Association between rs11200014, rs2981579, and rs1219648 polymorphism and breast cancer susceptibility: A meta-analysis.

BACKGROUND: Research on the polymorphism of breast cancer (BC) helps to search the BC susceptibility gene for mass screening, early diagnosis, and gene therapy, which has become a hotspot in BC research field. Previous studies have suggested associations between rs11200014, rs2981579, and rs1219648 polymorphisms and cancer risk. The aim of this study was to evaluate the relationship between rs11200014, rs2981579, and rs1219648 polymorphism and BC risk.
METHODS: PubMed, Web of science, and the Cochrane Library databases were searched before October 11, 2015, to identify relevant studies. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to estimate the strength of associations. Sensitivity and subgroup analyses were conducted. All included cases should have been diagnosed by a pathological examination.
RESULTS: Twenty-six studies published from 2007 to 2015 were included in this meta-analysis. The pooled results showed that there was a significant association between all the 3 variants and BC risk in any genetic model. When stratified by Source of controls, the results showed the same association between rs2981579 polymorphism and BC susceptibility in hospital-based (HB) group, although there was not any genetic model attained statistical correlation in population-based (PB) group. Subgroup analysis was performed on rs1219648 by ethnicity and Source of controls, and the effects remained in Asians, Caucasians, HB, and PB groups.
CONCLUSION: This meta-analysis of case-control studies provides strong evidence that fibroblast growth factor 2 (FGFR2; rs11200014, rs2981579, and rs1219648) polymorphisms are significantly associated with the BC risk. For rs2981579, the association remained in hospital populations, while not in general populations. For rs1219648, the association remained in Asians, Caucasians, hospital populations, and general populations. However, further large-scale multicenter epidemiological studies are warranted to confirm this finding and the molecular mechanism for the associations need to be elucidated in future studies.

Introduction

Breast cancer (BC) has become one of the most common malignant tumors in women, whose incidence accounts for about 23% of all female malignant tumors, and more than 400,000 people worldwide die from BC each year.[ The rising morbidity and mortality should not be ignored.[ Exploring the BC susceptible factors, etiology, and pathogenesis, establishing the model of BC risk, so as to guide clinical prevention and treatment better, is still a very challenging subject.

Currently, study on the interaction between BC gene and environment has gradually attracted the attention of researchers. The main methods of this study include candidate gene and genome-wide association study (GWAS).[ GWAS has made some achievements in the association between the polymorphism of fibroblast growth factor 2 (FGFR2), TNRC9, MAP3K1, H19, and LSP1 and the significant increase of BC risk.[ Research on the polymorphism helps to search the BC susceptibility gene for mass screening, early diagnosis, and gene therapy, which has become a hotspot in BC research field.

Recently, researches have paid more attention to the human FGFR2, whose several SNPs, rs11200014 (G > A), rs2981579 (C > T), rs1219648 (A > G), may associated with BC susceptibility in different crowds and different regions.[ However, conclusions of related reports are still inconclusive between susceptible[ and protective.[ These different conclusions may due to differences in ethnic and regional and other factors. Therefore, a systematic analysis with large samples should be applied to assess the association. To clarify the role of FGFR2 (rs11200014, rs2981579, and rs1219648) polymorphism in BC susceptibility, 5 meta-analyses[ on the correlation between FGFR2 (rs11200014, rs2981579, and rs1219648) polymorphism and BC susceptibility had been implemented. However, the results remain inconclusive and number of their studies included for each SNP is small, and some just no subgroup. Therefore, we carried out this meta-analysis on all the included case–control studies to make a more accurate assessment of the relationship.

Methods

Literature searching strategy

We searched PubMed, Web of science, and the Cochrane Library for relevant studies published before October 11, 2015. The following keywords were used: (FGFR2) and (variant∗ or genotype or polymorphism or SNP) and (breast) and (cancer or carcinom∗ or neoplasm∗ or tumor), and the combined phrases for all genetic studies on the association between the FGFR2 (rs11200014, rs2981579, and rs1219648) polymorphism and BC risk. The reference lists of all articles were also manually screened for potential studies. Abstracts and citations were screened independently by 2 researchers independently. All the eligible articles need a second screening for full-text. The searching was done without language limitations.

Selection and exclusion criteria

Inclusion criteria included that a study was included in this meta-analysis if it meets the following criteria: independent case–control studies for humans; the study evaluating the association between FGFR2 (rs11200014, rs2981579, and rs1219648) polymorphism and BC risk; the study presenting available genotype frequencies in cancer cases and control subjects for risk estimate; and cases should have been diagnosed by a pathological examination. We excluded comments, editorials, systematic reviews, and studies lacking sufficient data or studies with male cases. If the researches were duplicated or shared in more than one study, the most recent publications were included.

Data extraction and synthesis

We used endnote bibliographic software to construct an electronic library of citations identified in the literature search. All the PubMed, Web of science, and the Cochrane Library searches were performed using Endnote. Duplicates were found automatically by endnote and deleted manually. All data extraction was checked and calculated twice according to the inclusion criteria listed above by 2 independent investigators. Data extracted from the included studies were as follows: First author, year of publication, country, ethnicity, source of controls, genotyping method, number of cases and controls, and evidence of HWE in controls. A third reviewer would participate if some disagreements were emerged, and a final decision was made by the majority of the votes.

Statistical analysis

All statistical analyses were performed using STATA version 11.0 software (StataCorp LP, College Station, TX) and Review Manage version 5.2.0 (The Cochrane Collaboration, 2012). Hardy–Weinberg equilibrium (HWE) was assessed by χ test in the control group of each study.[ The strength of associations between the FGFR2 (rs11200014, rs2981579, and rs1219648) polymorphism and BC risk was measured by odds ratios (ORs) with 95% confidence interval (95% CIs). Z test was used to assess the significance of the ORs, and I and Q statistics was used to determine the statistical heterogeneity among studies. A random-effect model was used if the P value of heterogeneity tests was no more than .1 (P ≤ 0.1), and otherwise, a fixed-effect model was selected.[ Sensitivity analyses were performed to assess the stability of the results. We used Begg funnel plot and Egger test to evaluate the publication bias.[ The strength of the association was estimated in the allele model, the dominant model, the recessive model, the homozygous genetic model, and the heterozygous genetic model, respectively. P < .05 was considered statistically significant. We performed subgroup according to ethnicity and source of controls.

Ethical approval

The ethical approval was not necessary for the reason that our study was a meta-analysis belonging to secondary analysis.

Results

Characteristics of included papers

The specific search process is shown in Fig. 1. A total of 563 references were preliminarily identified at first based on our selection strategy. We also identified 4 papers through other sources. Four hundred fifty-six records were left after removing repeated studies. We refer to titles or abstracts of all the included literatures, and then removed obviously irrelevant papers. In the end, the whole of the rest of the papers were checked based on the inclusion and exclusion criteria. Finally, 26 studies on FGFR2 (rs11200014, rs2981579, and rs1219648) polymorphism and the occurrence of BC were eventually included in our study. Characteristics of eligible analysis are summarized in Table 1. The 26 case–control papers were published between 2007 and 2015; among them, 1 study was performed in African, 17 in Asians, and 8 in Caucasians. All studies were case-controlled and all included cases had been diagnosed by a pathological examination.

Figure 1

Flow chart of studies selection in this meta-analysis.

Table 1

Characteristics of the studies included in the meta-analysis.

Meta-analysis results

Table 2 summarizes the FGFR2 (rs11200014, rs2981579, and rs1219648) polymorphisms genotype distribution and allele frequencies in case groups and control groups. Main results of our study are summarized in Table 3. There were 26 studies with 3425 cases and 4157 controls for FGFR2 rs11200014 variants. As shown in Table 3 and Fig. 2, the pooled results indicated that the correlation between FGFR2 rs11200014 polymorphism and the occurrence of BC was significant in any genetic model: Allele model (OR: 1.37; 95% CI: 1.14–1.66; P = .001), Dominant model (OR: 1.88; 95% CI: 1.23–2.85; P = .003), Recessive model (OR: 1.28; 95% CI: 1.12–1.46; P = .0003), Homozygous genetic model (OR: 1.66; 95% CI: 1.18–2.33; P = .003), Heterozygote comparison (OR: 1.85; 95% CI: 1.16–2.93; P = .009).

Table 2

Polymorphisms genotype distribution and allele frequency in cases and controls.

Table 3

Meta-analysis results.

Figure 2

Forest plots of rs11200014 (G > A) polymorphism and breast cancer risk (Recessive model AA vs GG + AG).

For rs2981579, 12 studies with 5356 cases and 6441 controls were included to assess the association. As shown in Table 3 and Fig. 3, the pooled ORs suggested that rs2981579 was significantly associated with BC susceptibility in all the 5 genetic models: Allele model 1.19 (95% CI: 1.13–1.25; P < .00001), Dominant model 1.25 (95% CI: 1.15–1.35; P < .00001), Recessive model 1.26 (95% CI: 1.16–1.38; P < .00001), Homozygous genetic model 1.40 (95% CI: 1.27–1.56; P < .00001), Heterozygote comparison 1.18 (95% CI: 1.08–1.28; P = .0002). When stratified by Source of controls, the results showed the same association between FGFR2 rs2981579 polymorphism and BC susceptibility in HB (Allele model: OR = 1.20, 95% CI = 1.13–1.28, P < .00001; Dominant model: OR = 1.27, 95% CI = 1.15–1.41, P < .00001; Recessive model: OR = 1.27, 95% CI = 1.14–1.41, P < .0001; Homozygous genetic model: OR = 1.44, 95% CI = 1.27–1.63, P < .00001; Heterozygote comparison: OR = 1.21, 95% CI = 1.09–1.34, P = .0005), although there not any genetic models attained statistical correlation in PB.

Figure 3

Forest plots of rs2981579 (C > T) polymorphism and breast cancer risk (Heterozygote comparison TC vs CC). (A) Overall. (B) HB. (C) PB.

Twenty papers with 13,173 cases and 14,917 controls were adopted to evaluate the association between the rs1219648 polymorphism and the BC risk. As shown in Table 3, Figs. 4 and 5, the association between rs1219648 variant and BC susceptibility was significant in any genetic model (Allele model: OR = 1.25, 95% CI = 1.20–1.29, P < .00001; Dominant model: OR = 1.32, 95% CI = 1.26–1.39, P < .00001; Recessive model: OR = 1.36, 95% CI = 1.28–1.45, P < .00001; Homozygous genetic model: OR = 1.54, 95% CI = 1.44–1.66, P < .00001; Heterozygote comparison: OR = 1.24, 95%CI = 1.18–1.31, P < .00001). The subgroup study stratified by Ethnicity showed an increased BC risk both in Asians (Allele model: OR = 1.23, 95% CI = 1.16–1.30, P < .00001; Dominant model: OR = 1.28, 95% CI = 1.18–1.39, P < .00001; Recessive model: OR = 1.35, 95% CI = 1.22–1.50, P < .00001; Homozygous genetic model: OR = 1.48, 95% CI = 1.32–1.67, P < .00001; Heterozygote comparison: OR = 1.21, 95% CI = 1.11–1.32, P < .0001) and Caucasians (Allele model: OR = 1.25, 95% CI = 1.20–1.30, P < .00001; Dominant model: OR = 1.33, 95% CI = 1.24–1.42, P < .00001; Recessive model: OR = 1.39, 95% CI = 1.22–1.58, P < .00001; Homozygous genetic model: OR = 1.57, 95% CI = 1.44–1.72, P < .00001; Heterozygote comparison: OR = 1.25, 95% CI = 1.16–1.34, P < .00001).We did not discuss the African subgroup for just 1 study from Africa. When stratified by Source of controls, the results showed the same association between FGFR2 rs1219648 polymorphism and BC susceptibility in HB (Allele model: OR = 1.24, 95% CI = 1.17–1.32, P < .00001; Dominant model: OR = 1.32, 95% CI = 1.21–1.44, P < .00001; Recessive model: OR = 1.35, 95% CI = 1.21–1.51, P < .00001; Homozygous genetic model: OR = 1.54, 95% CI = 1.35–1.74, P < .00001; Heterozygote comparison: OR = 1.26, 95% CI = 1.14–1.38, P < .00001) and PB (Allele model: OR = 1.25, 95% CI = 1.20–1.30, P < .00001; Dominant model: OR = 1.32, 95% CI = 1.24–1.40, P < .00001; Recessive model: OR = 1.37, 95% CI = 1.27–1.47, P < .00001; Homozygous genetic model: OR = 1.55, 95% CI = 1.42–1.68, P < .00001; Heterozygote comparison: OR = 1.26, 95% CI = 1.13–1.41, P < .0001).

Figure 4

Forest plots of rs1219648 (A > G) polymorphism and breast cancer risk stratified by ethnicity (Dominant model GA + GG vs AA).

Figure 5

Forest plots of rs1219648 (A > G) polymorphism and breast cancer risk stratified by Source of controls (Dominant model GA + GG vs AA).

Sensitivity analyses

As summarized in Table 1, all the studies conformed to the balance of HWE in controls except the studies by Chan et al[ in rs11200014 group and Cherdyntseva et al[ in rs1219648 group; however, after performing the sensitivity analyses, the overall outcomes were no statistically significant change when removing any of the articles, indicating that our study has good stability and reliability.

Detection for heterogeneity

Heterogeneity among studies was obtained by Q statistic. Random-effect models were applied if P value of heterogeneity tests was less than 0.1 (P ≤ .1); otherwise, fixed-effect models were selected (Table 3).

Publication bias

As Fig. 6 indicated, the symmetrical funnel plot indicated that there is no significant publication bias in the total population. We used Begg funnel plot and Egger test to evaluate the published bias, and no significant publication bias was found in the Begg test and Egger test (P > .05).

Figure 6

Funnel plot assessing evidence of publication bias. A. rs11200014 (G > A) (Recessive model AA vs GG + AG). B. rs2981579 (C > T) (Heterozygote comparison TC vs CC). C. rs1219648 (A > G) (Dominant model GA + GG vs AA). OR = odds ratio, SE = standard error.

Discussion

Human FGFR2 gene is located in 10q26, containing 22 exons and including 2 subtypes (FGFR2b and FGFR2c). FGFR2b is mainly expressed in epithelial cells, while FGF2c is mostly expressed in stromal cells.[ Studies indicated that FGFR2 may inhibit the occurrence and development of cancer. In a variety of epithelial tumor cell lines and tumor tissues, the expression of FGFR2b was significantly lower than that of normal epithelial cells, speculating that it might be related to the carcinostasis.[ But the mutations in FGFR2 gene can induce tumor occurrence, and the missense mutations in FGFR2 gene exist in the BC, gastric cancer, lung cancer, ovarian cancer, and endometrial cancer.[ As early as 1992, it was found that the expression of FGFR2 in human was significantly higher in ER-positive BC.[ Subsequently, a large number of studies on the relationship between the polymorphism of FGFR2 gene and BC have been implemented in different countries and regions around the world.[

Recently, researches have paid more attention to the human FGFR2, whose several SNPs, rs11200014 (G > A), rs2981579 (C > T), rs1219648 (A > G), may be associated with BC susceptibility in different crowds and different regions.[ The 3 SNPs are located in intron 2 of FGFR2, encoded by FGFR2 gene. Through interacting with the mitogenic ligand fibroblast growth factors (FGFs), a cascade of downstream signals will be activated, thus influencing on angiogenesis, wound healing, cell migration neural outgrowth, and embryonic development.[ However, the association between rs11200014, rs2981579, and rs1219648 polymorphism and BC susceptibility in related reports is still inconclusive between susceptible[ and protective.[ Thus, we conducted the meta-analysis to evaluate the relationship between FGFR2 (rs11200014, rs2981579, and rs1219648) polymorphism and BC risk.

Main results of our study are summarized in Table 3. There were 26 studies with 3425 cases and 4157 controls for rs11200014 variants. In the total population, the pooled results indicated that the correlation between rs11200014 polymorphism and the occurrence of BC was significant in any genetic model. The meta-analysis by Zhou et al[ indicated the same remarkable associations in Caucasians, but not in Asians and Africans. However, in Asian and African subgroups, there are only a few literatures and cases, and even only 1 paper in African subgroups. Such meta-analysis may not be particularly appropriate. For rs2981579, 12 studies with 5356 cases and 6441 controls were included to assess the association. Overall, the pooled ORs suggested that rs2981579 was significantly associated with BC susceptibility in all the 5 genetic models. The results were consistent with studies by Zhou et al[ and Peng et al[ studies, but they did not carry out further subgroup analysis. When stratified by source of controls, the results showed the same association between rs2981579 polymorphism and BC susceptibility in hospital populations, while there was not any genetic models attained statistical correlation in general populations, indicating that there was a difference in the association between rs2981579 polymorphism and BC risk among different groups. For the first time, this study conducted a subgroup analysis for rs2981579 stratified by source of controls, and for the first time came to this conclusion. However, further large-scale, multicenter, epidemiological studies are warranted to confirm this finding. Twenty papers with 13,173 cases and 14,917 controls were adopted to evaluate the association between the rs1219648 polymorphism and the BC risk. In the total population, the association between rs1219648 variant and BC risk was significant in any genetic model. The results were consistent with the studies by Zhang et al[ and Jia et al.[ The subgroup study stratified by Ethnicity showed an increased BC risk both in Asians and Caucasians. We did not discuss the African subgroup for just 1 study from African meet our inclusion criteria. In the study by Zhang et al,[ significantly increased risks were also found among Asian and Caucasian populations in all genetic models. However, these similar significant associations were not observed for African population, indicating that these associations vary in different ethnic populations. When stratified by Source of controls, the results showed the same association between rs1219648 polymorphism and BC susceptibility in HB and PB.

Overall, all the results for the 3 variants (rs11200014, rs2981579, and rs1219648) were partially consistent with the consequences of previous 5 meta-analyses,[ while they did not conduct analysis in different source of controls. And our sample size was several times than theirs, making our results more convincing. Furthermore, they did not use all the 5 genetic models (allele model, dominant model, recessive model, homozygous model, and heterozygous model) to assess the strength of association.

Our meta-analysis has several limitations. First, only published papers were included in our meta-analysis, and there may still be some unpublished studies in line with the conditions. Therefore, publication bias may exist; even no statistical evidence suggest publication bias in the meta-analysis. Second, for rs11200014 and rs2981579 variants, almost all of the included studies are from Asia. Therefore, we could not assess the association stratified by Ethnicity. Moreover, our study is a summary of the data. For lack of all individual raw data, we could not assess the cancer risk stratified by other covariates, including age, sex, environment, hormone level, menopause age, and other risk factors. We also need verify it from the level of molecular mechanism. Data from large-scale, multicenter, epidemiological studies are still needed to confirm the relationship between FGFR2 (rs11200014, rs2981579, and rs1219648) polymorphisms and BC risk, and the molecular mechanism for the associations need to be elucidated in future studies.

Conclusion

Our meta-analysis of case–control studies provides strong evidence that FGFR2 (rs11200014, rs2981579, and rs1219648) polymorphisms are significantly associated with the BC risk. For rs2981579, the association remained in hospital populations, while not in general populations. For rs1219648, the association remained in Asians, Caucasians, hospital populations, and general populations. However, further large-scale, multicenter, epidemiological studies are warranted to confirm this finding, and the molecular mechanism for the associations need to be elucidated in future studies.