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. 2020 Oct 23;40(10):BSR20192051. doi: 10.1042/BSR20192051

An updated meta-analysis of the association between fibroblast growth factor receptor 4 polymorphisms and susceptibility to cancer

Abdolkarim Moazeni-Roodi 1,2, Sahel Sarabandi 3, Shima Karami 3, Mohammad Hashemi 3,4,*, Saeid Ghavami 5,6,✉
PMCID: PMC7584815  PMID: 33017009

Abstract

Fibroblast growth factor receptor 4 (FGFR4) is a cell surface receptor tyrosine kinases (RTKs) for FGFs.

Several studies have focused on the association between FGFR4 polymorphisms and cancer development. This meta-analysis aimed to estimate the association between FGFR4 rs351855 (Gly388Arg), rs1966265 (Val10Ile), rs7708357, rs2011077, and rs376618 polymorphisms and cancer risk. Eligible studies were identified from electronic databases. All statistical analyses were achieved with the STATA 14.0 software. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were used to quantitatively estimate the association. Overall, no significant association was found among rs351855, rs2011077, and rs376618 polymorphisms with the risk of overall cancer. The rs1966265 polymorphism significantly decreased the risk of cancer in recessive (OR = 0.87, 95% CI = 0.78–0.97, P=0.009, TT vs CT+CC) genetic model. Whereas the rs7708357 polymorphism was positively associated with cancer risk in dominant (OR = 1.17, 95% CI = 1.02–1.36, P=0.028) genetic model. Stratified analysis revealed that rs351855 variant significantly increased the risk of prostate cancer in heterozygous (OR = 1.16, 95% CI = 1.02–1.32, P=0.025 AG vs GG), dominant (OR = 1.20, 95% CI = 1.06–1.35, P=0.004, AG+AA vs GG), and allele (OR = 1.22, 95% CI = 1.06–1.41, P=0.005, A vs G) genetic models.

In summary, the findings of this meta-analysis indicate that rs1966265, rs7708357, and rs351855 polymorphisms are correlated to cancer development. Further well-designed studies are necessary to draw more precise conclusions.

Keywords: Cancer, FGFR4, Meta-analysis, Polymorphism, Susceptibility

Introduction

Cancer poses a major health problem in both developing and developed countries [1–3]. There were approximately 18.1 million new cases and 9.6 million cancer deaths in 2018 [4]. The exact mechanism of cancer development is not clear yet. Mounting evidence have indicated that cancer development and progression is influenced by environmental and genetic factors [3,5–7].

The human fibroblast growth factor receptors (FGFRs), a subfamily of cell surface receptor tyrosine kinases (RTKs), consist of four closely related family members (FGFR1–4) [8]. FGFR activation by a various fibroblast growth factors (FGFs) triggers a cascade that leads to the activation of multiple signal transduction pathways, including the Ras/Raf/MapK, PI3K/Akt, STAT, and PLCγ, which can promote cell survival, cell proliferation, tissue development, differentiation, angiogenesis, epithelial-to-mesenchymal transition (EMT), angiogenesis, and can thereby involve in carcinogenesis [9–11].

The human FGFR4 gene, also termed as cluster of differentiation 334 (CD334), is mapped to chromosome 5 (5q 35.1) [12] and is highly polymorphic. A common nonsynonymous single nucleotide polymorphism (SNP) at codon 388 (rs351855 G>A) in exon 9, which results in a change of glycine to arginine (Gly388Arg), was recognized in the transmembrane domain of the EGFR4 receptor [13]. Several studies inspected the relationship between FGFR4 gene rs351855 G>A polymorphism and numerous types of cancer including breast cancer [13–18], cervical cancer [19–21], colon cancer [13,18,22], gastric cancer [23], prostate cancer [24–27], head and neck squamous cell carcinoma (HNSCC) [28,29], oral squamous cell carcinoma (OSCC) [30,31], lung cancer [32–34], hepatocellular carcinoma [35–37], sarcoma [38], skin cancer [39], neuroblastoma [40], non-Hodgkin’s lymphoma [41], and glioma [42]. There are few direct reports about the effect of FGFR4 polymorphism on the gene expression. FGFR4 rs351855 polymorphism induced higher expression of FGFR4 protein and worse prognosis in breast cancer [43]. It has been reported that the rate of degradation of the Arg388 receptor was slower than the Gly388 receptor in neuroblastoma cells and also initiated internalization of the receptor into multivesicular structures (Rev1-1) [40]. In another investigation, the researchers showed that expression of the FGFR4 Arg388 protein activated the extracellular signal-related kinase pathway with subsequent expression of several genes which were associated with the aggressive form of prostate cancer [44], (Rev1-1). Researchers have reported that there was not any significant difference between different genotypes of FGFR4 in gastric cancer [45]. Interestingly in the lung normal tissue, genotype-dependent transcriptional profile is present [46]. In the past few years, there were few epidemiological analysis and meta-analysis focusing on FGFR4 in uterine leiomyomata [47], hip bone geometry [48–50], and all types of cancer [31,51]. Our current meta-analysis covers Gly388Arg rs351855 G>A and Val10Ile rs1966265 polymorphism in FGFR4 polymorphisms to cancer susceptibility and provide wider information in this important regulator of cancers (Rev 1-2).

Methods

Literature search and inclusion criteria

We performed a literature research for all eligible articles regarding the association between FGFR4 polymorphisms on multiple electronic databases including Web of Science, PubMed, Scopus, and Google Scholar databases through using the following terms: ‘FGFR4 OR CD334’ AND ‘polymorphism OR, SNP, OR variation OR mutation’ AND ‘cancer OR carcinoma OR neoplasm OR tumor’ up to 10 May 2020. Besides, we also screened references of the included studies. Figure 1 shows the process of studies selection. Relevant studies included the meta-analysis if they met the following inclusion criteria: (1) original case–control studies addressing the correlation between FGFR4 polymorphisms; (2) studies containing sufficient genotype data in both cases and controls; (3) the largest sample sizes were selected when repeatedly published articles by the same team. The exclusion criteria were: (1) conference abstract, case reports, reviews, duplication data; (2) insufficient genotype data provided.

Figure 1. Flow chart illustrates the detailed study selection process of this meta-analysis.

Figure 1

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Data extraction

Two investigators independently screened the literature and extracted data from eligible studies according to exclusion and inclusion criteria. The following data were collected from each study including the first author’s name, publication year, country, ethnicity of participants, cancer type, genotyping methods, the sample size, and the genotype and allele frequencies of cases and controls (Table 1).

Table 1. Characteristics of the studies eligible for meta-analysis.

First author Year Country Ethnicity Type of cancer Source of control Genotyping method Case/control Cases Controls HWE Score
Gly388Arg rs351855 G>A GG AG AA G A GG AG AA G A
Ansell 2009 Sweden Caucasian HNSCC PB PCR-RFLP 110/192 61 49 - - 81 111 - - - 9
Bange 2002 Russia Caucasian Breast PB PCR-RFLP 61/123 26 28 7 80 42 55 60 8 170 76 0.114 7
Bange 2002 Germany Caucasian Breast PB PCR-RFLP 84/123 41 34 9 116 52 55 60 8 170 76 0.114 8
Bange 2002 Italy Caucasian Colon cancer PB PCR-RFLP 82/123 37 38 7 112 52 55 60 8 170 76 0.114 8
Batschauer 2011 Brazil Caucasian Breast PB PCR-RFLP 68/85 39 26 3 104 32 47 35 3 129 41 0.249 9
Chen 2018 Taiwan Asian Cervical cancer HB TaqMan 226/335 69 101 56 239 213 96 165 74 357 313 0.845 9
Chou 2017 Taiwan Asian OSCC PB TaqMan 955/1191 225 524 206 974 936 334 596 261 1264 1118 0.873 11
Fang 2013 China Asian NSCLC HB Sequencing 629/729 193 331 105 717 541 163 391 175 717 741 0.049 9
FitzGerald 2009 U.S.A. Caucasian Prostate PB SNPlex 1254/1251 587 544 123 1718 790 631 496 124 1758 744 0.070 15
FitzGerald 2009 U.S.A. African Prostate PB SNPlex 146/80 104 39 3 247 45 60 18 2 138 22 0.646 13
Gao 2014 China Asian NHL NA PCR-RFLP 421/486 117 189 115 423 419 171 240 75 582 390 0.541 11
Heinzle 2012 Austria Caucasian Colon cancer PB TaqMan 85/1660 42 33 10 117 53 802 723 135 2327 993 0.114 10
Ho 2009 Singapore Asian HCC PB Sequencing 58/88 27 17 14 71 45 30 38 20 98 78 0.241 6
Ho 2010 U.K. Caucasian Prostate PB TaqMan 397/291 183 182 32 548 246 150 117 24 417 165 0.860 11
Hosseini 2017 Iran Asian Breast Cancer PB PCR-RFLP 126/160 87 33 6 207 45 54 57 49 165 155 <0.001 6
Jiang 2015 China Asian Breast cancer NA Snapshot 747/716 205 404 138 814 680 270 348 98 888 544 0.398 12
Li 2017 China Asian Cervical Cancer HB PCR-RFLP 162/162 35 79 48 149 175 50 72 40 172 152 0.170 8
Ma 2008 Japan Asian Prostate HB PCR-RFLP 492/179 163 196 133 522 462 67 87 25 221 137 0.701 10
Mawrin 2006 Germany Caucasian Glioma HB PCR-RFLP 94/25 39 51 4 129 59 10 13 2 33 17 0.428 6
Morimoto 2003 Japan Asian Sarcomas NA PCR-RFLP 143/102 54 72 17 180 106 39 50 13 128 76 0.624 10
Naidu 2009 Malaysia Asian Breast HB PCR-RFLP 387/252 179 172 36 530 244 132 105 15 369 135 0.322 9
Nan 2009 U.S.A. Caucasian Skin cancer PB TaqMan 768/833 365 325 78 1055 481 406 343 84 1155 511 0.359 11
Shen 2013 China Asian Gastric cancer PB Sequencing 304/392 118 124 62 360 248 132 188 72 452 332 0.724 11
Sheu 2015 China Asian HCC HB TaqMan 289/595 82 150 57 314 264 159 314 122 632 558 0.146 8
Spinola 2005 Italy Caucasian Lung HB Pyrosequencing 274/401 148 104 22 400 148 193 168 40 554 248 0.699 9
Spinola 2005 Italy Caucasian Breast HB Pyrosequencing 142/220 67 55 20 189 95 112 83 25 307 133 0.117 8
Spinola 2005 Italy Caucasian CRC HB Pyrosequencing 179/220 98 63 18 259 99 112 83 25 307 133 0.117 8
Tanuma 2010 Japan Asian OSCC HB PCR-SSCP 150/100 69 53 28 191 109 42 48 10 132 68 0.487 8
Tsay 2020 Taiwan Asian Cervical cancer HB TaqMan 428/856 114 222 92 450 406 242 426 188 910 802 0.984 10
Ture 2015 Turkey Asian Lung cancer HB PCR-RFLP 124/100 66 47 11 179 69 48 46 6 142 58 0.242 7
Wang 2004 U.S.A. Caucasian Prostate PB PCR-RFLP 284/97 125 117 42 367 201 53 40 4 146 48 0.291 8
Wang 2004 U.S.A. African Prostate PB PCR-RFLP 45/94 37 6 2 80 10 76 18 0 170 18 0.305 7
Whittle 2016 U.S.A. Caucasian Neuroblastoma NA PCR-RFLP 126/114 45 69 12 159 93 50 60 4 160 68 0.006 9
Wimmer 2019 Germany Caucasian HNSCC PB PCR-RFLP 284/123 188 84 12 460 108 55 60 8 170 76 0.114 9
Yang 2012 China Asian HCC HB TaqMan 711/740 216 351 144 783 639 247 361 132 855 625 0.996 10
First author Year Country Ethnicity Type of Cancer Source of control Genotyping method Case/control Cases Controls HWE Score
Val10Ile rs1966265 CC CT TT C T CC CT TT C T
Chen 2018 Taiwan Asian Uterine Cervical HB TaqMan 227/335 61 105 61 227 227 91 168 76 350 320 0.927 9
Chou 2017 Taiwan Asian OSCC PB TaqMan 955/1191 213 514 228 940 970 285 580 326 1150 1232 0.391 11
FitzGerald 2009 U.S.A. Caucasian Prostate PB SNPlex 1259/1254 782 405 72 1969 549 742 447 65 1931 577 0.827 15
FitzGerald 2009 U.S.A. African Prostate PB SNPlex 147/80 132 15 0 279 15 70 10 0 150 10 0.551 13
Jiang 2015 China Asian Breast NA Snapshot 747/716 171 408 168 750 744 126 364 226 616 816 0.322 12
Nan 2009 U.S.A. Caucasian Skin cancer PB TaqMan 753/821 461 251 41 1173 333 507 271 43 1285 357 0.390 11
Sheu 2015 China Asian HCC HB TaqMan 289/595 65 160 64 290 288 151 300 144 602 588 0.835 8
Tsay 2020 Taiwan Asian Cervical cancer HB TaqMan 428/856 95 226 107 416 440 215 420 221 850 862 0.585 10
First author Year Country Ethnicity Type of cancer Source of control Genotyping method Case/control Cases Controls HWE Score
rs7708357 GG GA AA G A GG GA AA G A
Chen 2018 Taiwan Asian Uterine cervical HB TaqMan 227/335 222 4 1 448 6 321 13 1 655 15 0.038 9
Chou 2017 Taiwan Asian OSCC PB TaqMan 955/1191 932 22 1 1886 24 1167 23 1 2357 25 0.015 11
FitzGerald 2009 U.S.A. Caucasian Prostate PB SNPlex 1258/1254 459 632 167 1550 966 507 569 178 1583 925 0.368 15
FitzGerald 2009 U.S.A. African Prostate PB SNPlex 146/78 49 74 23 172 120 34 35 9 103 53 0.999 13
Sheu 2015 China Asian HCC HB TaqMan 289/595 283 5 1 571 7 577 18 0 1172 18 0.708 8
Tsay 2020 Taiwan Asian Cervical cancer HB TaqMan 428/856 416 10 2 842 14 838 17 1 1693 19 0.005 9
First author Year Country Ethnicity Type of cancer Source of control Genotyping method Case/control Cases Controls HWE Score
rs2011077 CC CT TT C T CC CT TT C T
Chen 2018 Taiwan Asian UT-cervical HB TaqMan 227/335 63 102 62 228 226 94 163 78 351 319 0.652 9
Chou 2017 Taiwan Asian OSCC PB TaqMan 955/1191 210 509 236 929 981 288 577 326 1153 1229 0.299 11
Ma 2008 Japan Asian Prostate HB PCR-RFLP 492/179 94 285 113 473 511 11 85 83 107 251 0.075 10
Sheu 2015 China Asian HCC HB TaqMan 289/595 66 159 64 291 287 147 297 151 591 599 0.968 8
Tsay 2020 Taiwan Asian Cervical cancer HB TaqMan 428/856 94 224 110 412 444 217 418 221 852 860 0.495 10
First author Year Country Ethnicity Type of cancer Source of control Genotyping method Case/control Cases Controls HWE Score
rs376618 AA AG GG A G AA AG GG A G
FitzGerald 2009 U.S.A. Caucasian Prostate PB SNPlex 1238/1245 703 448 87 1854 622 712 437 96 1861 629 0.013 15
FitzGerald 2009 U.S.A. African Prostate PB SNPlex 145/80 65 59 21 189 101 38 38 4 114 46 0.154 13
Nan 2009 U.S.A. Caucasian Skin cancer PB TaqMan 762/830 451 273 38 1175 349 468 326 36 1262 398 0.026 11
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Quality assessment

Two investigators evaluated the quality of each study using the quality assessment criteria [52]. Quality scores of studies ranged from 0 (lowest) to 15 (highest). Studies with scores ≤9 were considered as low quality, while those with scores > 9 were considered as high quality.

Statistical analysis

Meta-analysis was carried out using STATA 14.0 software (Stata Corporation, College Station, TX, U.S.A.). The Hardy–Weinberg equilibrium (HWE) of control genotypes was determined by the chi-square test.

The strength of the association between FGFR4 polymorphisms and cancer susceptibility was evaluated by pooled odds ratios (ORs) and their 95% confidence intervals (CIs) in five (heterozygous, homozygous, dominant, recessive, and allele) genetic models. The significance of the pooled OR was assessed by the Z-test, and P<0.05 was considered to be statistically significant. The between-study heterogeneity was evaluated by the Q statistic. When the PQ < 0.1, indicating the presence of heterogeneity, the random-effects model was selected, otherwise, the fixed-effects model was chosen.

Publication bias was inspected by using Begg’s funnel plots and the asymmetric plots implied potential publication bias. Egger’s test was used to measure the degree of asymmetry. A P<0.05 indicated significant publication bias.

Sensitivity analyses was done to evaluate whether a single study influenced the overall pooled results by omitting each study in turn.

Results

Study characteristics

A total of 57 case–control studies from 30 published articles [13–42] that met the inclusion criteria were included in our meta‐analyses. Of these 57 studies, the FGFR4 rs351855 in 35 studies, rs1966265 in 8 studies, rs7708357 in 6 studies, rs2011077 in 5 studies, and rs376618 in 3 studies were analyzed, respectively. The characteristics and relevant data of the included studies are presented in Table 1.

Meta-analysis results

The findings did not support an association between FGFR4 rs351855 polymorphism and overall cancer susceptibility in heterozygous (OR = 0.97, 95% CI = 0.87–1.07, P=0.514, AG vs GG), homozygous (OR = 1.14, 95% CI = 0.95–1.37, P=0.166, AA vs GG), dominant (OR = 0.98, 95% CI = 0.87–1.10, P=0.686, AG+AA vs GG), recessive (OR = 1.15, 95% CI = 0.98–1.33, P=0.79, AA vs AG+GG), and allele (OR = 1.02, 95% CI = 0.93–1.12, P=0.663, A vs G) genetic models (Figure 2 and Table 2). Stratified analysis was achieved by ethnicity and cancer type (Table 3 and Figure 3). The results indicated that rs351855 variant significantly increased the risk of prostate cancer in heterozygous (OR = 1.16, 95% CI = 1.02–1.32, P=0.025 AG vs GG), dominant (OR = 1.20, 95% CI = 1.06–1.35, P=0.004, AG+AA vs GG), and allele (OR = 1.22, 95% CI = 1.06–1.41, P=0.005, A vs G) genetic models.

Figure 2. Forest plot for the association of the FGFR4 rs351855 polymorphism with overall cancer susceptibility in codominant (AG+AA vs GG).

Figure 2

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Table 2. The pooled ORs and 95% CIs for the association between FGFR4 polymorphisms and cancer susceptibility.

n Genetic model Association test Heterogeneity test Egger’s test P Begg’s test P
OR (95% CI) Z P χ2 I2 (%) P
Overall
rs351855 G>A 35 AG vs GG 0.97 (0.87–1.07) 0.65 0.514 82.87 60.2 <0.0001 0.012 0.064
AA vs GG 1.14 (0.95–1.37) 1.39 0.166 116.25 71.6 <0.0001 0.966 0.975
AG+AA vs GG 0.98 (0.87–1.10) 0.40 0.686 122.33 72.2 <0.0001 0.061 0.129
AA vs AG+GG 1.15 (0.98–1.33) 1.76 0.79 94.88 65.2 <0.0001 0.476 0.306
A vs G 1.02 (0.93–1.12) 0.47 0.639 150.59 78.1 <0.0001 0.416 0.293
rs1966265 C>T 8 CT vs CC 1.01 (0.89–1.14) 0.14 0.891 11.12 37.1 0.133 0.739 1.000
TT vs CC 0.94 (0.77–1.16) 0.56 0.574 14.60 58.9 0.024 0.373 0.176
CT+TT vs CC 0.98 (0.87–1.11) 0.31 0.759 11.52 39.2 0.118 0.810 0.805
TT vs CT+CC 0.87 (0.78–0.97) 2.61 0.009 14.07 57.3 0.029 0.094 0.051
T vs C 0.95 (0.87–1.04) 1.03 0.303 14.24 50.8 0.047 0.722 0.805
rs7708357 G>A 6 AG vs GG 1.17 (0.95–1.44) 1.45 0.146 5.61 10.9 0.346 0.221 0.039
AA vs GG 1.10 (0.87–1.40) 0.83 0.406 3.61 0.0 0.607 0.143 1.000
AG+AA vs GG 1.17 (1.02–1.36) 2.19 0.028 4.72 0.0 0.451 0.467 0.091
AA vs AG+GG 0.98 (0.79–1.21) 0.20 0.840 3.77 0.0 0.583 0.097 0.624
A vs G 1.08 (0.98–1.20) 1.51 0.132 4.22 0.0 0.518 0.964 0.348
rs2011077 C>T 5 CT vs CC 1.03 (0.79–1.33) 0.21 0.831 11.30 64.6 0.023 0.054 0.014
TT vs CC 0.79 (0.49–1.25) 1.02 0.309 28.54 86.0 <0.0001 0.228 0.327
CT+TT vs CC 0.94 (0.69–1.28) 0.39 0.695 17.85 77.6 0.001 0.091 0.050
TT vs CT+CC 0.79 (0.56–1.13) 1.27 0.203 28.84 86.1 <0.0001 0.681 1.000
T vs C 0.89 (0.70–1.13) 0.97 0.332 33.89 88.2 <0.0001 0.380 0.327
rs376618 A>G 3 AG vs AA 0.95 (0.85–1.09) 0.56 0.753 1.76 0.0 0.414 0.761 0.602
GG vs AA 1.04 (0.81–1.33) 0.29 0.771 4.12 51.5 0127 0.067 0.117
AG+GG vs AA 0.97 (0.76–1.10) 0.45 0.654 1.27 0.0 0.531 0.858 0.602
GG vs AG+AA 1.19 (0.74–1.93) 0.71 0.476 5.04 60.3 0.080 0.014 0.117
G vs A 0.99 (0.90–1.09) 0.20 0.841 2.21 9.5 0.331 0.383 0.602
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Table 3. Stratified analysis of rs351855 polymorphisms by ethnicity and cancer type.

n Genetic model Association test Heterogeneity test Egger’s test P Begg’s test P
OR (95% CI) Z P χ2 I2 (%) P
Caucasian 15 AG vs GG 0.97 (0.84–1.12) 0.42 0.672 26.56 47.3 0.002 0.162 0.586
AA vs GG 1.11 (0.95–1.29) 1.30 0.193 19.36 27.7 0.152 0.331 0.216
AG+AA vs GG 0.96 (0.83–1.12) 0.47 0.636 35.15 57.3 0.002 0.257 0.471
AA vs AG+GG 1.09 (0.94–1.26) 1.09 0.278 16.49 15.1 0.284 0.118 0.125
A vs G 1.03 (0.92–1.15) 0.43 0.666 30.34 53.9 0.007 0.789 0.458
Asian 17 AG vs GG 0.96 (0.82–1.12) 0.56 0.572 55.29 71.1 0.000 0.023 0.039
AA vs GG 1.1 (0.84–1.44) 0.72 0.470 94.65 83.1 0.000 0.636 0.510
AG+AA vs GG 0.98 (0.81–1.17) 0.27 0.786 86.32 81.5 0.000 0.092 0.070
AA vs AG+GG 1.13 (0.91–1.40) 1.12 0.262 76.08 79.0 0.000 0.832 0.458
A vs G 1.01 (0.87–1.16) 0.11 0.913 119.83 86.6 0.000 0.352 0.217
Breast cancer 7 AG vs GG 0.94 (0.66–1.33) 0.35 0.729 26.00 76.9 0.000 0.050 0.099
AA vs GG 1.03 (0.48–2.22) 0.08 0.939 44.25 86.4 0.000 0.358 0.186
AG+AA vs GG 0.91 (0.58–1.44) 0.40 0.691 50.88 88.2 0.000 0.135 0.099
AA vs AG+GG 1.05 (0.56–1.96) 0.16 0.877 31.50 81.0 0.000 0.540 0.176
A vs G 0.92 (0.61–1.38) 0.40 0.690 71.30 91.6 0.000 0.233 0.072
Prostate cancer 6 AG vs GG 1.16 (1.02–1.32) 2.25 0.025 2.91 0.0 0.714 0.422 0.188
AA vs GG 1.60 (0.98–2.61) 1.90 0.058 13.39 62.7 0.020 0.378 0.462
AG+AA vs GG 1.20 (1.06–1.35) 2.89 0.004 1.67 0.0 0.892 0.639 0.851
AA vs AG+GG 1.56 (0.92–2.65) 1.63 0.103 17.29 71.1 0.004 0.452 0.624
A vs G 1.22 (1.06–1.41) 2.81 0.005 7.55 33.8 0.183 0.279 0.260
Gastrointestinal cancer 7 AG vs GG 0.92 (0.80–1.06) 1.17 0.243 6.73 10.9 0.346 0.071 0.090
AA vs GG 1.06 (0.88–1.28) 0.63 0.528 3.72 0.0 0.715 0.581 0.881
AG+AA vs GG 0.95 (0.84–1.09) 0.70 0.487 6.14 2.2 0.408 0.093 0.652
AA vs AG+GG 1.10 (0.94–1.30) 1.17 0.241 2.34 0.0 0.886 0.824 0.762
A vs G 1.01 (0.92–1.10) 0.16 0.873 4.37 0.0 0.627 0.172 0.230
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Bold values denote statistical significance at the P <0.05 level.

Figure 3. Forest plot for the association of the FGFR4 rs351855 polymorphism with prostate cancer susceptibility (A vs G).

Figure 3

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For FGFR4 rs1966265 polymorphism, the findings revealed that this variant significantly reduced the risk of cancer susceptibility in recessive (OR = 0.87, 95% CI = 0.78–0.97, P=0.009, TT vs CT+CC) model (Table 2 and Figure 4).

Figure 4. Forest plot for the association between FGFR4 rs1966265 and overall cancer risk in recessive (TT vs CT+CC) models.

Figure 4

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The rs7708357 variant of FGFR4 significantly increased the risk of cancer development in dominant (OR = 1.17, 95% CI = 1.02–1.36, P=0.028, AG+AA GG) genetic model (Table 2 and Figure 5).

Figure 5. Forest plot for the association between FGFR4 rs7708357 and overall cancer risk in dominant model (AG+AA vs GG).

Figure 5

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The rs2011077 and rs376618 variants were not associated with overall cancer risk in any genetic models tested (Table 2).

Heterogeneity and publication bias

As shown in Table 2, heterogeneity among the studies was observed in all genetic comparisons for rs351855 and rs2011077. For rs1966265, heterogeneity was not found in heterozygous and dominant genetic models. While, heterogeneity was not detected in all genetic models for rs7708357 and rs376618.

The potential publication bias was evaluated using Begg’s funnel plot and Egger’s test. The shape of funnel plots was symmetrical and the Egger’s test supported no existence of publication bias in all comparison except rs351855 polymorphism in heterozygous and rs376618 polymorphism in recessive genetic model (Table 2 and Figure 6).

Figure 6. Begg’s funnel plot for the test of publication bias for FGFR4 rs351855 in recessive model (AA vs AG+GG).

Figure 6

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Sensitivity analysis

We performed sensitivity analysis to assess the effect of a specific publication on the overall estimate. For rs351855, the pooled ORs showed no significant change appeared when each study was neglected, one at a time, in heterozygous, dominant, and allele genetic models (Figure 7). For rs1966265, sensitivity analysis indicated no changes of results in heterozygous, homozygous, dominant, recessive, and allele genetic models. For rs7708357, no alterations of results were detected in homozygous, recessive, and allele genetic models. Thus, the final pooled results are both stable and reliable.

Figure 7. Sensitivity analysis on the association between the rs351855 polymorphism and susceptibility of overall cancer in allele genetic model (A vs G).

Figure 7

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Discussion

FGFs and their receptors (FGFRs) regulate numerous cellular processes including the regulation of cell proliferation, differentiation, migration, and metabolism [12]. Deregulation of FGFRs signaling have been found to play an important role in cancer development and progression as well as resistance to anticancer [53–55]. Overexpression of FGFR4 predict metastasis and poor survival outcome in various cancers [56–58]. Blocking FGFR4 significantly suppresses the cancer and indicates that FGFR4 is a potential target for the cancer treatment [59]. Polymorphisms in the FGFR4 rs351855 (Gly388Arg) polymorphism, is positioned in the transmembrane domain of the EGFR4. It has been found that Arg388 allele causes increased receptor stability and prolonged receptor activation [60].

Several reports have examined the relationship between FGFR4 gene polymorphisms and diverse cancer types [13–20,22–42]. However, the findings were inconsistent. Therefore, this updated meta-analysis including more eligible studies was performed to evaluate the impact of FGFR4 polymorphisms on cancer susceptibility. For FGFR4 rs351855 polymorphism, the findings from 34 studies including 10407 cases and 12382 controls did not support an association between this polymorphism and overall cancer susceptibility. Stratified analyses showed that this SNP significantly increased the risk of prostate cancer (n=6) in heterozygous, homozygous, dominant, and allele genetic models. The variant was not related to breast cancer as well as gastrointestinal cancer. Furthermore, the variant was not correlated with ethnicity. A meta-analysis performed by Xiong et al. [51] from 27 studies indicated a significant association between FGFR4 rs351855 polymorphism and overall cancer risk in recessive genetic model. Stratified analysis showed that rs351855 SNP significantly increased the risk of prostate cancer. A meta-analysis performed by Shu et al. [61] on 14 studies investigated the association between FGFR4 rs351855 polymorphism and various cancer risks indicated a significant association between this SNP and risk of overall cancer in all heterozygous, homozygous, dominant, recessive, and allele tested genetic models.

FGFR4 rs1966265 changes chemotherapy response in breast cancer [62], higher risk of oral squamous cell carcinoma susceptibility [31], initiation of cervical cancer (Taiwanese women) [19], and higher risk of breast cancer in Chinese women of Heilongjiang province [16]. FGFR4 rs2011077 TC+CC polymorphism is associated with higher tumor stage, tumor size, and grading in urothelial cell carcinoma [21]. FGFR4 rs2011077 with the GG genotype also increased the risk of prostate cancer in Japanese population [26].

To the best of our knowledge, for the first time, we performed pooled analysis to inspect the impact of rs1966265, rs7708357, rs2011077, and rs376618 polymorphisms and overall cancer risk.

For FGFR4 rs1966265 polymorphism, the findings revealed that this variant significantly reduced the risk of cancer susceptibility in recessive (OR = 0.87, 95% CI = 0.78–0.97, P=0.009, TT vs CT+CC) model (Table 2 and Figure 3). Regarding rs7708357 polymorphism, the finding indicated that the rs1966265 variant significantly increased the risk of overall cancer in dominant (OR = 1.17, 95% CI = 1.02–1.36, P=0.028, AG+AA GG) genetic model (Table 2 and Figure 4). While, the rs2011077 and rs376618 polymorphisms were not associated with cancer risk in any genetic models tested (Table 2).

Some limitations of this meta-analysis should be taken into account. First, the sample sizes of this meta-analysis were not large especially for rs1966265 (n=7 studies), rs7708357 (n=5 studies), rs2011077 (n=4 studies), and rs376618 (n=3 studies) polymorphisms as well as in stratified analyses, which may lead to reduced statistical power. Second, the strength of the association were measured by unadjusted ORs for confounding factors due to the lack of demographic and environmental factors, which might have affected the results. Third, publication bias may be unavoidable since we were only able to acquire data from published articles. Finally, the meta-analysis was associated with a significant heterogeneity in some polymorphisms.

The current investigation provided a source for basic medical scientist and clinician to understand the importance of FGFR4 in different types of cancers and use the results as potential biomarkers for susceptibility to cancers. It also provided a collection of previous investigation on this gene to help epidemiologist scientists for their future investigations (Rev 1-4).

In summary, this meta-analysis revealed that FGFR4 rs351855 (Gly388Arg) polymorphism might be a marker for susceptibility to prostate cancer. The rs1966265 polymorphism significantly decreased and rs1966265 polymorphism significantly increased the risk of overall cancer. No significant associations were found for the FGFR4 rs2011077 and rs376618 polymorphisms. However, these findings need to be further confirmed through large samples and different ethnic populations.

Acknowledgements

We would like to dedicate this article to Professor Mohammad Hashemi who passed away recently after the submission of this work. He was a pioneer in genetic studies.

Abbreviations

CD334

cluster of differentiation 334

CI

confidence interval

EGFR

epidermal growth factor receptor

EMT

epithelial-to-mesenchymal transition

FGF

fibroblast growth factor

FGFR

FGF receptor

HNSCC

head and neck squamous cell carcinoma

MAPK

mitogen-activated protein kinase

OR

odds ratio

PI3K

Phosphoinositide 3-kinases

PLCγ

phospholipase C gamma

RTK

receptor tyrosine kinases

SNP

single nucleotide polymorphism

STAT

signal transducer and activator of transcription

Competing Interests

The authors declare that there are no competing interests associated with the manuscript.

Funding

The authors declare that there are no sources of funding to be acknowledged.

Author Contribution

Abdolkarim Moazeni-Roodi did the analysis, participated in revision and updating the manuscript. Sahel Sarabandi and Shima Karami did the literature review and helped in data analysis. Mohammad Hashemi prepared the final draft of manuscript and supervised the analysis. Saeid Ghavami prepared the final edit of manuscript, supervised the whole project and led the revision after passing away of Professor Mohammad Hashemi.

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