast cancer research and treatment. 2015;151(3):709–15. WOS:000355658700022. pmid:25981900
  9. 9. Lipphardt MF, Deryal M, Ong MF, Schmidt W, Mahlknecht U. ESR1 single nucleotide polymorphisms predict breast cancer susceptibility in the central European Caucasian population. International journal of clinical and experimental medicine. 2013;6(4):282–8. Epub 2013/05/04. pmid:23641305; PubMed Central PMCID: PMCPmc3631553.
  10. 10. Yu JC, Hsiung CN, Hsu HM, Bao BY, Chen ST, Hsu GC, et al. Genetic variation in the genome-wide predicted estrogen response element-related sequences is associated with breast cancer development. Breast cancer research: BCR. 2011;13(1):R13. Epub 2011/02/02. pmid:21281495; PubMed Central PMCID: PMCPmc3109581.
  11. 11. Gonzalez-Zuloeta Ladd AM, Vasquez AA, Rivadeneira F, Siemes C, Hofman A, Stricker BH, et al. Estrogen receptor alpha polymorphisms and postmenopausal breast cancer risk. Breast cancer research and treatment. 2008;107(3):415–9. Epub 2007/04/25. pmid:17453340; PubMed Central PMCID: PMCPmc2217623.
  12. 12. Li N, Dong J, Hu Z, Shen H, Dai M. Potentially functional polymorphisms in ESR1 and breast cancer risk: a meta-analysis. Breast cancer research and treatment. 2010;121(1):177–84. Epub 2009/09/18. pmid:19760036.
  13. 13. Guo H, Ming J, Liu C, Li Z, Zhang N, Cheng H, et al. A common polymorphism near the ESR1 gene is associated with risk of breast cancer: evidence from a case-control study and a meta-analysis. PloS one. 2012;7(12):e52445. pmid:23272245; PubMed Central PMCID: PMC3525547.
  14. 14. Li LW, Xu L. Menopausal status modifies breast cancer risk associated with ESR1 PvuII and XbaI polymorphisms in Asian women: a HuGE review and meta-analysis. Asian Pacific journal of cancer prevention: APJCP. 2012;13(10):5105–11. pmid:23244119.
  15. 15. Jeon S, Choi JY, Lee KM, Park SK, Yoo KY, Noh DY, et al. Combined genetic effect of CDK7 and ESR1 polymorphisms on breast cancer. Breast cancer research and treatment. 2010;121(3):737–42. WOS:000277636000022. pmid:19941161
  16. 16. Anghel A, Narita D, Seclaman E, Popovici E, Anghel M, Tamas L. Estrogen receptor alpha polymorphisms and the risk of malignancies. Pathology oncology research: POR. 2010;16(4):485–96. Epub 2010/04/13. pmid:20383761.
  17. 17. Yu J-C, Hsu H-M, Chen S-T, Hsu G-C, Huang C-S, Hou M-F, et al. Breast cancer risk associated with genotypic polymorphism of the genes involved in the estrogen-receptor-signaling pathway: a multigenic study on cancer susceptibility. Journal of biomedical science. 2006;13(3):419–32. pmid:16502042
  18. 18. Wang YR, He YS, Qin ZZ, Jiang Y, Jin GF, Ma HX, et al. Evaluation of functional genetic variants at 6q25.1 and risk of breast cancer in a Chinese population. Breast Cancer Res. 2014;16(4):1–9. WOS:000344310300028.
  19. 19. Hsiao WC, Young KC, Lin SL, Lin PW. Estrogen receptor-alpha polymorphism in a Taiwanese clinical breast cancer population: a case-control study. Breast cancer research: BCR. 2004;6(3):R180–6. Epub 2004/04/16. pmid:15084241; PubMed Central PMCID: PMCPmc400668.
  20. 20. Bosviel R, Garcia S, Lavediaux G, Michard E, Dravers M, Kwiatkowski F, et al. BRCA1 promoter methylation in peripheral blood DNA was identified in sporadic breast cancer and controls. Cancer Epidemiology. 2012;36(3):e177–e82. pmid:22402307
  21. 21. Gallicchio L, Berndt SI, McSorley MA, Newschaffer CJ, Thuita LW, Argani P, et al. Polymorphisms in estrogen-metabolizing and estrogen receptor genes and the risk of developing breast cancer among a cohort of women with benign breast disease. BMC cancer. 2006;6:1–11. WOS:000239610100001.
  22. 22. Tapper W, Hammond V, Gerty S, Ennis S, Simmonds P, Collins A, et al. The influence of genetic variation in 30 selected genes on the clinical characteristics of early onset breast cancer. Breast Cancer Res. 2008;10(6):1–10.
  23. 23. Wang J, Lu K, Song Y, Xie L, Zhao S, Wang Y, et al. Indications of clinical and genetic predictors for aromatase inhibitors related musculoskeletal adverse events in Chinese Han women with breast cancer. PloS one. 2013;8(7):e68798. Epub 2013/07/31. pmid:23894347; PubMed Central PMCID: PMCPmc3716812.
  24. 24. Kallel I, Rebai M, Khabir A, Farid NR, Rebaï A. Genetic polymorphisms in the EGFR (R521K) and estrogen receptor (T594T) genes, EGFR and ErbB-2 protein expression, and breast cancer risk in Tunisia. BioMed research international. 2009;2009:753683.
  25. 25. Son BH, Kim MK, Yun YM, Kim HJ, Yu JH, Ko BS, et al. Genetic polymorphism of ESR1 rs2881766 increases breast cancer risk in Korean women. Journal of cancer research and clinical oncology. 2014;141(4):633–45. Epub 2014/10/18. pmid:25323936.
  26. 26. Fernandez LP, Milne RL, Barroso E, Cuadros M, Arias JI, Ruibal A, et al. Estrogen and progesterone receptor gene polymorphisms and sporadic breast cancer risk: a Spanish case-control study. International journal of cancer Journal international du cancer. 2006;119(2):467–71. Epub 2006/02/16. pmid:16477637.
  27. 27. Nyante S, Gammon M, Kaufman J, Bensen J, Lin D, Barnholtz-Sloan J, et al. Genetic variation in estrogen and progesterone pathway genes and breast cancer risk: an exploration of tumor subtype-specific effects. Cancer Causes & Control. 2015;26(1):121–31.
  28. 28. Diergaarde B, Potter JD, Jupe ER, Manjeshwar S, Shimasaki CD, Pugh TW, et al. Polymorphisms in genes involved in sex hormone metabolism, estrogen plus progestin hormone therapy use, and risk of postmenopausal breast cancer. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2008;17(7):1751–9. Epub 2008/07/17. pmid:18628428; PubMed Central PMCID: PMCPmc2732341.
  29. 29. Tse YE. Estrogen receptor gene polymorphisms and breast cancer risk in the Chinese population. The University of Hong Kong (Pokfulam, Hong Kong). 2006. https://doi.org/10.5353/th_b3870946
  30. 30. Xu Y, Zhang F, Lin Y. Relationship between ESR1 gene polymorphisms and breast cancer susceptibility. Journal of Jilin University(Medicine Edition). 2004;30(6):904–7.
  31. 31. O'Brien KM, Cole SR, Engel LS, Bensen JT, Poole C, Herring AH, et al. Breast cancer subtypes and previously established genetic risk factors: a bayesian approach. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2014;23(1):84–97. pmid:24177593; PubMed Central PMCID: PMC3947131.
  32. 32. Zhang L, Gu L, Qian B, Hao X, Zhang W, Wei Q, et al. Association of genetic polymorphisms of ER-alpha and the estradiol-synthesizing enzyme genes CYP17 and CYP19 with breast cancer risk in Chinese women. Breast cancer research and treatment. 2009;114(2):327–38. Epub 2008/07/17. pmid:18629629.
  33. 33. Boone S. TGF-B SIGNALING PATHWAY, ER-α AND THE HETEROGENEITY OF BREAST CANCER RISK AMONG HISPANIC AND NON-HISPANIC WHITE WOMEN. digitallibrarylouisvilleedu. 2013
  34. 34. Zhang LN. Association of genetic polymorphisms of ER-alpha and the estradiol-synthesizing enzyme genes CYP17 and CYP19 with breast cancer risk in Chinese women [master]: Tianjin University 2008.
  35. 35. Zhou Y, Dendukuri N. Statistics for quantifying heterogeneity in univariate and bivariate meta-analyses of binary data: the case of meta-analyses of diagnostic accuracy. Statistics in medicine. 2014;33(16):2701–17. Epub 2014/06/07. pmid:24903142.
  36. 36. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. Bmj. 2003;327(7414):557–60. Epub 2003/09/06. pmid:12958120; PubMed Central PMCID: PMCPmc192859.
  37. 37. Thakkinstian A, McElduff P, D'Este C, Duffy D, Attia J. A method for meta-analysis of molecular association studies. Statistics in medicine. 2005;24(9):1291–306. Epub 2004/11/30. pmid:15568190.
  38. 38. Song F, Khan KS, Dinnes J, Sutton AJ. Asymmetric funnel plots and publication bias in meta-analyses of diagnostic accuracy. International journal of epidemiology. 2002;31(1):88–95. Epub 2002/03/27. pmid:11914301.
  39. 39. Gjerdevik M, Heuch I. Improving the error rates of the Begg and Mazumdar test for publication bias in fixed effects meta-analysis. BMC medical research methodology. 2014;14(109):1–16. Epub 2014/09/24. pmid:25245217; PubMed Central PMCID: PMCPmc4193136.
  40. 40. Fernandez LP, Milne RL, Barroso E, Cuadros M, Arias JI, Ruibal A, et al. Estrogen and progesterone receptor gene polymorphisms and sporadic breast cancer risk: a Spanish case-control study. Int J Cancer. 2006;119(2):467–71. WOS:000238267300029. pmid:16477637
  41. 41. Mahdi KM, Nassiri MR, Nasiri K. Hereditary genes and SNPs associated with breast cancer. Asian Pacific journal of cancer prevention: APJCP. 2013;14(6):3403–9. pmid:23886119.
  42. 42. Gold B, Kalush F, Bergeron J, Scott K, Mitra N, Wilson K, et al. Estrogen receptor genotypes and haplotypes associated with breast cancer risk. Cancer research. 2004;64(24):8891–900. WOS:000225809200017. pmid:15604249
  43. 43. Barrdahl M, Canzian F, Joshi AD, Travis RC, Chang-Claude J, Auer PL, et al. Post-GWAS gene-environment interplay in breast cancer: results from the Breast and Prostate Cancer Cohort Consortium and a meta-analysis on 79,000 women. Human molecular genetics. 2014;23(19):5260–70. pmid:24895409; PubMed Central PMCID: PMC4159150.
  44. 44. Barzan D, Veldwijk MR, Herskind C, Li Y, Zhang B, Sperk E, et al. Comparison of genetic variation of breast cancer susceptibility genes in Chinese and German populations. European journal of human genetics: EJHG. 2013;21(11):1286–92. Epub 2013/03/15. pmid:23486537; PubMed Central PMCID: PMCPmc3798843.
  45. 45. Kim HC, Lee JY, Sung H, Choi JY, Park SK, Lee KM, et al. A genome-wide association study identifies a breast cancer risk variant in ERBB4 at 2q34: results from the Seoul Breast Cancer Study. Breast cancer research: BCR. 2012;14(2):R56. Epub 2012/03/29. pmid:22452962; PubMed Central PMCID: PMCPmc3446390.
  46. 46. Hein R, Maranian M, Hopper JL, Kapuscinski MK, Southey MC, Park DJ, et al. Comparison of 6q25 breast cancer hits from Asian and European Genome Wide Association Studies in the Breast Cancer Association Consortium (BCAC). PloS one. 2012;7(8):e42380. Epub 2012/08/11. pmid:22879957; PubMed Central PMCID: PMCPmc3413660.
  47. 47. Rinella ES, Shao Y, Yackowski L, Pramanik S, Oratz R, Schnabel F, et al. Genetic variants associated with breast cancer risk for Ashkenazi Jewish women with strong family histories but no identifiable BRCA1/2 mutation. Human genetics. 2013;132(5):523–36. Epub 2013/01/29. pmid:23354978; PubMed Central PMCID: PMCPmc4072456.
  48. 48. Long J, Cai Q, Sung H, Shi J, Zhang B, Choi JY, et al. Genome-wide association study in east Asians identifies novel susceptibility loci for breast cancer. PLoS genetics. 2012;8(2):e1002532. Epub 2012/03/03. pmid:22383897; PubMed Central PMCID: PMCPmc3285588.
  49. 49. Turnbull C, Ahmed S, Morrison J, Pernet D, Renwick A, Maranian M, et al. Genome-wide association study identifies five new breast cancer susceptibility loci. Nature genetics. 2010;42(6):504–7. Epub 2010/05/11. pmid:20453838; PubMed Central PMCID: PMCPmc3632836.
  50. 50. Sasaki M, Tanaka Y, Sakuragi N, Dahiya R. Six polymorphisms on estrogen receptor 1 gene in Japanese, American and German populations. European journal of clinical pharmacology. 2003;59(5–6):389–93. Epub 2003/08/19. pmid:12923601.