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. 2016 Dec 9;8(2):3454–3470. doi: 10.18632/oncotarget.13839

Association between FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphism and breast cancer susceptibility: a meta-analysis

Yafei Zhang 1, Xianling Zeng 2, Pengdi Liu 1, Ruofeng Hong 1, Hongwei Lu 1, Hong Ji 1, Le Lu 1, Yiming Li 1
PMCID: PMC5356895  PMID: 27966449

Abstract

The association between fibroblast growth factor receptor 2 (FGFR2) polymorphism and breast cancer (BC) susceptibility remains inconclusive. The purpose of this systematic review was to evaluate the relationship between FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphism and BC risk. 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. Thirty-five studies published from 2007 to 2015 were included in this meta-analysis. The pooled results showed that there was significant association between all the 3 variants and BC risk in any genetic model. Subgroup analysis was performed on rs2981582 and rs2420946 by ethnicity and Source of controls, the effects remained in Asians, Caucasians, population-based and hospital-based groups. We did not carryout subgroup analysis on rs2981578 for the variant included only 3 articles. This meta-analysis of case-control studies provides strong evidence that FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphisms were significantly associated with the BC risk. For rs2981582 and rs2420946, the association remained significant in Asians, Caucasians, general populations and hospital populations. However, further large scale multicenter epidemiological studies are warranted to confirm this finding and the molecular mechanism for the association need to be elucidated further.

Keywords: breast cancer, FGFR2, polymorphism

INTRODUCTION

Breast cancer (BC), one of the most common malignant tumors among women worldwide, has the highest mortality rate in female cancer. Its incidence rate is increasing year by year and the patients are becoming younger and younger in the world [1, 2]. BC is the result of the interaction of environmental and genetic factors. Under the same carcinogenic factors, only a small fraction of people develop BC, which suggests that the genetic background differences lead to individual differences in BC susceptibility [3].

In recent years, genome-wide association study (GWAS) provides a good technical support for the study on the susceptibility loci with high variation frequency and low penetrance [4]. Large numbers of BC related susceptibility genes and single nucleotide polymorphism sites have been found through GWAS, such as LSP1, MAP3K1, FGFR2, TGFB1, TOX3, etc [5]. The discovery of these genes will have an important impact on the prevention and treatment of BC, especially FGFR2 (rs2981582, rs2420946 and rs2981578). FGFR2 gene is located in 10q26, and contains at least 22 exons [6]. FGFR2 is a member of the tyrosine kinase receptor family. It is a transmembrane protein, and is mainly composed of three parts: extracellular region, transmembrane region and intracellular region. The extracellular segment has three immunoglobulin like protein functional areas. Through the combination with FGFs, the functional areas could activate the tyrosine kinase activity and induce receptor tyrosine phosphorylation. It also starts series of cascade reaction through the RAS-MAPK, JAK-STATs and PLC-Y signal system, and then regulate the transcription of downstream genes involve in the body's physiological and pathological activities, such as cell proliferation, differentiation, migration and apoptosis, angiogenesis, skeletal development. So FGFR2 plays an important role in the processes of human growth and development [7].

Lots of researches have reported the association between FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphism and BC risk. However, due to differences in ethnic and regional and other factors, the conclusions of related reports are still inconclusive. Raskin et al [8] found FGFR2 rs2420946 was significantly associated with BC risk in Ashkenazi and Sephardi Jews, with a similar but not significant trend in Arabs. Liang et al's [9] study indicated that each of thesingle nucleotide polymorphisms (SNPs) (rs2981582and rs2420946) was significantly associated with increased BC risk, and the risk was the highest for those carrying the 2 mutation sites at the same time. While, there are also some different reports. Liu et al [10] found that FGFR2 rs2420946was not significantly correlated with the occurrence of BC in Chinese population. These different conclusions may result from the diversity of genetic background and carcinogenic factors, therefore, further studies in different populations should be implemented to assess the correlation between SNPs and BC risk. Although five meta-analysis [11–15] on the associations between FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphism and BC risk had been implemented, yet the results remained inconclusive and some just no subgroup. Therefore, we carried out this meta-analysis on all the included case-control researches to make a more accurate assessment of the relationship.

RESULTS

Characteristics of included papers

The specific search process is shown in Figure 1. A total of 563 references were preliminarily identified at first based on our selection strategy. We also identified 2 papers through other sources. 454 records 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, 35 studies on FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphism and the occurrence of BC were eventually included in our study. Characteristics of eligible analysis are shown in Table 1. The 35 case-control papers were published between 2007 and 2015, among them, 1 study was performed in African, 17 in Asian, 14 in Caucasians and 3 in both Asian and Caucasians. All studies were case-controlled.

Figure 1. Flow chart of studies selection in this meta-analysis.

Figure 1

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Table 1. Characteristics of the studies included in the meta-analysis.

First author Year Country Ethnicity Source of controls Genotyping medthod Number(case/control) HWE
rs2981582 (C>T)
Kawase [20] 2009 Japan Asian HB TaqMan 455/912 0.773315
Hu [25] 2011 China Asian PB PCR-RFLP 203/200 0.758366
Li [26] 2011 China Asian HB MassArray 401/441 0.219207
Chen [27] 2012 China Asian PB PCR-SSCP 388/424 0.048991
Butt [28] 2012 Swedish Caucasian PB MassArray 713/1399 0.816442
Shan [29] 2012 Tunisian African PB TaqMan 600/358 0.060883
Fu [30] 2012 China Asian HB iPLEX 118/104 0.474243
Campa [31] 2011 Mixed Mixed PB Taqman 8313/11594 0.607558
Slattery [32] 2011 American Caucasian PB Taqman 1734/2040 0.822253
Han [33] 2011 Korean Asian PB Taqman 3232/3489 0.361342
Tamimi [34] 2010 Swedish Caucasian PB Taqman 680/734 0.535243
Gorodnova [35] 2010 Russian Caucasian NA Taqman 140/174 0.000621
Ren [36] 2010 China Asian HB Taqman 956/471 0.024883
McInerney [37] 2009 British Caucasian PB KASPar 941/997 0.83057
Boyarskikh [38] 2009 Russia Caucasian PB Taqman 744/628 0.659988
Garcia-Closas [39] 2008 Mixed Mixed PB, HB Taqman 16882/26058 0.892667
Liang [9] 2008 China Asian HB Taqman 1026/1062 0.97418
Antoniou [40] 2008 European Mixed NA Taqman, MALDI-TOF 4990/4301 0.596563
Zhao [41] 2010 China Asian HB PCR-RFLP 956/471 0.024883
Xi [42] 2014 China Asian HB MALDI-TOF 815/849 0.959015
Campa [19] 2015 Mixed Caucasian PB TaqMan 1234/12231 0.779613
Slattery [43] 2013 American Caucasian PB multiplexed bead array 3560/4138 0.364662
Chan [44] 2012 China Asian HB Taqman 1168/1475 0.164674
Dai [45] 2012 China Asian HB TaqMan 1768/1844 0.423521
Jara [46] 2013 Chile Caucasian PB TaqMan 351/802 0.138274
Liang [18] 2015 China Asian HB MassARRAY 607/856 0.298476
Liu [47] 2013 China Asian HB PCR-RFLP 203/200 0.758366
Murillo-Zamora [48] 2013 Mexico Caucasian PB Multiplexed assays 687/907 0.351295
Ottini [49] 2013 Italy Caucasian PB TaqMan 413/745 0.76716
Ozgoz [50] 2013 Turkey Caucasian PB PCR-RFLP 31/30 0.281979
Siddiqui [51] 2014 India Asian HB PCR-RFLP 368/484 0.526174
rs2420946 (C>T)
Raskin [8] 2008 USA Caucasian PB TaqMan 1480/1474 0.224235
Kawase [20] 2009 Japan Asian HB TaqMan 453/912 0.519554
Liu [10] 2009 China Asian PB PCR-RFLP 106/116 0.361602
Hu [25] 2011 China Asian PB PCR-RFLP 203/200 0.325727
Li [26] 2011 China Asian HB MassArray 391/432 0.703117
Fu [30] 2012 China Asian HB iPLEX 118/104 0.505449
Liang [9] 2008 China Asian HB Taqman 1020/1050 0.413194
Hunter [52] 2007 USA Caucasian PB Taqman 2912/3212 0.293864
Jara [46] 2013 Chile Caucasian PB TaqMan 351/802 0.292806
Liang [18] 2015 China Asian HB MassARRAY 603/847 0.063645
Liu [47] 2013 China Asian HB PCR-RFLP 203/200 0.325727
rs2981578 (A>G)
Chen [27] 2012 China Asian PB PCR-SSCP 378/458 0.290218
Lin [53] 2012 Taiwan Asian PB PCR-RFLP 87/70 0.724138
Siddiqui [51] 2014 India Asian HB PCR-RFLP 368/484 0.278456
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HWE: hardy-weinberg equilibrium; PB: population based; HB: hospital-based.

Meta-analysis results

The FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphisms genotype distribution and allele frequencies incase groups and control groups were shown in Table 2. Main results of our study were shown in Table 3. There were 31 studies with 54,677 cases and 80,418 controls for FGFR2 rs2981582 variants. As shown in Table 3, Figure 2 and Figure 3, the pooled results indicated that the correlation between FGFR2 rs2981582 polymorphism and the occurrence of BC was significant in any genetic model: Allele model (OR: 1.23; 95% CI: 1.19- 1.26; P< 0.00001), Dominant model (OR: 1.29; 95% CI: 1.24-1.34; P< 0.00001), Recessive model (OR: 1.35; 95% CI: 1.31-1.40; P<0.00001), Homozygous genetic model (OR: 1.50; 95% CI: 1.42-1.58; P< 0.00001), Heterozygote comparison (OR: 1.22; 95% CI: 1.17-1.27; P< 0.00001). In ethnicity specific analysis, FGFR2 rs2981582 were significantly associated with BC risk both in Asians (Allele model: OR=1.19, 95% CI=1.15- 1.24, P< 0.00001; Dominant model: OR=1.23, 95% CI=1.17-1.29, P< 0.00001; Recessive model: OR=1.31, 95% CI=1.21-1.42, P< 0.00001; Homozygous genetic model: OR=1.42, 95% CI=1.31-1.54, P< 0.00001; Heterozygote comparison: OR=1.18, 95% CI=1.12-1.25, P< 0.00001) and Caucasians (Allele model: OR=1.25, 95% CI=1.21-1.30, P< 0.00001; Dominant model: OR=1.33, 95% CI=1.26-1.40, P< 0.00001; Recessive model: OR=1.37, 95% CI=1.28-1.46, P< 0.00001; Homozygous genetic model: OR=1.56, 95% CI=1.45-1.68, P< 0.00001; Heterozygote comparison: OR=1.26, 95% CI=1.19-1.33, P< 0.00001). We didn't discuss the African subgroup for just one study from African. The analysis in different source of controls showed the same association between FGFR2 rs2981582 polymorphism and BC susceptibility both in HB(Allele model: OR=1.22, 95% CI=1.16-1.27, P< 0.00001; Dominant model: OR=1.27, 95% CI=1.20-1.35, P< 0.00001; Recessive model: OR=1.31, 95% CI=1.20-1.44, P< 0.00001; Homozygous genetic model: OR=1.45, 95% CI=1.31-1.60, P< 0.00001; Heterozygote comparison: OR=1.23, 95% CI=1.15-1.31, P< 0.00001) and PB(Allele model: OR=1.24, 95% CI=1.19-1.29, P< 0.00001; Dominant model: OR=1.31, 95% CI=1.23-1.40, P< 0.00001; Recessive model: OR=1.35, 95% CI=1.29-1.42, P< 0.00001; Homozygous genetic model: OR=1.50, 95% CI=1.43-1.58, P< 0.00001; Heterozygote comparison: OR=1.23, 95% CI=1.15-1.31, P< 0.00001).

Table 2. Polymorphisms genotype distribution and allele frequency in cases and controls.

First author Genotype (N) Allele frequency (N)
Case Control Case Control
rs2981582 (C>T) Total TT TC CC Total TT TC CC T C T C
Kawase [20] 455 42 192 221 912 63 347 502 276 634 473 1351
Hu [25] 203 47 78 78 200 26 95 79 172 234 147 253
Li [26] 401 54 180 167 441 60 189 192 288 514 309 573
Chen [27] 388 48 208 132 424 60 224 140 304 472 344 504
Butt [28] 713 124 356 233 1399 185 653 561 604 822 1023 1775
Shan [29] 600 147 315 138 358 64 154 140 609 591 282 434
Fu [30] 118 21 55 42 104 8 47 49 97 139 63 145
Campa [31] 8313 1568 3951 2794 11594 1718 5456 4420 7087 9539 8892 14296
Slattery [32] 1734 315 884 535 2040 318 981 741 1514 1954 1617 2463
Han [33] 3232 342 1393 1497 3489 281 1457 1751 2077 4387 2019 4959
Tamimi [34] 680 136 304 240 734 91 324 319 576 784 506 962
Gorodnova [35] 140 23 67 50 174 25 54 95 113 167 104 244
Ren [36] 956 130 400 426 471 56 181 234 660 1252 293 649
McInerney [37] 941 214 458 269 997 179 483 335 886 996 841 1153
Boyarskikh [38] 744 126 371 247 628 71 273 284 623 865 415 841
Garcia-Closas [39] 16882 3243 8218 5421 26058 3747 12255 10056 14704 19060 19749 32367
Liang [9] 1026 119 460 447 1062 91 439 532 698 1354 621 1503
Antoniou [40] 4990 936 2407 1647 4301 703 2051 1547 4279 5701 3457 5145
Zhao [41] 956 130 400 426 471 56 181 234 660 1252 293 649
Xi [42] 815 100 423 292 849 94 376 379 623 1007 564 1134
Campa [19] 1234 241 608 385 12231 1847 5793 4591 1090 1378 9487 14975
Slattery [43] 3560 708 1749 1103 4138 638 2009 1491 3165 3955 3285 4991
Chan [44] 1168 155 527 486 1475 162 618 695 837 1499 942 2008
Dai [45] 1768 216 820 732 1844 164 796 884 1252 2284 1124 2564
Jara [46] 351 80 178 93 802 141 366 295 338 364 648 956
Liang [18] 607 103 266 238 856 111 375 370 472 742 597 1115
Liu [47] 203 47 78 78 200 26 95 79 172 234 147 253
Murillo-Zamora [48] 687 145 309 233 907 139 415 353 599 775 693 1121
Ottini [49] 413 98 205 110 745 139 361 245 401 425 639 851
Ozgoz [50] 31 9 16 6 30 10 12 8 34 28 32 28
Siddiqui [51] 368 56 168 144 484 53 205 226 280 456 311 657
rs2420946 (C>T) Total TT TC CC Total TT TC CC T C T C
Raskin [8] 1480 356 715 409 1474 285 700 489 1427 1533 1270 1678
Kawase [20] 453 60 226 167 912 99 416 397 346 560 614 1210
Liu [10] 106 16 51 39 116 21 51 44 83 129 93 139
Hu [25] 203 50 92 61 200 34 105 61 192 214 173 227
Li [26] 391 74 186 131 432 68 202 162 334 448 338 526
Fu [30] 118 25 55 38 104 9 48 47 105 131 66 142
Liang [9] 1020 163 519 338 1050 142 505 403 845 1195 789 1311
Hunter [52] 2912 603 1409 900 3212 484 1562 1166 2615 3209 2530 3894
Jara [46] 351 85 175 91 802 143 374 285 345 357 660 944
Liang [18] 603 116 297 190 847 145 379 323 529 677 669 1025
Liu [47] 203 50 92 61 200 34 105 61 192 214 173 227
rs2981578 (A>G) Total GG GA AA Total GG GA AA G A G A
Chen [27] 378 150 188 40 458 160 212 86 488 268 532 384
Lin [53] 87 35 39 13 70 21 36 13 109 65 78 62
Siddiqui [51] 368 129 185 54 484 151 228 105 443 293 530 438
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Table 3. Meta-analysis results.

Outcome or Subgroup Studies Participants Statistical Method Effect Estimate P value Heterogeneity
I2 P value
Allele model
rs2981582 (C>T) 31 270190 OR (M-H, Random, 95% CI) 1.23 [1.19, 1.26] < 0.00001 41% 0.01
Asian 15 51892 OR (M-H, Fixed, 95% CI) 1.19 [1.15, 1.24] < 0.00001 0% 0.54
Caucasian 12 72106 OR (M-H, Fixed, 95% CI) 1.25 [1.21, 1.30] < 0.00001 4% 0.4
HB 12 36020 OR (M-H, Fixed, 95% CI) 1.22 [1.16, 1.27] < 0.00001 0% 0.87
PB 16 129080 OR (M-H, Random, 95% CI) 1.24 [1.19, 1.29] < 0.00001 46% 0.02
rs2420946 (C>T) 11 34378 OR (M-H, Fixed, 95% CI) 1.23 [1.18, 1.29] < 0.00001 0% 0.67
Asian 8 13916 OR (M-H, Fixed, 95% CI) 1.19 [1.11, 1.28] < 0.00001 0% 0.67
Caucasian 3 20462 OR (M-H, Fixed, 95% CI) 1.26 [1.19, 1.33] < 0.00001 0% 0.53
HB 6 12666 OR (M-H, Fixed, 95% CI) 1.20 [1.12, 1.29] < 0.00001 0% 0.61
PB 5 21712 OR (M-H, Fixed, 95% CI) 1.25 [1.18, 1.32] < 0.00001 0% 0.5
rs2981578 (A>G) 3 3690 OR (M-H, Fixed, 95% CI) 1.29 [1.13, 1.47] 0.0002 0% 0.93
Dominant model
rs2981582 (C>T) 31 135095 OR (M-H, Random, 95% CI) 1.29 [1.24, 1.34] < 0.00001 46% 0.003
Asian 15 25946 OR (M-H, Fixed, 95% CI) 1.23 [1.17, 1.29] < 0.00001 0% 0.63
Caucasian 12 36053 OR (M-H, Fixed, 95% CI) 1.33 [1.26, 1.40] < 0.00001 16% 0.28
HB 12 18010 OR (M-H, Fixed, 95% CI) 1.27 [1.20, 1.35] < 0.00001 0% 0.89
PB 16 64540 OR (M-H, Random, 95% CI) 1.31 [1.23, 1.40] < 0.00001 55% 0.004
rs2420946 (C>T) 11 17189 OR (M-H, Fixed, 95% CI) 1.28 [1.20, 1.37] < 0.00001 0% 0.77
Asian 8 6958 OR (M-H, Fixed, 95% CI) 1.25 [1.13, 1.39] < 0.00001 0% 0.75
Caucasian 3 10231 OR (M-H, Fixed, 95% CI) 1.31 [1.20, 1.42] < 0.00001 0% 0.38
HB 6 6333 OR (M-H, Fixed, 95% CI) 1.28 [1.15, 1.42] < 0.00001 0% 0.73
PB 5 10856 OR (M-H, Fixed, 95% CI) 1.29 [1.19, 1.40] < 0.00001 0% 0.44
rs2981578 (A>G) 3 1845 OR (M-H, Fixed, 95% CI) 1.71 [1.32, 2.21] < 0.0001 0% 0.63
Recessive model
rs2981582 (C>T) 31 135095 OR (M-H, Fixed, 95% CI) 1.35 [1.31, 1.40] < 0.00001 15% 0.24
Asian 15 25946 OR (M-H, Fixed, 95% CI) 1.31 [1.21, 1.42] < 0.00001 19% 0.24
Caucasian 12 36053 OR (M-H, Fixed, 95% CI) 1.37 [1.28, 1.46] < 0.00001 0% 0.74
HB 12 18010 OR (M-H, Fixed, 95% CI) 1.31 [1.20, 1.44] < 0.00001 0% 0.5
PB 16 64540 OR (M-H, Fixed, 95% CI) 1.35 [1.29, 1.42] < 0.00001 0% 0.45
rs2420946 (C>T) 11 17189 OR (M-H, Fixed, 95% CI) 1.36 [1.26, 1.48] < 0.00001 4% 0.4
Asian 8 6958 OR (M-H, Fixed, 95% CI) 1.27 [1.12, 1.45] 0.0003 8% 0.37
Caucasian 3 10231 OR (M-H, Fixed, 95% CI) 1.42 [1.29, 1.57] < 0.00001 0% 0.61
HB 6 6333 OR (M-H, Fixed, 95% CI) 1.27 [1.11, 1.46] 0.0006 4% 0.39
PB 5 10856 OR (M-H, Fixed, 95% CI) 1.41 [1.28, 1.56] < 0.00001 0% 0.46
rs2981578 (A>G) 3 1845 OR (M-H, Fixed, 95% CI) 1.24 [1.02, 1.50] 0.03 0% 0.75
Homozygous genetic model
rs2981582 (C>T) 31 71786 OR (M-H, Random, 95% CI) 1.50 [1.42, 1.58] < 0.00001 33% 0.04
Asian 15 14673 OR (M-H, Fixed, 95% CI) 1.42 [1.31, 1.54] < 0.00001 2% 0.43
Caucasian 12 18824 OR (M-H, Fixed, 95% CI) 1.56 [1.45, 1.68] < 0.00001 0% 0.73
HB 12 10192 OR (M-H, Fixed, 95% CI) 1.45 [1.31, 1.60] < 0.00001 0% 0.69
PB 16 34101 OR (M-H, Fixed, 95% CI) 1.50 [1.43, 1.58] < 0.00001 32% 0.11
rs2420946 (C>T) 11 8925 OR (M-H, Fixed, 95% CI) 1.52 [1.39, 1.66] < 0.00001 0% 0.54
Asian 8 3629 OR (M-H, Fixed, 95% CI) 1.40 [1.21, 1.62] < 0.00001 0% 0.57
Caucasian 3 5296 OR (M-H, Fixed, 95% CI) 1.60 [1.43, 1.79] < 0.00001 0% 0.56
HB 6 3303 OR (M-H, Fixed, 95% CI) 1.43 [1.22, 1.66] < 0.00001 0% 0.53
PB 5 5622 OR (M-H, Fixed, 95% CI) 1.57 [1.41, 1.76] < 0.00001 0% 0.47
rs2981578 (A>G) 3 957 OR (M-H, Fixed, 95% CI) 1.80 [1.36, 2.39] < 0.0001 0% 0.8
Heterozygote genetic model
rs2981582 (C>T) 31 114046 OR (M-H, Random, 95% CI) 1.22 [1.17, 1.27] < 0.00001 42% 0.007
Asian 15 23025 OR (M-H, Fixed, 95% CI) 1.18 [1.12, 1.25] < 0.00001 1% 0.44
Caucasian 12 30051 OR (M-H, Fixed, 95% CI) 1.26 [1.19, 1.33] < 0.00001 26% 0.19
HB 12 15893 OR (M-H, Fixed, 95% CI) 1.23 [1.15, 1.31] < 0.00001 0% 0.75
PB 16 54285 OR (M-H, Random, 95% CI) 1.23 [1.15, 1.31] < 0.00001 52% 0.009
rs2420946 (C>T) 11 14127 OR (M-H, Fixed, 95% CI) 1.21 [1.13, 1.29] < 0.00001 0% 0.69
Asian 8 5852 OR (M-H, Fixed, 95% CI) 1.21 [1.08, 1.34] 0.0005 0% 0.62
Caucasian 3 8275 OR (M-H, Fixed, 95% CI) 1.21 [1.11, 1.32] < 0.0001 0% 0.37
HB 6 5348 OR (M-H, Fixed, 95% CI) 1.23 [1.10, 1.38] 0.0002 0% 0.66
PB 5 8779 OR (M-H, Fixed, 95% CI) 1.19 [1.09, 1.30] < 0.0001 0% 0.42
rs2981578 (A>G) 3 1199 OR (M-H, Fixed, 95% CI) 1.65 [1.26, 2.16] 0.0003 0% 0.51
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CI: Confidence interval.

Figure 2. Forest plots of rs2981582 (C>T) polymorphism and breast cancer risk stratified by ethnicity (Recessive model TT vs. CC + TC).

Figure 2

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Figure 3. Forest plots of rs2981582 (C>T) polymorphism and breast cancer risk stratified by Source of controls (Recessive model TT vs. CC + TC).

Figure 3

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For rs2420946, 11 studies with 7,840 cases and 9,349 controls were included to assess the association. As shown in Table 3, Figure 4 and Figure 5, the pooled ORs suggested that rs2420946 was significantly associated with BC susceptibility in all the five genetic models: Allele model 1.23 (95% CI: 1.18-1.29; P< 0.00001), Dominant model 1.28 (95% CI: 1.20-1.37; P< 0.00001), Recessive model 1.36 (95% CI: 1.26-1.48; P< 0.00001), Homozygous genetic model 1.52 (95% CI: 1.39-1.66; P< 0.00001), Heterozygote comparison 1.21 (95% CI: 1.13-1.29; P< 0.00001). When stratified by Ethnicity and Source of controls, the results showed that FGFR2 rs2420946 was significantly associated with BC risk in Asians, Caucasians, HB and PB.

Figure 4. Forest plots of rs2420946 (C>T) polymorphism and breast cancer risk stratified by ethnicity (Dominant model TC + TT vs. CC).

Figure 4

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Figure 5. Forest plots of rs2420946 (C>T) polymorphism and breast cancer risk stratified by Source of controls (Dominant model TC + TT vs. CC).

Figure 5

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3 papers with 833 cases and 1012 controls were adopted to evaluate the association between the rs2981578 polymorphism and the BC risk. As shown in Table 3, Figure 6, the association between rs2981578 variant and BC susceptibility was also significant in any genetic model (Allele model: OR= 1.29, 95% CI= 1.13-1.47, P= 0.0002; Dominant model: OR= 1.71, 95% CI= 1.32-2.21, P< 0.0001; Recessive model: OR= 1.24, 95% CI= 1.02-1.50, P= 0.03; Homozygous genetic model: OR= 1.80, 95% CI= 1.36-2.39, P< 0.0001; Heterozygote comparison: OR= 1.65, 95% CI= 1.26-2.16, P= 0.0003).

Figure 6. Forest plots of rs2981578 (A>G) polymorphism and breast cancer risk (Allele model G vs. A).

Figure 6

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Sensitivity analyses

As shown in Table 1, all the studies conformed to the balance of HWE in controls except Chen’s(2012), Gorodnova’s(2012), Ren’s(2012), Zhao’s(2012) studies(P<0.05) in rs2981582 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 were less than 0.1 (p ≤ 0.1), otherwise, fixed-effect models were selected (Table 3).

Publication bias

As Figure 7 indicated, the symmetrical funnel plot indicated that there is no significant publication bias in the total population. We use Begg's funnel plot and Egger test to evaluate the published bias, no significant publication bias was found in the Begg's test and Egger's test (P>0.05).

Figure 7. Funnel plot assessing evidence of publication bias.

Figure 7

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A. rs2981582 (C>T) (Recessive model TT vs. CC + TC). B. rs2420946 (C>T) (Dominant model TC + TT vs. CC). C. rs2981578 (A>G) (Allele model G vs. A). SE: standard error; OR: odds ratio.

DISCUSSION

FGFR2 has been proved to be associated with many diseases, especially the relationship between FGFR2 and cancer, which has become a hot research topic in recent years [16]. GWAS analysis revealed that FGFR2 gene was one of the BC susceptibility genes. There are 8 SNPs(rs35054928, rs2981578, rs2912778, rs2912781, rs35393331, rsl0736303, rs7895676, rs33971856) in its second intron and the SNPs of FGFR2 have become the hotspot in BC susceptibility gene study [17–19]. But the difference of SNPs allele frequency and LD structure reflects the difference of the genetic variation in the race, so the occurrence and characteristics of BC were different. Therefore, a variation in one study does not have the same risk impact on other crowds. This requires repeated studies on previously related locis in multiple populations worldwide.

Lots of researches have reported the association between FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphism and BC risk. However, due to differences in ethnic and regional and other factors, the conclusions of related reports are still inconclusive. Thus, we conducted the meta-analysis to evaluate the relationship between FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphism and BC risk.

In our study, there were 31 studies with 54,677 cases and 80,418 controls for FGFR2 rs2981582 variants. In the total population, the pooled results indicated that the correlation between FGFR2 rs2981582 polymorphism and the occurrence of BC was significant in any genetic model. Furthermore, in ethnicity-specific analysis, FGFR2 rs2981582 were also significantly associated with BC risk both in Asians and Caucasians. We didn't discuss the African subgroup for just one study was from African. The analysis in different source of controls showed the same association between FGFR2 rs2981582 polymorphism and BC susceptibility both in HB and PB, indicating that both hospital populations and general populations followed the same relationship. For rs2420946, 11 studies with 7,840 cases and 9,349 controls were included to assess the association. In the total population, the pooled ORs suggested that rs2420946 was significantly associated with BC susceptibility in all the five genetic models. When stratified by ethnicity and source of controls, the results showed the same association in Asians, Caucasians, hospital populations and general populations, indicating that different genetic backgrounds and living environment were not strong enough to change these associations. All the results for the two variants (rs2981582, rs2420946) were partially consistent with the consequences of Wang's [13], Peng's [14], Zhang's [12] and Jia's [15] meta-analysis, while they didn't conduct analysis in different source of controls, making our results more valuable. Furthermore, they didn't use all the five genetic models(allele model, dominant model, recessive model, homozygous model and heterozygous model) to assess the strength of association. Wang's [13] study also reported that the association appeared to be much stronger for estrogen receptor-positive and progesterone receptor-positive BC, which was not analyzed in our study. Peng's [14] study was conducted on the base of present mata-ananlyses, which may missed some individual studies with larger sample sizes, and this type meta-analysis may not appropriate. In Zhang's [12] study, the increased risk was found in the subgroup of postmenopausal women for rs2420946. However, only one study [20] reported that risk in premenopausal women. For Jia's [15] study, in the ethnicity subgroup, using Non-Caucasians represent different ethnicities may cause some heterogeneity.

Three articles with 833 cases and 1012 controls were adopted to evaluate the association between the rs2981578 polymorphism and the BC risk. As the preceding two variants, the association between rs2981578 variant and BC susceptibility was also significant in any genetic model. For just only 3 studies, no stratified study was conducted for rs2981578 polymorphism. However, in Zhou's [11] meta-analysis, they found that rs2981578 polymorphism might decrease BC risk. This may result from the literature selection bias. While the sample size of our study for rs2981578 was so small, data from a large sample of multiple centers is still needed to assess the association.

Our meta-analysis has several limitations. First, 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 cannot analyze the potential interaction of gene-environment and gene-gene. Second, only published papers were included in our meta-analysis, there still may be some unpublished studies which are in line with the conditions. Therefore, publication bias may exist even no statistical evidence was found in the meta-analysis. Third, for just only 3 papers, no stratified study was conducted for rs2981578 polymorphism. Moreover, our study is a summary of the data. We did not verify it from the level of molecular mechanism. Data from large scale multicenter epidemiological studies is still needed to confirm the relationship between FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphisms and BC risk, and the molecular mechanism for the associations need to be elucidated further.

In conclusion, our meta-analysis based on case-control studies provides strong evidence that FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphisms are significantly associated with the BC risk. For rs2981582 and rs2420946, the association remained significant in Asians, Caucasians, general populations and hospital 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 further.

MATERIALS AND METHODS

Literature search

We searched PubMed, Web of science and the Cochrane Library for relevant studies published before October 11, 2015. The following keywords were used: (Fibroblast Growth Factor Receptor 2 or 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 (rs2981582, rs2420946 and rs2981578) polymorphism and BC risk. The reference lists of all articles were also manually screened for potential studies. Abstracts and citations were screened by two researchers independently. All the eligible articles need a second screening for the full-text. The searching was done without language limitations.

Selection and exclusion criteria

Inclusion criteria: A study was included in this meta-analysis if it met the following criteria: i)independent case-control studies for humans; ii) the study evaluating the association between FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphism and BC risk; iii) the study presenting available genotype frequencies in cancer cases and control subjects for risk estimated; iiii) 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 were checked and calculated twice according to the inclusion criteria listed above by two 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 Hardy-Weinberg equilibrium(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 χ2 test in the control group of each study [21]. The strength of associations between the FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphism and BC risk were measured by odds ratio (ORs) with 95% confidence interval (CIs). Z test was used to assess the significance of the ORs, I2 and Q statistics was used to determine the statistical heterogeneity among studies. A random-effect model was used if p value of heterogeneity tests was no more than 0.1 (p ≤ 0.1), and otherwise, a fixed-effect model was selected [21, 22]. Sensitivity analyses were performed to assess the stability of the results. We used Begg's funnel plot and Egger's test to evaluate the publication bias [23, 24]. 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< 0.05 was considered statistically significant. We performed subgroup according to Ethnicity and Source of controls.

Footnotes

CONFLICTS OF INTEREST

The authors have declared that no conflicts of interest exists.

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