Introduction {#sec1-0300060519869058}
============

Breast cancer (BC) is one of the most common malignancies and a major cause of death among women. In China, BC accounts for 15% of new cases of cancer in women, with morbidity and mortality increasing year by year.^[@bibr1-0300060519869058],[@bibr2-0300060519869058]^ Although the exact mechanism of BC is still not completely elucidated, a number of comprehensive analyses have shown that genomic and gene expression patterns contribute to the morbidity.^[@bibr3-0300060519869058],[@bibr4-0300060519869058]^

Fibroblast growth factor receptor 2 (FGFR2, also known as CEK3), located at chromosome 10q26 and containing 22 exons, is a member of the FGFR family of tyrosine kinase receptors.^[@bibr5-0300060519869058],[@bibr6-0300060519869058]^ It participates in the process of tumorigenesis by inducing mitogenic and survival signals and promoting invasiveness and angiogenesis.^[@bibr7-0300060519869058],[@bibr8-0300060519869058]^ FGFR2 overexpression was observed in breast cancer cell lines and breast tumor tissues in estrogen receptor (ER)-positive BC as early as 1992.^[@bibr9-0300060519869058]^ Since then, a large number of studies on the relationship between polymorphisms in the FGFR2 gene and BC have been implemented in different regions of the world.

Large genome-wide association studies (GWAS) have demonstrated that an intronic FGFR2 polymorphism, rs2981582, is significantly associated with BC.^[@bibr10-0300060519869058][@bibr11-0300060519869058]--[@bibr12-0300060519869058]^ Meyer et al.^[@bibr13-0300060519869058]^ reported that the T allele of rs2981582 has been linked to a higher level of FGFR2 transcription in both breast cancer cell lines and tumors. This link between rs2981582 and FGFR2 transcription is variable in patients of different ethnic origins and therefore sometimes contradictory. Murillo-Zamora et al.^[@bibr14-0300060519869058]^ reported that the T allele of rs2981582 was significantly associated with an increased risk of BC in Mexican women \[odds ratio (OR) = 1.24, 95% confidence interval (CI): 1.06--1.46). Ledwoń et al.^[@bibr15-0300060519869058]^ reported a similar result in Polish women (OR = 1.31, 95% CI: 1.17--1.45). In contrast, Bayraktar et al. found no relationship of rs2981582 with BC in an American population.^[@bibr16-0300060519869058]^ Chen et al.^[@bibr17-0300060519869058]^ demonstrated that the CT and TT genotypes of rs2981582 reduced BC risk in a Han Chinese population (*p* = 0.006). Therefore, to clarify the association of FGFR2 rs2981582 polymorphism and sporadic BC, we performed a comprehensive association analysis in a case-control study in a Chinese Han population; we then conducted a meta-analysis on all case-control studies to make a more accurate assessment of the relationship.

Materials and methods {#sec2-0300060519869058}
=====================

Patient recruitment {#sec3-0300060519869058}
-------------------

Patients were consecutively recruited from June to October 2016 among inpatients in Xi'an Hospital of Traditional Chinese Medicine. Ninety-five female patients who were histopathologically diagnosed with BC and did not have a family history of BC were included in the study. The control group comprised 140 unrelated healthy women. Patients with a history of other types of cancer, hormone replacement therapy, or other serious diseases were excluded from this study. This study was approved by the Ethical Committee of Xi'an Hospital of Traditional Chinese Medicine, and all patients and controls gave written informed consent for participation in this research. All of the studies were performed in accordance with the Declaration of Helsinki.

Genotyping {#sec4-0300060519869058}
----------

Venous blood samples were collected in tubes with EDTA anticoagulant and stored at −80°C until use. Genomic DNA was isolated from whole blood using the Wizard kit (Promega, Madison, WI, USA) according to the manufacturer's protocol. Genotyping of rs2981582 was carried out following time-of-flight mass spectrometry on a MassARRAY iPLEX platform (Sequenom, San Diego, CA, USA). The average genotype call rate for the single nucleotide polymorphism (SNP) was \>99%.

Meta-analysis {#sec5-0300060519869058}
-------------

Studies published through 20 May 2019 were identified by a systematic literature search in PubMed, Embase, Web of Knowledge, China National Knowledge Infrastructure (CNKI), and WanFang Data. The medical subject headings (MeSH) and title/abstract were used to identify all eligible studies that mainly focused on the genotype of rs2981582 polymorphism. Eligible studies had to meet the following criteria: (1) the study evaluated the association between risk of BC and FGFR2 rs2981582 polymorphism in Asian populations; (2) the report focused on detailed genotype frequencies among patients with sporadic BC; and (3) the investigation was a case-control study. The exclusion criteria were as follows: (1) comment, review, or editorial articles; and (2) studies without detailed genotype data or with overlapping data. Publication bias was evaluated by using the Begg's test.

Statistical analysis {#sec6-0300060519869058}
--------------------

All statistical analyses were performed using SPSS (version 17.0, SPSS Inc., Chicago, IL, USA). Pearson's chi-squared test or the Mann-Whitney U test was performed to test the association between rs2981582 polymorphism in cases and controls. Logistic regression analysis was used to evaluate the contribution of genetic and nongenetic factors under the dominant, recessive, and additive models.

Stata software (version 12.0; Stata Corporation, College Station, TX, USA) was used for meta-analysis. The OR and 95% CI were calculated to assess the relationship between the polymorphism and BC. Pooled ORs were obtained from combinations of single study under the dominant model (TT + TC vs. CC), the recessive model (TT vs. TC + CC), the homozygous model, and the heterozygote model. Heterogeneity was tested by the Q test (Chi squared test; *p* \< 0.10 was considered indicative of statistically significant heterogeneity) and the *I*^2^ statistic (values of 25%, 50%, and 75% were considered to represent low, medium, and high heterogeneity, respectively). If *I*^2^ \> 50%, the random-effect model (DerSimonian--Laird method) was used to calculate the pooled ORs; otherwise, the fixed effect model (Mantel--Haenszel method) was used. Begg's linear regression tests were applied to assess the potential publication bias.

Results {#sec7-0300060519869058}
=======

Characteristics of the study participants {#sec8-0300060519869058}
-----------------------------------------

Ninety-five patients and 140 healthy controls were recruited in our study. Seventy patients were at clinical stage I to II and 25 patients were at stage III. In the patient group, 24.3% had a drinking habit and 25.1% had a history of smoking. In the healthy control group, 18.5% had a drinking habit and 19.4% had a history of smoking. The demographic characteristics of the subjects in the patient and control groups are summarized in [Table 1](#table1-0300060519869058){ref-type="table"}.

###### 

Demographic data of enrolled participants.

![](10.1177_0300060519869058-table1)

  Variable              Case (n = 95)   Controls (n = 140)   *p*-value
  --------------------- --------------- -------------------- -----------
  Age (years)           47.22 ± 12.43   48.15 ± 10.61        0.192
  Body weight (kg)      48.31 ± 11.64   45.20 ± 10.57        0.086
  Tobacco consumption                                        
   Yes                  25.1%           19.4%                0.091
   No                   74.9%           80.6%                
  Drinking                                                   
   Yes                  24.3%           18.5%                0.065
   No                   75.7%           76.5%                
  Clinical stage                                             
   I-II                 70                                   
   III                  25                                   
  ER                                                         
   Positives            31 (32.6%)                           
   Negative             64 (67.4%)                           

Genetics analysis {#sec9-0300060519869058}
-----------------

The minor allele frequency (MAF) was 0.419. A significant difference was observed between the patient group and the control group in the three genotypes, TT, TC, and CC (*p* = 0.021) ([Table 2](#table2-0300060519869058){ref-type="table"}). A logistic regression analysis of rs2981582 SNP genotype between patients and controls was also performed. The results showed that the TT and TC alleles were significantly correlated with BC development (OR = 1.21, 95% CI: 1.050--2.27, *p* = 0.019; OR = 1.81, 95% CI: 1.24--2.73, *p* = 0.035), even after adjusting for age, drinking, smoking, and ER status ([Table 3](#table3-0300060519869058){ref-type="table"}).

###### 

Genotypes of FGFR2 rs2981582 polymorphism between patient (case) and control group.

![](10.1177_0300060519869058-table2)

  rs2981582   Control   Case   *p*-value
  ----------- --------- ------ -----------
  CC          16        24     0.021
  TC          59        33     
  TT          65        38     
  C           91        81     0.025
  T           189       109    

###### 

Logistic regression analysis of rs2981582 SNP genotype with breast cancer.

![](10.1177_0300060519869058-table3)

  rs2981582         OR          95% CI        *p*-value   OR\*        95% CI\*       *p*-value\*
  ----------------- ----------- ------------- ----------- ----------- -------------- -------------
  CC                Reference                             Reference                  
  TT                1.21        1.050--2.27   **0.019**   1.19        1.172--2.957   **0.017**
  TC                1.81        1.24--2.73    **0.035**   1.80        1.169--2.71    **0.031**
  Dominant model    2.15        1.25--5.31    **0.043**   2.1         1.21--5.28     **0.041**
  Recessive model   1.14        0.37--1.9     0.165       1.12        0.31--1.85     0.152

\*Adjusting for age, body weight, smoking, drinking, and estrogen receptor status. Significant associations are marked in bold.

In addition, we assessed the influence of genetic models (dominant, recessive, and additive models) on BC risk; however, no significant associations in the recessive model were observed (OR = 1.14, 95% CI: 0.37--1.9). In contrast, there was a significant difference in the dominant model (OR = 2.15, 95% CI: 1.25--5.31, *p* = 0.043). These differences remained even after adjusting for age, drinking, smoking, and ER status ([Table 3](#table3-0300060519869058){ref-type="table"}).

Meta-analysis {#sec10-0300060519869058}
-------------

Fifteen relevant studies were identified from the literature search. The flow diagram of the search process is illustrated in [Figure 1](#fig1-0300060519869058){ref-type="fig"}, and the characteristics of the enrolled studies are summarized in [Table 4](#table4-0300060519869058){ref-type="table"}. Heterogeneity was identified by the Q test and *I*^2^ statistic, and no significant publication bias was found by the Begg's test (*p* \> 0.05) ([Figure 2](#fig2-0300060519869058){ref-type="fig"}). We performed a sensitivity analysis to assess the influence of each individual study on the pooled OR by sequentially removing each eligible study. The results indicated that no single study influenced the quality of the pooled ORs in the sensitivity analysis ([Figure 3](#fig3-0300060519869058){ref-type="fig"}).

![Flow sheet summarizing identification and selection of studies.](10.1177_0300060519869058-fig1){#fig1-0300060519869058}

###### 

The basic characteristics of the 15 studies in the meta-analysis, showing FGFR2 rs2981582 single nucleotide polymorphism genotype and allele distribution in breast cancer patients (cases) and controls.

![](10.1177_0300060519869058-table4)

  Studies                                         Year   Total Number   Case   Control                                                                         
  ----------------------------------------------- ------ -------------- ------ --------- ----- ----- ------ ------ ----- ----- ----- ------ ------ ----------- -------
  Liang J et al.^[@bibr22-0300060519869058]^      2008   1049           1073   119       460   447   698    1354   91    439   532   621    1503   Taqman      0.974
  Kawase T et al. ^[@bibr23-0300060519869058]^    2009   456            912    42        192   221   276    634    63    347   502   473    1351   TaqMan      0.773
  Hu X et al.^[@bibr24-0300060519869058]^         2011   203            200    47        78    78    172    234    26    95    79    147    253    PCR-RFLP    0.758
  Li X et al.^[@bibr25-0300060519869058]^         2011   869            464    54        180   167   288    514    60    189   192   309    573    MassArray   0.219
  Ren L et al.^[@bibr26-0300060519869058]^        2011   936            471    130       400   426   660    1252   56    181   234   293    649    Taqman      0.361
  Fu F et al.^[@bibr27-0300060519869058]^         2012   118            104    21        55    42    97     139    8     47    49    63     145    MassARRAY   0.474
  Chan M et al.^[@bibr28-0300060519869058]^       2012   1191           1534   155       527   486   837    1499   162   618   695   942    2008   Taqman      0.165
  Dai J et al.^[@bibr29-0300060519869058]^        2012   914            967    216       820   732   1252   2284   164   796   884   1124   2564   TaqMan      0.424
  Xi J et al.^[@bibr30-0300060519869058]^         2014   839            863    100       423   292   623    1007   94    376   379   564    1134   MALDI-TOF   0.959
  Siddiqui S et al.^[@bibr31-0300060519869058]^   2014   368            484    56        168   144   280    456    53    205   226   311    657    PCR-RFLP    0.526
  Liang H et al.^[@bibr32-0300060519869058]^      2015   609            882    103       266   238   472    742    111   375   370   597    1115   MassARRAY   0.298
  He YN et al.^[@bibr33-0300060519869058]^        2015   253            343    41        103   109   185    321    49    157   137   255    431    iMLDR       0.710
  Jiang Q et al.^[@bibr34-0300060519869058]^      2014   35             35     16        10    9     42     28     4     18    13    26     44     DHPLC       0.549
  Xu WH et al.^[@bibr35-0300060519869058]^        2012   280            280    20        174   86    214    346    29    131   120   189    371    PCR-RFLP    0.439
  Lao HM et al.^[@bibr36-0300060519869058]^       2012   623            620    46        150   155   242    460    48    151   151   247    453    MassARRAY   0.301

HWE, Hardy-Weinberg equilibrium; PCR-RFLP, polymerase chain reaction-restriction fragment length polymorphisms, MALDI-TOF, matrix-assisted laser desorption/ionization time-of-flight, iMLDR, improved multiplex ligase detection reaction; DHPLC, denaturing high performance liquid chromatography.

![The publication bias examined by Begg's test in the dominant model (TC + TT vs. CC).](10.1177_0300060519869058-fig2){#fig2-0300060519869058}

![Sensitivity analysis of the association between FGFR2 rs2981582 polymorphism and breast cancer susceptibility in the dominant model (TC + TT vs. CC).](10.1177_0300060519869058-fig3){#fig3-0300060519869058}

We used a pooled meta-analysis to determine BC risk and rs2981582 polymorphism in the Asian population under the recessive, dominant, homozygous, and heterozygote models ([Figure 4](#fig4-0300060519869058){ref-type="fig"}). Our results showed that all four models could significantly increase the BC risk (dominant model: OR = 1.259, 95% CI: 1.187--1.336; recessive model: OR = 1.29, 95% CI: 1.13--1.46; heterozygote model: OR = 1.22, 95% CI: 1.14--1.30; homozygote model: OR = 1.41, 95% CI: 1.28--1.55).

![Forest plot of FGFR2 rs2981582 polymorphism and breast cancer susceptibility. (a) Dominant model (TC + TT vs. CC); (b) recessive model (TT vs. TC + CC); (c) heterogeneity model (TC vs. CC); and (d) homozygote model (TT vs. CC).](10.1177_0300060519869058-fig4){#fig4-0300060519869058}

Discussion {#sec11-0300060519869058}
==========

Breast cancer is a disease that results from the interaction of environmental and genetic factors, and it is now a leading cause of cancer-related deaths in women worldwide.^[@bibr18-0300060519869058],[@bibr19-0300060519869058]^ The incidence rate is increasing annually and patients are being diagnosed at an earlier age. However, the etiology, in terms of genetic risk factors associated with prognosis, is still not fully understood. FGFR2 has been reported to be associated with many diseases, especially with carcinogenesis. As a transmembrane protein, FGFR2 has an extracellular region, a transmembrane region, and an intracellular region. Once activated, it begins a cascade reaction through the RAS-MAPK and JAK-STAT signal pathways and then regulates the transcription of downstream genes involved in physiological and pathological activities, such as cell proliferation, differentiation, migration, and apoptosis.

In our study, the FGFR2 rs2981582 polymorphisms in the TT and TC genotypes were significantly correlated with BC development. These results were partly in accordance with reports by Liu et al.^[@bibr20-0300060519869058]^ and Mazhar et al.^[@bibr21-0300060519869058]^ However, those authors reported a significant association of genotype TT with the development of BC, whereas in our study both the TT and TC genotypes were significantly associated with BC. In addition, we found that either the dominant or additive model carried an increased risk of BC. To further elucidate the possible relationship of the polymorphism and BC, we performed a meta-analysis that included 15 studies in the Asian population. The meta-analysis results showed that the recessive model was also significantly associated with BC, which was different from our result in the case-control study.

Meyer et al.^[@bibr13-0300060519869058]^ supplied the first experimental evidence of mechanisms between particular FGFR2 intronic SNPs and breast cancer risk. Those authors showed that 2 FGFR2 intronic SNPs (rs7895676 and rs2981578) altered the binding affinity of FGFR2 for transcription factors and increased FGFR2 expression.^[@bibr13-0300060519869058]^ However, based on published studies, the associations of rs2981582 polymorphism with BC are still inconsistent. These different conclusions may result from the diversity of genetic backgrounds and carcinogenic factors. As a member of the FGFR family, FGFR2 has been found to be overexpressed in breast cancer cell lines. Aberrant expression of alternatively spliced isoforms of FGFR2 has been shown to activate cell mitogenesis and differentiation and lead to transformation in breast cancer cells. Nevertheless, this could be influenced by age, family history, reproductive and gynecologic factors, as well as lifestyle factors, such as alcohol consumption and lack of physical activity. Thus, the association of FGFR2 rs2981582 polymorphism with BC is still worth exploring.

Although our results might be useful in helping to identify patients with BC risk, the results should be considered preliminary and further research is necessary. However, we cannot ignore some of the limitations. First, the mechanism of BC is complex and we observed only one SNP; therefore, our results cannot completely explain the association of genetic polymorphism and BC. Second, our sample size for BC cases was relatively small and all patients were from ShaanXi Province in northwest China, which may bias the final results. Third, the meta-analysis results were not representative because all enrolled studies were from an Asian population. Furthermore, the existence of confounding factors, such as family history, hormone metabolism, and menopausal status, might have distorted the meta-analysis, explaining the inconsistent results between the case-control study and the meta-analysis.

In summary, our results showed an association of the FGFR2 rs2981582 polymorphism with BC in an Asian population. However, these data need to be replicated in a larger cohort of patients, and functional studies are necessary to investigate whether and how the polymorphism might influence key pathways involved in the pathogenesis of BC.

Declaration of conflicting interest {#sec12-0300060519869058}
===================================

The authors declare that there is no conflict of interest.

Funding {#sec13-0300060519869058}
=======

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by China Postdoctoral Science Foundation (No. 2017M613179) and The National Natural Science Foundation of China (No. 81691877).
