Skip to main content
NCBI home page
Journal of Cancer logoLink to Journal of Cancer
. 2019 Feb 26;10(6):1538–1549. doi: 10.7150/jca.28137

Genetic Association between Interleukin-4 Receptor Polymorphisms and Cancer Susceptibility: A Meta-Analysis Based on 53 Case-Control Studies

Yong Qi 1,✉, Taofei Zeng 2, Song Fan 2, Li Zhang 2, Chaozhao Liang 2
PMCID: PMC6485229  PMID: 31031864

Abstract

Polymorphisms in interleukin-4 receptor (IL-4R) gene have been reported susceptible to a variety of cancer types, nevertheless, data from these publications remained inconsistent and controversial. We further performed a comprehensive meta-analysis to present a precise estimation of its relationship. Extensive retrieve was performed in PubMed, Google Scholar and Web of Science up to May 25, 2018. Odds ratios (ORs) and 95% confidence intervals (CIs) were conducted to evaluate the overall strength of the associations in five genetic models, as well as in subgroup analyses, stratified by ethnicity, cancer type or source of control. Q-test, Egger's test and Begg's funnel plot were applied to evaluate the heterogeneity and publication bias. In-silico analysis was managed to demonstrate the relationship of IL-4R expression correlated with cancer tissues. Finally, 31 publications including 53 case-control studies were enrolled, with 24,452 cases and 24,971 controls. After a comprehensive analysis, no significant evidence was revealed for the association between four IL-4R polymorphisms (rs1801275, rs1805010, rs1805015, rs2057768) and cancer susceptibility in the overall population, as well as the subgroup analysis stratified by ethnicity, cancer type, the genotyping method or the source of control. To sum up, no evidence was identified between IL-4R polymorphisms and overall cancer susceptibility. Further well-designed studies with large sample sizes will be continued on this issue of interest.

Keywords: meta-analysis, polymorphism, cytokines, interleukin-4 receptor, cancer susceptibility

Introduction

Cancer has been regarded as one of the most frequent causes of death in economically developing and developed countries. According to the 2018 updated global estimation, there are approximately 42 million people across the world suffered from any type of cancer. Including 8 million had breast cancer, 6.3 million had colon and rectum cancer, 5.7 million with prostate cancer and over 2.8 million suffered from respiratory cancer1. Another worldwide result conducted by GLOBOCAN represented that there are about 12.7 million new cancer cases and 7.6 million deaths had occurred in 2012, suggested that cancer has become a primary public health threat2. It is established that cancers were multifactorial diseases which commonly arose from these intricate interactions between genetic and environmental factors3.

For the past few years, numerous epidemiologic studies have uncovered that single nucleotide polymorphisms (SNPs) in the cytokine family may contribute to the tumorigenesis of many cancers in several ways, such as, influencing the function of cytokines participated in immune reactions and inflammatory responses, affecting the binding of nuclear factors with targeted genes, and inhibiting apoptosis 4. Interleukin-4 (IL-4) is a member of the α-helical cytokine family, which is generated by activated CD4+T cells, basophils, and mast cells, regarding as the central differentiation factor managing Th2 development, removing extracellular pathogens, and inhibiting Th1 differentiation. In addition, IL-4 receptor (IL-4R) is a heterodimeric complex that can bind to the Th2 cytokines IL-4 and IL-135, 6. Overexpression of IL-4R has been observed in colorectal carcinoma 6. In addition, polymorphisms in IL-4R were identified implicated in the tumorigenesis of a variety of cancer types, including pancreatic cancer, renal cell carcinoma, bladder cancer and cervical cancer 7-10. For example, Schwartzbaum et al. reported an increased susceptibility of glioblastoma contributed by rs1801275 of IL-4R 11, however, Li et al. indicated that the mutant G allele plays a protective function in tumorigenesis 12. The inconsistent might cause by the differences of genotyping methods, source of control, and ethnic lines, as well as the small-scale sample size. Therefore, we conducted a comprehendsive meta-analysis to explore the association between IL-4R polymorphisms and cancer susceptibility.

Material and Methods

Literature search

All eligible publications up to May 25, 2018 were retrieved and extracted by investigators from the databases of PubMed, Google Scholar, Web of Science, CNKI and Wanfang databases, respectively. When discrepancies occurred in data interpretation, we will deal with them by discussing, review of the publications, and counseling other cancer research experts if necessary. The keywords applied for literature retrieve are as follows: (“IL-4R,” OR “Interleukin-4 receptor,” OR “IL4R,”) AND (“SNP,” OR “mutation,” OR “variant,” OR “polymorphism,”) AND (“cancer,” OR “carcinoma,” OR “tumor,” OR “malignancy,” OR “leukemia” OR “lymphoma”). In addition, we conducted a manual retrieve for the additional eligible studies from the studies cited in the reference lists.

Inclusion criteria and exclusion criteria

The publications enrolled in our studies should keep to the following inclusion criteria: 1) publications should illustrate the association between the polymorphisms in IL-4R and cancer susceptibility; 2) The detail genotype frequency of the cases and controls could be obtained directly or indirectly through calculating; 3) case-control studies. However, publications should be removed when they were: 1) no control studies, meta-analysis or systematic review, comments, and case report; 2) no efficient data of the genotype frequency offered; 3) repetitive publications; 4) the publications were conducted on animals or cell lines; 5) they were concerned about other disorders instead of cancers.

Data extraction

Two investigators extracted data from the enrolled case-control studies individually. The following details were collected from each study: the name of the first author, the date of publication, ethnicity, sample size, genotyping method, and genotype frequency of the cases and controls. By comparing enrolled forms between two investigators, the accuracy of the data was verified. If any difference was generated, we would check the full-text of the articles.

Statistical analysis

We applied ORs with corresponding 95% CIs to assess the strength of the relationship between the polymorphisms in IL-4R and overall cancer susceptibility. Five common genetic models applied for assessing gene-disease associations are allele contrast model (B vs. A), heterozygote model (BA vs. AA), homozygote model (BB vs. AA), recessive model (BB vs. BA+AA) and dominant model (BA+BB vs. AA) (AA, homozygotes for the wild allele; AB, heterozygotes; BB, homozygotes for the mutant allele). Bonferroni corrections were also performed to adjust the results, Padjust = 0.05* number of calculated polymorphism * 5 models, and less than 0.05 was considered as statistically significant13. In addition, we applied the chi-squared (χ2)-based Q test to calculate between-study heterogeneity14. P<0.1 was indicated a substantial level of heterogeneity, and a random effects model (the DerSimonian and Laird method) was selected to pool the data 15; or else, the fixed effects model (the Mantel-Haenszel method) was adopted. Stratified analyses were also calculated by ethnicity, cancer type, the genotyping method and the source of control. Moreover, we also conducted the Begg's funnel plots and Egger's test to evaluate the publication bias 16, 17. Hardy-Weinberg equilibrium (HWE) of controls was calculated by the χ2 test. We applied STATA 12.0 (Version 12.0, Stata Corp) to conduct all the statistical analyses, and all the P values were two-sided.

Linkage Disequilibrium (LD) Analysis and in-silico analysis of IL-4R expression

Data were extracted from the 1000 genomes project comprising the polymorphisms in genes of IL-4R in the current study. CHB (Han Chinese in Beijing, China), CHS (southern Han Chinese, China), CEU (Utah residents with Northern and Western European ancestry from the CEPH collection), JPT (Japanese in Tokyo, Japan) and YRI (Yoruba in Ibadan, Nigeria), ESN (Esan in Nigeria) were enrolled in the calculate project, analyses were performed with Haploview software, LD in each above-mentioned population was assessed by r2 statistics.

In order to further explore the relationship between IL-4R expression and cancer, we used a newly developed interactive web server, GEPIA (http://gepia.cancer-pku.cn/), which provided the RNA sequencing expression data of tumors and normal samples from the TCGA and the GTEx projects18.

Results

Study characteristics

A total of 31 publications including 53 case-control studies satisfied the inclusion criteria, including 24452 cancer patients and 24971 controls focused on rs1801275, rs1805010, rs1805015 and rs2057768, while another 5 polymorphisms (rs1805011, rs1805012, rs1805013, rs1805016, rs3024536) were finally exceeded because of less than 3 studies. We provide a flowchart to show the details of the data selection process (Figure 1). There were 26 case-control studies of the rs1801275 polymorphism5, 8, 9, 11, 12, 19-37, 15 of the rs1805010 polymorphism5, 9, 20, 23-26, 28, 32, 33, 37-40, 9 of the rs1805015 polymorphism9, 11, 12, 23, 24, 32, 33, 38, 41, and 3 of the rs2057768 polymorphism42-44. 19 studies were performed in Asians, 29 in Caucasians, 2 in the mixed group (more than two descendant), and 1 in African group. The characteristics of each case-control study, genotype frequencies and HWE examination results were presented in Table 1. Six case-control studies were not comforted to HWE11, 24, 26, 34, 35, 40, and we further conducted a sensitive analysis to validate the influence of the three studies on the integrated data. In order to evaluate the quality of each enrolled studies, we applied Newcastle-Ottawa Scale (NOS) 45, and filled the result in Table S1, the result of PRISMA2009 checklist was also listed to present our meta-analysis work (Table S2).

Figure 1.

Figure 1

Open in a new tab

Flow chart presenting the study selection procedure.

Table 1.

Details of enrolled studies for current meta-analysis and systematic review

SNP First author Year Ethnicity Genotyping Method Source of Control Cancer Type case control
PAA PAB PBB HAA HAB HBB HWE
rs1801275 Calhoun et al.19 2002 Caucasian PCR PB CC 78 45 4 60 41 7 Y
rs1801275 Nakamura et al.5 2002 Asian PCR-RFLP HB RC 98 40 5 161 42 2 Y
rs1801275 Wu et al.20 2003 Asian PCR HB GC 160 57 3 164 61 5 Y
rs1801275 Schwartzbaum et al.11 2005 Caucasian PCR-RFLP PB HL 53 45 11 243 236 24 N
rs1801275 Balasubramanian et al.21 2006 Caucasian Taq-Man PB BC 493 249 33 451 288 28 N
rs1801275 Brenner et al.22 2007 Caucasian PCR HB Glioma 407 214 28 651 331 44 Y
rs1801275 Ivansson et al.23 2007 Caucasian Taq-Man PB CC 766 462 66 163 100 23 Y
rs1801275 Landi et al.24 2007 Caucasian Taq-Man HB CRC 183 87 14 332 180 24 Y
rs1801275 Olson et al.8 2007 Caucasian PCR-RFLP PB PC 104 38 7 89 41 5 Y
rs1801275 Wiemels et al.38 2007 Caucasian PCR-RFLP PB Glioma 243 126 15 303 144 22 Y
rs1801275 Gu et al.25 2008 Caucasian PCR PB Melanoma 120 64 11 121 65 7 Y
rs1801275 Yang et al.27 2008 Mixed PCR-RFLP PB UBC 406 193 29 374 229 22 Y
rs1801275 Zambon et al.26 2008 Caucasian Taq-Man HB GC 17 7 0 29 15 1 Y
rs1801275 Lee et al.29 2010 Asian PCR-SSP HB CRC 137 29 4 84 43 4 Y
rs1801275 Mohan et al.28 2009 Caucasian PCR-RFLP PB RC 37 9 4 31 17 3 Y
rs1801275 Scola et al.30 2010 Caucasian PCR-RFLP HB PC 32 11 15 79 48 4 Y
rs1801275 Chu et al.32 2012 Asian Taq-Man HB UBC 559 227 26 793 314 30 Y
rs1801275 Ruan et al.31 2011 Asian PCR HB Glioma 462 196 14 466 205 25 Y
rs1801275 Chu et al.9 2012 Asian Taq-Man HB RC 407 195 18 424 176 23 Y
rs1801275 Li et al.12 2012 Asian PCR PB Glioma 161 62 2 157 88 5 Y
rs1801275 Ingram et al.33 2013 Mixed Taq-Man PB CRC 847 524 77 364 190 23 Y
rs1801275 Jin et al.34 2013 Asian PCR PB Glioma 56 14 2 187 105 6 N
rs1801275 Quan et al.35 2014 Caucasian PCR PB BC 642 288 47 660 261 32 Y
rs1801275 Quan et al.35 2014 African PCR PB BC 296 490 800 360 522 847 N
rs1801275 Sousa et al.36 2015 Caucasian Taq-Man PB NPC 159 63 16 436 212 39 Y
rs1801275 Liang et al.37 2017 Asian PCR-LDR HB RC 84 44 4 100 43 2 Y
rs1805010 Nakamura et al.5 2002 Asian PCR-RFLP HB RC 42 76 25 84 94 27 Y
rs1805010 Wu et al.20 2003 Asian PCR HB GC 51 120 49 52 119 59 Y
rs1805010 Ivansson et al.23 2007 Caucasian Taq-Man HB CC 365 653 267 99 147 38 Y
rs1805010 Landi et al.24 2007 Caucasian Taq-Man HB CRC 83 141 55 162 262 102 Y
rs1805010 Wiemels et al.38 2007 Caucasian PCR-RFLP HB Glioma 119 196 72 148 232 91 Y
rs1805010 Gu et al.25 2008 Caucasian PCR PB Melanoma 67 104 43 57 110 50 Y
rs1805010 Zambon et al.26 2008 Caucasian Taq-Man HB GC 4 9 10 19 15 11 N
rs1805010 Crusius et al.42 2008 Caucasian PCR PB GC 71 134 39 352 549 249 Y
rs1805010 Mohan et al.28 2009 Caucasian PCR-RFLP PB RC 20 12 18 23 22 6 Y
rs1805010 Ando et al.39 2009 Asian PCR-RFLP HB GC 137 156 37 77 85 28 Y
rs1805010 Chu et al.9 2012 Asian Taq-Man PB UBC 213 399 205 305 557 278 Y
rs1805010 Wang et al.51 2012 Asian PCR-RFLP PB HL 185 98 51 190 96 48 N
rs1805010 Chu et al.32 2012 Asian Taq-Man PB RC 219 268 133 168 310 145 Y
rs1805010 Ingram et al.33 2013 Mixed Taq-Man PB CRC 428 700 280 162 295 106 Y
rs1805010 Liang et al.37 2017 Asian PCR-LDR HB RC 26 76 30 36 78 31 Y
rs1805015 Schwartzbaum et al.11 2005 Caucasian PCR-RFLP PB Glioma 64 40 4 288 107 16 Y
rs1805015 Ivansson et al.23 2007 Caucasian Taq-Man HB CC 871 379 44 176 94 16 Y
rs1805015 Landi et al.24 2007 Caucasian Taq-Man HB CRC 201 73 7 362 164 6 N
rs1805015 Wiemels et al.38 2007 Caucasian PCR-RFLP HB Glioma 274 99 13 341 113 16 Y
rs1805015 Chu et al.9 2012 Asian Taq-Man PB UBC 673 141 2 951 182 7 Y
rs1805015 Chu et al.32 2012 Asian Taq-Man PB RC 519 100 1 527 90 6 Y
rs1805015 Li et al.12 2012 Asian PCR PB Glioma 196 30 0 207 42 4 Y
rs1805015 Ingram et al.33 2013 Mixed Taq-Man PB CRC 951 442 54 400 158 18 Y
rs1805015 Shamran et al.41 2014 Caucasian PCR-RFLP PB Glioma 70 25 5 17 15 8 Y
rs2057768 Crusius et al.42 2008 Caucasian PCR PB GC 108 116 11 583 433 91 Y
rs2057768 Wilkening et al.43 2008 Caucasian Taq-Man PB CRC 150 139 18 296 238 47 Y
rs2057768 Burada et al.44 2012 Caucasian Taq-Man HB GC 53 40 12 144 85 13 Y
Open in a new tab

CRC: Colorectal cancer; GC: Gastric cancer; BC: Breast cancer; UBC: Bladder cancer; CC: Cervical cancer; PC: Pancreatic cancer; RC: Renal cancer; NPC: nasopharyngeal carcinoma; HL: Hodgkin's lymphoma; H-B: Hospital based; P-B: Population based; HWE: Hardy Weinberg Equilibrium

Quantitative data synthesis

The summary of the meta-analysis between the IL-4R polymorphisms and cancer susceptibility was shown in Table 2. After complicated calculation, we revealed that there is no significant association between rs1801275, rs1805010, rs1805015, and rs2057768 polymorphisms and cancer susceptibility in the overall population (Figure 2, Figure S1-S3). In the subgroup analyses stratified by ethnicity, cancer type, the genotyping method or the source of control, the homozygote model of cervical cancer in rs1801275 shown a decreased risk (BB vs. AA: OR (95% CI) = 0.581(0.364-0.925), PH=0.022), while shown an increased risk in breast cancer (BB vs. AA: OR (95% CI) = 1.181(1.006-1.386), PH=0.043), the recessive model of cervical cancer in rs1801275 also shown a decreased risk (BB vs. BA+AA: OR (95% CI) = 0.59(0.374-0.931), PH=0.024). For rs1805010, several significant risk were shown in HB subgroup of heterozygote model (BA vs. AA: OR (95% CI) = 1.151(1.001-1.323), PH=0.049), HB subgroup of dominant model (BA+BB vs. AA: OR (95% CI) = 1.167(1.022- 1.332), PH=0.023), PCR subgroup of recessive model (BB vs. BA+ AA: OR (95% CI) = 0.767 (0.604- 0.974), PH=0.03), gastric cancer subgroup of recessive model (BB vs. BA+AA: OR (95% CI) = 0.785 (0.617-0.998), PH=0.048). For rs1805015, the significant risk was shown in Asian population subgroup of homozygote model (BB vs. AA: OR (95% CI) = 0.24(0.076- 0.752), PH=0.014), Asian population subgroup of recessive model (BB vs. BA+AA: OR (95% CI) = 0.239(0.076-0.749), PH=0.014). For rs2057768, the subgroup of PB and gastric cancer shown statistical difference in heterozygote model (BA vs. AA), recessive model (BB vs. BA+AA) or dominant model (BA+BB vs. AA). However, after Bonferroni corrections, all the Padjust value is higher than 0.05. The results revealed that there is no significant association between IL-4R polymorphisms and cancer risks in stratification analyses.

Table 2.

Results of meta-analysis for polymorphisms in and cancer susceptibility.

Polymorphisms Comparision Subgroup N PH PZ Padjust Random Fixed
rs1801275 B VS A Overall 26 0.001 0.588 1.000 0.980(0.910-1.055) 1.007(0.963-1.053)
B VS A Caucasian 14 0.089 0.983 1.000 0.998(0.909-1.095) 0.999(0.933-1.070)
B VS A Asian 9 0.003 0.187 1.000 0.889(0.747-1.059) 0.939(0.857-1.029)
B VS A Mixed 2 0.028 0.808 1.000 1.035(0.783-1.369) 1.054(0.929-1.194)
B VS A PB 15 0.019 0.546 1.000 0.974(0.893-1.062) 1.010(0.956-1.067)
B VS A HB 11 0.003 0.983 1.000 0.998(0.866-1.151) 1.000(0.925-1.082)
B VS A CC 2 0.627 0.088 1.000 0.850(0.704-1.026) 0.849(0.704-1.025)
B VS A RC 4 0.055 0.522 1.000 1.036(0.752-1.426) 1.055(0.895-1.243)
B VS A GC 2 0.672 0.501 1.000 0.890(0.631-1.254) 0.733(0.281-1.914)
B VS A BC 3 0.044 0.537 1.000 1.047(0.904-1.213) 1.060(0.981-1.144)
B VS A Glioma 5 0.119 0.146 1.000 0.898(0.768-1.048) 0.925(0.832-1.028)
B VS A CRC 3 0.002 0.497 1.000 0.874(0.591-1.291) 1.036(0.909-1.182)
B VS A PC 2 0.015 0.462 1.000 1.340(0.615-2.922) 1.277(0.928-1.757)
B VS A UBC 2 0.209 0.761 1.000 0.977(0.832-1.147) 0.980(0.863-1.114)
B VS A PCR 9 0.03 0.613 1.000 0.971(0.866-1.089) 1.024(0.959-1.094)
B VS A PCR-RFLP 7 0.013 0.338 1.000 1.111(0.895-1.379) 1.040(0.925-1.171)
B VS A Taq-Man 8 0.222 0.969 1.000 0.992(0.908-1.084) 0.999(0.928-1.075)
B VS A Y 22 0.002 0.658 1.000 0.981(0.901-1.068) 1.002(0.950-1.057)
B VS A N 4 0.035 0.695 1.000 0.963(0.799-1.162) 1.018(0.937-1.106)
BA VS AA Overall 26 0.003 0.181 1.000 0.941(0.860-1.029) 0.971(0.916-1.030)
BA VS AA Caucasian 14 0.4 0.174 1.000 0.940(0.860-1.029) 0.942(0.865-1.027)
BA VS AA Asian 9 0.002 0.362 1.000 0.907(0.735-1.119) 0.963(0.865-1.073)
BA VS AA Mixed 2 0.008 0.86 1.000 0.963(0.636-1.458) 0.989(0.847-1.154)
BA VS AA PB 15 0.009 0.142 1.000 0.914(0.812-1.031) 0.955(0.886-1.030)
BA VS AA HB 11 0.05 0.751 1.000 0.977(0.847-1.127) 0.998(0.908-1.097)
BA VS AA CC 2 0.623 0.699 1.000 0.953(0.746-1.217) 0.953(0.746-1.217)
BA VS AA RC 4 0.143 0.127 1.000 1.146(0.831-1.579) 1.166(0.957-1.419)
BA VS AA GC 2 0.754 0.734 1.000 0.935(0.631-1.384) 0.934(0.631-1.383)
BA VS AA BC 3 0.02 0.927 1.000 1.011(0.801-1.275) 1.019(0.906-1.145)
BA VS AA Glioma 5 0.047 0.297 1.000 0.892(0.719-1.106) 0.942(0.829-1.070)
BA VS AA CRC 3 0.001 0.378 1.000 0.801(0.490-1.312) 0.990(0.841-1.165)
BA VS AA PC 2 0.478 0.12 1.000 0.713(0.462-1.101) 0.710(0.461-1.093)
BA VS AA UBC 2 0.08 0.242 1.000 0.899(0.684-1.180) 0.912(0.782-1.064)
BA VS AA PCR 9 0.092 0.856 1.000 0.973(0.852-1.110) 1.009(0.919-1.107)
BA VS AA PCR-RFLP 7 0.071 0.127 1.000 0.889(0.703-1.124) 0.891(0.768-1.034)
BA VS AA Taq-Man 8 0.145 0.746 1.000 0.978(0.868-1.101) 0.985(0.899-1.079)
BA VS AA Y 22 0.022 0.378 1.000 0.959(0.873-1.053) 0.985(0.923-1.052)
BA VS AA N 4 0.009 0.258 1.000 0.838(0.616-1.139) 0.918(0.803-1.048)
BB VS AA Overall 26 0.046 0.233 1.000 1.110(0.935-1.319) 1.127(1.006-1.261)
BB VS AA Caucasian 14 0.019 0.276 1.000 1.172(0.881-1.561) 1.145(0.951-1.377)
BB VS AA Asian 9 0.317 0.527 1.000 0.903(0.634-1.285) 0.907(0.672-1.226)
BB VS AA Mixed 2 0.656 0.114 1.000 1.341(0.928-1.938) 1.345(0.931-1.941)
BB VS AA PB 11 0.336 0.048 1.000 1.131(0.966-1.323) 1.138(1.001-1.295)
BB VS AA HB 15 0.014 0.485 1.000 1.158(0.767-1.748) 1.088(0.860-1.377)
BB VS AA CC 2 0.638 0.022 0.440 0.584(0.366-0.933) 0.581(0.364-0.925)
BB VS AA RC 4 0.254 0.657 1.000 1.299(0.629-2.684) 1.123(0.673-1.873)
BB VS AA GC 2 0.96 0.457 1.000 0.606(0.161-2.275) 0.605(0.161-2.271)
BB VS AA BC 3 0.524 0.043 0.860 1.180(1.005-1.386) 1.181(1.006-1.386)
BB VS AA Glioma 5 0.579 0.212 1.000 0.819(0.590-1.136) 0.812(0.586-1.126)
BB VS AA CRC 3 0.466 0.259 1.000 1.231(0.842-1.798) 1.240(0.853-1.803)
BB VS AA PC 2 0.016 0.239 1.000 3.333(0.449-24.745) 3.452(1.564-7.617)
BB VS AA UBC 2 0.975 0.315 1.000 1.222(0.827-1.807) 1.222(0.827-1.807)
BB VS AA PCR 9 0.225 0.212 1.000 1.039(0.815-1.324) 1.100(0.947-1.277)
BB VS AA PCR-RFLP 7 0.021 0.052 1.000 1.765(0.995-3.130) 1.551(1.124-2.140)
BB VS AA Taq-Man 8 0.409 0.747 1.000 1.022(0.826-1.263) 1.035(0.840-1.275)
BB VS AA Y 22 0.027 0.516 1.000 1.076(0.862-1.343) 1.092(0.936-1.273)
BB VS AA N 4 0.504 0.066 1.000 1.173(0.993-1.386) 1.169(0.990-1.382)
BA+BB VS AA Overall 26 0.003 0.382 1.000 0.963(0.884-1.048) 0.990(0.936-1.047)
BA+BB VS AA Caucasian 14 0.524 0.427 1.000 0.968(0.891-1.050) 0.967(0.891-1.050)
BA+BB VS AA Asian 9 0.001 0.364 1.000 0.908(0.736-1.119) 0.958(0.863-1.063)
BA+BB VS AA Mixed 2 0.01 0.993 1.000 0.998(0.676-1.474) 1.023(0.881-1.187)
BA+BB VS AA PB 11 0.008 0.249 1.000 0.935(0.835-1.048) 0.980(0.913-1.052)
BA+BB VS AA HB 15 0.052 0.877 1.000 1.001(0.873-1.148) 1.007(0.920-1.103)
BA+BB VS AA CC 2 0.611 0.308 1.000 0.887(0.703-1.118) 0.886(0.703-1.118)
BA+BB VS AA RC 4 0.136 0.115 1.000 1.171(0.857-1.599) 1.165(0.964-1.409)
BA+BB VS AA GC 2 0.705 0.611 1.000 0.906(0.617-1.330) 0.905(0.616-1.329)
BA+BB VS AA BC 3 0.017 0.746 1.000 1.037(0.833-1.292) 1.051(0.943-1.171)
BA+BB VS AA Glioma 5 0.057 0.222 1.000 0.881(0.719-1.078) 0.926(0.819-1.047)
BA+BB VS AA CRC 3 0.001 0.428 1.000 0.822(0.506-1.334) 1.014(0.867-1.185)
BA+BB VS AA PC 2 0.341 0.893 1.000 0.974(0.659-1.438) 0.974(0.659-1.437)
BA+BB VS AA UBC 2 0.107 0.414 1.000 0.929(0.729-1.183) 0.940(0.810-1.090)
BA+BB VS AA PCR 9 0.038 0.61 1.000 0.965(0.839-1.108) 1.018(0.932-1.111)
BA+BB VS AA PCR-RFLP 7 0.116 0.557 1.000 0.987(0.804-1.212) 0.959(0.832-1.104)
BA+BB VS AA Taq-Man 8 0.165 0.84 1.000 0.983(0.880-1.099) 0.991(0.908-1.081)
BA+BB VS AA Y 22 0.019 0.583 1.000 0.975(0.891-1.067) 0.997(0.936-1.062)
BA+BB VS AA N 4 0.009 0.393 1.000 0.883(0.664-1.175) 0.963(0.852-1.088)
BB VS BA+AA Overall 26 0.026 0.183 1.000 1.124(0.946-1.335) 1.094(0.991-1.207)
BB VS BA+AA Caucasian 14 0.007 0.185 1.000 1.226(0.908-1.655) 1.175(0.979-1.411)
BB VS BA+AA Asian 9 0.393 0.545 1.000 0.911(0.661-1.257) 0.912(0.676-1.230)
BB VS BA+AA Mixed 2 0.959 0.113 1.000 1.342(0.932-1.932) 1.342(0.933-1.932)
BB VS BA+AA PB 11 0.297 0.111 1.000 1.116(0.956-1.302) 1.093(0.980-1.218)
BB VS BA+AA HB 15 0.007 0.419 1.000 1.193(0.778-1.830) 1.101(0.873-1.390)
BB VS BA+AA CC 2 0.695 0.024 0.480 0.593(0.375-0.938) 0.590(0.374-0.931)
BB VS BA+AA RC 4 0.268 0.727 1.000 1.264(0.626-2.554) 1.095(0.659-1.820)
BB VS BA+AA GC 2 0.988 0.476 1.000 0.619(0.166-2.315) 0.619(0.166-2.314)
BB VS BA+AA BC 3 0.414 0.168 1.000 1.093(0.963-1.240) 1.093(0.963-1.240)
BB VS BA+AA Glioma 5 0.594 0.22 1.000 0.823(0.595-1.139) 0.817(0.591-1.129)
BB VS BA+AA CRC 3 0.71 0.282 1.000 1.221(0.839-1.776) 1.226(0.846-1.778)
BB VS BA+AA PC 2 0.01 0.218 1.000 3.776(0.456-31.263) 3.895(1.787-8.491)
BB VS BA+AA UBC 2 0.833 0.227 1.000 1.270(0.861-1.871) 1.270(0.862-1.871)
BB VS BA+AA PCR 9 0.325 0.452 1.000 1.030(0.850-1.248) 1.047(0.928-1.182)
BB VS BA+AA PCR-RFLP 7 0.01 0.035 1.000 1.910(1.047-3.483) 1.648(1.201-2.261)
BB VS BA+AA Taq-Man 8 0.401 0.7 1.000 1.029(0.833-1.271) 1.041(0.847-1.281)
BB VS BA+AA Y 22 0.019 0.422 1.000 1.096(0.876-1.372) 1.101(0.946-1.281)
BB VS BA+AA N 4 0.271 0.196 1.000 1.179(0.908-1.530) 1.089(0.957-1.240)
rs1805010 B VS A Overall 15 0.002 0.468 1.000 1.034(0.944-1.132) 1.012(0.959-1.068)
B VS A Asian 7 0.051 0.609 1.000 0.998(0.883-1.126) 0.980(0.908-1.059)
B VS A Caucasian 7 0.004 0.259 1.000 1.107(0.928-1.322) 1.068(0.977-1.168)
B VS A PCR-RFLP 5 0.078 0.251 1.000 1.109(0.926-1.328) 1.071(0.953-1.203)
B VS A PCR 3 0.885 0.139 1.000 0.903(0.788-1.034) 0.903(0.788-1.034)
B VS A Taq-Man 6 0.001 0.498 1.000 1.055(0.903-1.234) 1.018(0.950-1.091)
B VS A HB 8 0.025 0.133 1.000 1.114(0.968-1.282) 1.103(1.011-1.203)
B VS A PB 7 0.058 0.253 1.000 0.964(0.867-1.071) 0.961(0.898-1.029)
B VS A RC 4 0.001 0.358 1.000 1.180(0.829-1.678) 0.974(0.859-1.105)
B VS A GC 4 0.083 0.434 1.000 0.987(0.793-1.229) 0.949(0.831-1.083)
B VS A CRC 2 0.758 0.989 1.000 1.001(0.892-1.123) 1.001(0.892-1.123)
B VS A Y 13 0.005 0.704 1.000 1.018(0.929-1.115) 1.005(0.951-1.062)
B VS A N 2 0.033 0.329 1.000 1.493(0.667-3.343) 1.149(0.919-1.436)
BB VS AA Overall 15 0.004 0.521 1.000 1.061(0.885-1.271) 1.024(0.919-1.142)
BB VS AA Asian 7 0.108 0.573 1.000 0.976(0.781-1.220) 0.957(0.821-1.116)
BB VS AA Caucasian 7 0.003 0.282 1.000 1.227(0.845-1.780) 1.140(0.949-1.369)
BB VS AA PCR-RFLP 5 0.069 0.363 1.000 1.192(0.820-1.733) 1.115(0.882-1.411)
BB VS AA PCR 3 0.931 0.089 1.000 0.782(0.589-1.039) 0.782(0.589-1.038)
BB VS AA Taq-Man 6 0.002 0.433 1.000 1.127(0.836-1.520) 1.051(0.915-1.208)
BB VS AA HB 8 0.027 0.172 1.000 1.226(0.915-1.643) 1.210(1.009-1.451)
BB VS AA PB 7 0.068 0.315 1.000 0.933(0.758-1.148) 0.933(0.814-1.069)
BB VS AA RC 4 0.003 0.324 1.000 1.407(0.714-2.773) 0.966(0.752-1.241)
BB VS AA GC 4 0.124 0.26 1.000 0.902(0.592-1.376) 0.852(0.645-1.126)
BB VS AA CRC 2 0.844 0.894 1.000 1.016(0.801-1.289) 1.016(0.801-1.289)
BB VS AA Y 13 0.006 0.734 1.000 1.033(0.857-1.244) 1.009(0.902-1.130)
BB VS AA N 2 0.062 0.286 1.000 1.848(0.498-6.859) 1.255(0.827-1.904)
BA VS AA Overall 15 0.061 0.987 1.000 1.019(0.904-1.148) 1.001(0.917-1.092)
BA VS AA Asian 7 0.025 0.838 1.000 1.022(0.828-1.262) 0.971(0.856-1.100)
BA VS AA Caucasian 7 0.413 0.25 1.000 1.087(0.940-1.256) 1.088(0.943-1.255)
BA VS AA PCR-RFLP 5 0.384 0.343 1.000 1.090(0.907-1.311) 1.090(0.912-1.303)
BA VS AA PCR 3 0.339 0.678 1.000 1.043(0.824-1.321) 1.049(0.838-1.312)
BA VS AA Taq-Man 6 0.018 0.74 1.000 0.966(0.788-1.184) 0.945(0.844-1.058)
BA VS AA HB 8 0.637 0.049 0.980 1.150(0.999-1.322) 1.151(1.001-1.323)
BA VS AA PB 7 0.073 0.123 1.000 0.913(0.773-1.078) 0.916(0.820-1.024)
BA VS AA RC 4 0.004 0.976 1.000 0.992(0.583-1.688) 0.855(0.697-1.050)
BA VS AA GC 4 0.506 0.232 1.000 1.136(0.918-1.407) 1.139(0.920-1.409)
BA VS AA CRC 2 0.447 0.541 1.000 0.943(0.782-1.138) 0.943(0.782-1.137)
BA VS AA Y 13 0.057 0.872 1.000 1.008(0.889-1.143) 0.993(0.907-1.086)
BA VS AA N 2 0.162 0.509 1.000 1.381(0.575-3.316) 1.119(0.801-1.564)
BA+BB VS AA Overall 15 0.018 0.576 1.000 1.036(0.916-1.172) 1.010(0.931-1.096)
BA+BB VS AA Asian 7 0.013 0.862 1.000 1.019(0.828-1.254) 0.969(0.863-1.090)
BA+BB VS AA Caucasian 7 0.208 0.145 1.000 1.103(0.930-1.308) 1.106(0.966-1.267)
BA+BB VS AA PCR-RFLP 5 0.395 0.246 1.000 1.102(0.932-1.303) 1.103(0.935-1.300)
BA+BB VS AA PCR 3 0.482 0.74 1.000 0.964(0.778-1.195) 0.965(0.779-1.194)
BA+BB VS AA Taq-Man 6 0.002 0.89 1.000 1.016(0.809-1.277) 0.975(0.876-1.085)
BA+BB VS AA HB 8 0.21 0.023 0.560 1.173(0.997-1.381) 1.167(1.022-1.332)
BA+BB VS AA PB 7 0.105 0.139 1.000 0.926(0.800-1.073) 0.925(0.833-1.026)
BA+BB VS AA RC 4 0.002 0.641 1.000 1.133(0.670-1.918) 0.897(0.741-1.087)
BA+BB VS AA GC 4 0.259 0.609 1.000 1.059(0.825-1.359) 1.054(0.861-1.290)
BA+BB VS AA CRC 2 0.513 0.677 1.000 0.963(0.806-1.150) 0.963(0.806-1.150)
BA+BB VS AA Y 13 0.026 0.761 1.000 1.020(0.899-1.157) 0.999(0.917-1.088)
BA+BB VS AA N 2 0.067 0.347 1.000 1.644(0.536-5.038) 1.152(0.858-1.545)
BB VS BA+AA Overall 15 0.02 0.641 1.000 1.035(0.897-1.194) 1.025(0.933-1.126)
BB VS BA+AA Asian 7 0.662 0.773 1.000 0.981(0.859-1.119) 0.981(0.860-1.119)
BB VS BA+AA Caucasian 7 0.001 0.356 1.000 1.174(0.835-1.651) 1.073(0.915-1.257)
BB VS BA+AA PCR-RFLP 5 0.04 0.464 1.000 1.149(0.793-1.664) 1.068(0.862-1.324)
BB VS BA+AA PCR 3 0.738 0.03 0.6 0.770(0.605-0.979) 0.767(0.604-0.974)
BB VS BA+AA Taq-Man 6 0.077 0.169 1.000 1.114(0.927-1.340) 1.087(0.965-1.225)
BB VS BA+AA HB 8 0.078 0.209 1.000 1.100(0.877-1.380) 1.106(0.945-1.295)
BB VS BA+AA PB 7 0.043 0.867 1.000 0.984(0.813-1.191) 0.982(0.874-1.104)
BB VS BA+AA RC 4 0.027 0.273 1.000 1.309(0.809-2.119) 1.063(0.856-1.321)
BB VS BA+AA GC 4 0.191 0.048 0.960 0.819(0.592-1.133) 0.785(0.617-0.998)
BB VS BA+AA CRC 2 0.834 0.613 1.000 1.054(0.858-1.295) 1.054(0.859-1.295)
BB VS BA+AA Y 13 0.018 0.823 1.000 1.017(0.875-1.183) 1.015(0.922-1.118)
BB VS BA+AA N 2 0.176 0.375 1.000 1.366(0.668-2.793) 1.196(0.805-1.776)
rs1805015 B VS A Overall 9 <0.001 0.408 1.000 0.928(0.777-1.108) 0.981(0.897-1.072)
B VS A Caucasian 5 0.001 0.353 1.000 0.863(0.633-1.177) 0.903(0.792-1.031)
B VS A Asian 3 0.202 0.689 1.000 0.943(0.754-1.179) 0.967(0.819-1.141)
B VS A PCR-RFLP 3 <0.001 0.576 1.000 0.831(0.435-1.589) 1.002(0.820-1.224)
B VS A Taq-Man 5 0.099 0.928 1.000 0.980(0.847-1.134) 0.995(0.899-1.102)
B VS A PB 6 <0.001 0.528 1.000 0.918(0.703-1.198) 1.034(0.923-1.157)
B VS A HB 3 0.207 0.15 1.000 0.903(0.752-1.085) 0.899(0.777-1.039)
B VS A Glioma 4 <0.001 0.364 1.000 0.788(0.472-1.317) 0.936(0.778-1.125)
B VS A CRC 2 0.183 0.288 1.000 1.063(0.856-1.318) 1.085(0.933-1.261)
B VS A Y 8 <0.001 0.436 1.000 0.923(0.755-1.129) 0.988(0.899-1.085)
BB VS AA Overall 9 0.014 0.172 1.000 0.688(0.403-1.176) 0.788(0.589-1.056)
BB VS AA Caucasian 5 0.019 0.407 1.000 0.743(0.368-1.501) 0.745(0.507-1.093)
BB VS AA Asian 3 0.686 0.014 0.280 0.256(0.080-0.818) 0.240(0.076-0.752)
BB VS AA PCR-RFLP 3 0.024 0.372 1.000 0.593(0.189-1.867) 0.714(0.410-1.243)
BB VS AA Taq-Man 5 0.047 0.527 1.000 0.807(0.415-1.570) 0.867(0.610-1.234)
BB VS AA PB 6 0.013 0.098 1.000 0.461(0.184-1.152) 0.741(0.500-1.098)
BB VS AA HB 3 0.093 0.468 1.000 0.942(0.467-1.900) 0.851(0.549-1.317)
BB VS AA Glioma 4 0.031 0.213 1.000 0.503(0.170-1.484) 0.640(0.374-1.095)
BB VS AA CRC 2 0.417 0.194 1.000 1.395(0.855-2.276) 1.386(0.847-2.270)
BB VS AA Y 8 0.025 0.072 1.000 0.602(0.346-1.046) 0.731(0.540-0.989)
BA VS AA Overall 9 0.021 0.968 1.000 1.003(0.849-1.185) 1.028(0.926-1.141)
BA VS AA Caucasian 5 0.011 0.651 1.000 0.931(0.682-1.271) 0.933(0.795-1.096)
BA VS AA Asian 3 0.378 0.552 1.000 1.056(0.884-1.262) 1.055(0.884-1.261)
BA VS AA PCR-RFLP 3 0.012 0.992 1.000 1.003(0.551-1.824) 1.132(0.885-1.447)
BA VS AA Taq-Man 5 0.134 0.706 1.000 1.007(0.859-1.181) 1.023(0.909-1.151)
BA VS AA PB 6 0.044 0.522 1.000 1.074(0.863-1.338) 1.113(0.978-1.267)
BA VS AA HB 3 0.301 0.186 1.000 0.889(0.734-1.078) 0.889(0.746-1.059)
BA VS AA Glioma 4 0.012 0.814 1.000 0.946(0.595-1.504) 1.046(0.839-1.305)
BA VS AA CRC 2 0.054 0.622 1.000 0.991(0.682-1.440) 1.046(0.875-1.250)
BA VS AA Y 8 0.03 0.704 1.000 1.035(0.868-1.234) 1.058(0.948-1.182)
BA+BB VS AA Overall 9 0.003 0.683 1.000 0.962(0.801-1.157) 1.004(0.908-1.110)
BA+BB VS AA Caucasian 5 0.002 0.477 1.000 0.885(0.632-1.240) 0.911(0.781-1.062)
BA+BB VS AA Asian 3 0.272 0.902 1.000 0.998(0.809-1.231) 1.011(0.848-1.205)
BA+BB VS AA PCR-RFLP 3 0.001 0.738 1.000 0.889(0.446-1.773) 1.069(0.847-1.351)
BA+BB VS AA Taq-Man 5 0.109 0.877 1.000 0.992(0.844-1.165) 1.009(0.900-1.132)
BA+BB VS AA PB 6 0.004 0.96 1.000 0.993(0.765-1.291) 1.078(0.950-1.222)
BA+BB VS AA HB 3 0.258 0.154 1.000 0.886(0.728-1.079) 0.885(0.748-1.047)
BA+BB VS AA Glioma 4 0.001 0.535 1.000 0.845(0.496-1.440) 0.987(0.799-1.219)
BA+BB VS AA CRC 2 0.083 0.433 1.000 1.025(0.740-1.420) 1.072(0.902-1.274)
BA+BB VS AA Y 8 0.002 0.814 1.000 0.976(0.796-1.197) 1.024(0.920-1.139)
BB VS BA+AA Overall 9 0.039 0.18 1.000 0.717(0.441-1.167) 0.788(0.590-1.052)
BB VS BA+AA Caucasian 5 0.049 0.414 1.000 0.772(0.415-1.437) 0.764(0.523-1.115)
BB VS BA+AA Asian 3 0.699 0.014 0.280 0.254(0.080-0.812) 0.239(0.076-0.749)
BB VS BA+AA PCR-RFLP 3 0.08 0.221 1.000 0.625(0.249-1.571) 0.710(0.411-1.228)
BB VS BA+AA Taq-Man 5 0.058 0.425 1.000 0.823(0.433-1.566) 0.867(0.611-1.231)
BB VS BA+AA PB 6 0.041 0.086 1.000 0.493(0.220-1.105) 0.723(0.489-1.068)
BB VS BA+AA HB 3 0.101 0.545 1.000 0.970(0.489-1.922) 0.875(0.567-1.349)
BB VS BA+AA Glioma 4 0.094 0.099 1.000 0.549(0.225-1.341) 0.640(0.376-1.088)
BB VS BA+AA CRC 2 0.32 0.234 1.000 1.357(0.834-2.208) 1.347(0.825-2.202)
BB VS BA+AA Y 8 0.081 0.038 0.760 0.639(0.395-1.033) 0.727(0.539-0.982)
rs2057768 B VS A Overall 3 0.191 0.218 1.000 1.115(0.923-1.348) 1.093(0.949-1.260)
B VS A Taq-Man 2 0.07 0.305 1.000 1.178(0.806-1.721) 1.102(0.915-1.327)
B VS A PB 2 0.606 0.644 1.000 1.037(0.889-1.210) 1.037(0.889-1.210)
B VS A GC 2 0.152 0.095 1.000 1.218(0.907-1.638) 1.173(0.973-1.414)
BB VS AA Overall 3 0.032 0.96 1.000 1.019(0.488-2.129) 0.898(0.616-1.307)
BB VS AA Taq-Man 2 0.022 0.642 1.000 1.321(0.408-4.274) 1.078(0.676-1.717)
BB VS AA PB 2 0.742 0.117 1.000 0.709(0.459-1.095) 0.707(0.458-1.090)
BB VS AA GC 2 0.013 0.745 1.000 1.245(0.331-4.678) 1.024(0.625-1.678)
BA VS AA Overall 3 0.552 0.009 0.180 1.288(1.067-1.555) 1.288(1.067-1.554)
BA VS AA Taq-Man 2 0.72 0.183 1.000 1.183(0.924-1.516) 1.183(0.924-1.516)
BA VS AA PB 2 0.276 0.015 0.300 1.290(1.032-1.611) 1.289(1.051-1.581)
BA VS AA GC 2 0.672 0.008 0.160 1.401(1.091-1.798) 1.401(1.091-1.798)
BA+BB VS AA Overall 3 0.495 0.028 0.560 1.226(1.023-1.470) 1.226(1.023-1.470)
BA+BB VS AA Taq-Man 2 0.303 0.191 1.000 1.176(0.918-1.507) 1.171(0.924-1.484)
BA+BB VS AA PB 2 0.358 0.084 1.000 1.190(0.977-1.450) 1.190(0.977-1.450)
BA+BB VS AA GC 2 0.725 0.016 0.320 1.343(1.056-1.709) 1.343(1.056-1.709)
BB VS BA+AA Overall 3 0.021 0.82 1.000 0.916(0.429-1.955) 0.797(0.553-1.148)
BB VS BA+AA Taq-Man 2 0.021 0.734 1.000 1.219(0.389-3.820) 0.998(0.635-1.569)
BB VS BA+AA PB 2 0.557 0.031 0.610 0.634(0.415-0.967) 0.628(0.412-0.957)
BB VS BA+AA GC 2 0.007 0.904 1.000 1.090(0.268-4.429) 0.870(0.539-1.403)
Open in a new tab

PH: P value of Q test for heterogeneity test; PZ: means statistically significant (P < 0.05); PAdjust: Multiple testing P value according to Bonferroni Correction; CRC: Colorectal cancer; GC: Gastric cancer; BC: Breast cancer; UBC: Bladder cancer; CC: Cervical cancer; PC: Pancreatic cancer; RC: Renal cancer; NPC: nasopharyngeal carcinoma; HL: Hodgkin's lymphoma; H-B: Hospital based; P-B: Population based; HWE: Hardy Weinberg Equilibrium; Padjust value less than 0.05*(4 polymorphisms* 5 models) was considered as statistically significant, which was marked with bold font in the table). Note: Heterogeneity was considered to be significant when the P-value was less than 0.1. If there was no significant heterogeneity, a fixed effect model (Der-Simonian Laird) was used to evaluate the point estimates and 95% CI; otherwise, a random effects model (Der-Simonian Laird) was used. And the Pz was calculated based on the actual model adopted.

Figure 2.

Figure 2

Open in a new tab

Meta-analysis of the association between IL-4R rs1801275 polymorphism and overall cancer risk.

Sensitivity analysis and publication bias

Sensitivity analysis was applied to assess the impaction of the individual studies on the integrated data by removing a single data from the pooled analysis every time for each polymorphism. We uncovered that there was no individual study influenced the result of pooled ORs (Figure 3 and Table S3). Furthermore, Begg's funnel plot and Egger's test was conducted to evaluate the publication bias, and no significant evidence of distinct asymmetry was disclosed through the shapes of the funnel plots, as well as the value of P > |t| in Egger's test (Figure 4 and Table S4).

Figure 3.

Figure 3

Open in a new tab

Begg's funnel plot for publication bias test under 4 polymorphisms of IL-4R gene (B vs. A). The x-axis is log (OR), and the y-axis is natural logarithm of OR. The horizontal line in the figure represents the overall estimated log (OR). The two diagonal lines indicate the pseudo 95% confidence limits of the effect estimate.

Figure 4.

Figure 4

Open in a new tab

Sensitivity Analysis of Overall ORs Co-Efficients for 4 polymorphisms of Il-4R gene (B vs. A). Results were calculated by omitting each study in turn. The two ends of the dotted lines represent the 95% CIs.

LD Analyses across Populations and in-silico analysis of IL-4R expression

For better understanding the quantitative synthesis, LD analysis was performed to test the presence or absence of bins in the region containing these polymorphisms in IL-4R. LD plots for polymorphisms in IL-4R genes were presented in Figure S4. Highlighted, there is significant LD between rs1805010 and rs1801275 in CEU and JPT populations (CEU: r2= 0.7; JPT: r2= 0.67).

In-silico results indicated that the expression of IL-4R in colon adenocarcinoma was higher than that in normal colon tissue (Transcripts Per Kilo base Million (TPM) =41.6 vs. 60.0, P < 0.01), as well as in rectum adenocarcinoma (TPM=43.6 vs. 58.4, P < 0.01), kidney Chromophobe (TPM=6.47 vs. 21.8, P < 0.01), kidney renal clear cell carcinoma (TPM=43.7 vs. 18.2, P < 0.01), and pancreatic adenocarcinoma (TPM=62.4 vs. 4.99, P < 0.01). All the result is shown in Figure S5.

Discussion

At present, the identification of novel genetic and molecular predictors is desiderata, in order to successfully early diagnose or prevent the malignancies. Several biomarks are reported might be associated with tumorigenesis. IL4R, which encodes the alpha chain of the IL-4R, can bind IL-4 and IL-13 contributing to the regulation of IgE production46, one soluble form of the encoded protein can restrain IL-4 mediated cell proliferation. Enormous genetic studies have uncovered that several SNPs in IL-4R gene were identified to be significantly associated with many diseases, including cancers 47. These SNPs have the ability to regulate the efficacy of gene expressions, interfere with the synthesis of the protein, disrupt signaling pathways and result in the instabilities of the exonic mRNA 47, 48.

The recent study has suggested that rs1801275 polymorphism in the IL-4R gene can predict 2.29-fold glioblastoma susceptibility by the over-dominant model34, a result consistent with a previous study that rs1801275 contributed to an increased susceptibility of glioblastoma (OR = 1.61, 95% CI, 1.05-2.47) in a population-based study11. However, Li et al. indicated that the mutant G allele plays a protective function in tumorigenesis (OR=0.71, 95%CI, 0.50-0.99) 12. Moreover, a significantly increased susceptibility of gastric cancer was identified in the IL-4R rs1805010 polymorphism in a Caucasian population26, which was not consistent with previous investigations that no association of SNPs in IL-4R gene and gastric cancer susceptibility were displayed in Taiwan [20]and Japanese39 populations. And in a separate research, Chu et al. 9 identified that IL-4R rs1805010 polymorphism was associated with a significantly decreased susceptibility of renal cell carcinoma. A large number of case-control studies have elaborated the association between these polymorphisms in IL-4R and the susceptibility of a variety of cancer types, data from these publications remained inconsistent and controversial.

Meta-analysis is a validated method, by which we can enlarge the effective sample size via pooling the data from the separate related case-control studies. Besides, the statistical power for estimation of the genetic effects was also enhanced 49. There are several previous meta-analyses also concerned about IL-4R and tumorigenesis. Cho et al.50 demonstrated a reduced risk of GC for rs1801275, but they mixed the gastric cancer, esophageal cancer, pancreatic cancer and colorectal cancer, without any subgroup analysis of cancer type. Wang et al.51 performed a meta-analysis about IL-4R and cancer risk on rs1801275, rs1805010 and rs1805015, however, they only enrolled 36 studies, and didn't adjust the P value of Q-test, which might lead to statistical error.

All in all, our recent updated meta-analysis draw a comprehensive, precise and convincible result, which is that rs1801275, rs1805010, rs1805015 and rs2057768 polymorphisms of IL-4R are not associated with tumorigenesis. The advantages of this research should not be buried. Firstly, a comprehensive search was conducted to identify more qualified studies, this analysis is persuasive and substantive. Secondly, the quality of each registered research was evaluated by NOS scale, low-quality studies were eliminated to raise the credibility of results. Thirdly, stratification analysis was performed by ethnicity, source of controls, tumor type or race, in order to decrease the impact of heterogeneity and obtain the real conclusion. Fourthly, Bonferroni corrections formula was used to adjust the results of polymorphism, ensuring avoid overstating results. Fifthly, sensitivity analysis was presented to confirm the stability of conclusion calculated from these studies, and Egger's test and Begg's funnel plot was carried out to detect publication bias. Nevertheless, there are still several limitations that should be mentioned. Firstly, we have enrolled some small size case-control studies that contained small-scale numbers of the cases and controls. Thus, an insufficient capacity that slight effects on cancer susceptibility occurred when a stratified analysis was conducted by the cancer type, ethnicity, source of control, genotyping method and etc. Secondly, the controls were not accordant defined, part of them was population-based and the others were hospital-based. Therefore, when the polymorphism was predicted to influence the susceptibility of other diseases, the controls may not invariably be represented in the potential source populations. Thirdly, the majority of the enrolled studies were Caucasian groups, and no African data were available. Fourth, since the lack of raw data, such as smoking and drinking status, we are unable to perform a further assessment for the potential gene-gene and gene-environment interactions.

In conclusion, our work suggested that no significant association was identified between IL-4R polymorphisms and cancer susceptibility. Further well-designed studies with large sample sizes will be continued on this issue of interest.

Supplementary Material

Supplementary figures and tables.

Click here for additional data file. (2.5MB, pdf)

Acknowledgments

All authors agreed to publish this work and contributed to the design, data extraction, data analysis, chart drawing drafting, and revising of the article. All authors are committed to being responsible for their work. Y.Q. and Z.T. accessed information from literature for this article.Y.Q., S.F., L.Z. and C.L. contributed towards writing, discussing, and editing the manuscript. Y.Q. and C.L. reviewed the manuscript.

Abbreviations

Abbreviation

Full name

IL-4R

Interleukin-4 receptor

IL-4

Interleukin-4

ORs

Odds ratios

CIs

Confidence intervals

SNP

Single nucleotide polymorphism

HWE

Hardy-Weinberg equilibrium

NOS

Newcastle-Ottawa Scale

LD

Linkage Disequilibrium

TPM

Transcripts Per Kilobase Million.

References

  • 1.Roser M, Ritchie H. Cancer. OurWorldInData.org.: Max Roser and Hannah Ritchie. 2018.
  • 2.Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M. et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136:E359–86. doi: 10.1002/ijc.29210. [DOI] [PubMed] [Google Scholar]
  • 3.Pharoah PD, Dunning AM, Ponder BA, Easton DF. Association studies for finding cancer-susceptibility genetic variants. Nat Rev Cancer. 2004;4:850–60. doi: 10.1038/nrc1476. [DOI] [PubMed] [Google Scholar]
  • 4.Imyanitov EN. Gene polymorphisms, apoptotic capacity and cancer risk. Hum Genet. 2009;125:239–46. doi: 10.1007/s00439-009-0636-7. [DOI] [PubMed] [Google Scholar]
  • 5.Nakamura E, Megumi Y, Kobayashi T, Kamoto T, Ishitoya S, Terachi T. et al. Genetic polymorphisms of the interleukin-4 receptor alpha gene are associated with an increasing risk and a poor prognosis of sporadic renal cell carcinoma in a Japanese population. Clin Cancer Res. 2002;8:2620–5. [PubMed] [Google Scholar]
  • 6.Formentini A, Braun P, Fricke H, Link KH, Henne-Bruns D, Kornmann M. Expression of interleukin-4 and interleukin-13 and their receptors in colorectal cancer. Int J Colorectal Dis. 2012;27:1369–76. doi: 10.1007/s00384-012-1456-0. [DOI] [PubMed] [Google Scholar]
  • 7.Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI. et al. A new equation to estimate glomerular filtration rate. Annals of internal medicine. 2009;150:604–12. doi: 10.7326/0003-4819-150-9-200905050-00006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Olson S, Simon J, Orlow I, Tommasi D, Roy P, Bayuga S. et al. Allergies, variants in IL-4 and IL-4Rα genes, and risk of pancreatic cancer. Cancer Detection & Prevention. 2007;31:345–51. doi: 10.1016/j.cdp.2007.10.002. [DOI] [PubMed] [Google Scholar]
  • 9.Chu H, Wang M, Yan F, Zhong D, Shi D, Ma L. et al. Polymorphisms in the IL-13 and IL-4R genes are associated with the development of renal cell carcinoma. Ann Oncol. 2012;23:2114–21. doi: 10.1093/annonc/mdr607. [DOI] [PubMed] [Google Scholar]
  • 10.Chen X, Chen Z, Wang H, Xiong X, Liu X, Hu C. et al. Plasmid pUDK-HGF encoding human hepatocyte growth factor gene attenuates gentamicin-induced kidney injury in rats. Exp Toxicol Pathol. 2013;65:541–7. doi: 10.1016/j.etp.2012.03.003. [DOI] [PubMed] [Google Scholar]
  • 11.Schwartzbaum J, Ahlbom A, Malmer B, Lönn S, Brookes AJ, Doss H. et al. Polymorphisms Associated with Asthma Are Inversely Related to Glioblastoma Multiforme. Cancer Res. 2005;65:6459–65. doi: 10.1158/0008-5472.CAN-04-3728. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Li S, Jin T, Zhang J, Lou H, Yang B, Li Y. et al. Polymorphisms of TREH, IL4R and CCDC26 genes associated with risk of glioma. Cancer Epidemiology. 2012;36:283–7. doi: 10.1016/j.canep.2011.12.011. [DOI] [PubMed] [Google Scholar]
  • 13.Bonferroni CE. Teoria Statistica Delle Classi e Calcolo Delle Probabilità. Comm Firenze. 1936;1936:216–8. [Google Scholar]
  • 14.Lau J, Ioannidis JP, Schmid CH. Quantitative synthesis in systematic reviews. Annals of internal medicine. 1997;127:820–6. doi: 10.7326/0003-4819-127-9-199711010-00008. [DOI] [PubMed] [Google Scholar]
  • 15.R D, N L. Meta-analysis in clinical trials. Controlled Clinical Trials. 1986;7:177. doi: 10.1016/0197-2456(86)90046-2. [DOI] [PubMed] [Google Scholar]
  • 16.Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50:1088–101. [PubMed] [Google Scholar]
  • 17.Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21:1539–58. doi: 10.1002/sim.1186. [DOI] [PubMed] [Google Scholar]
  • 18.Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45:W98–W102. doi: 10.1093/nar/gkx247. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Calhoun ES, Mcgovern RM, Janney CA, Cerhan JR, Iturria SJ, Smith DI. et al. Host genetic polymorphism analysis in cervical cancer. Clin Chem. 2002;48:1218–24. [PubMed] [Google Scholar]
  • 20.MS W, CY W, CJ C, MT L, CT S, JT L. Interleukin-10 genotypes associate with the risk of gastric carcinoma in Taiwanese Chinese. Int J Cancer. 2003;104:617–23. doi: 10.1002/ijc.10987. [DOI] [PubMed] [Google Scholar]
  • 21.Balasubramanian SP, Azmy IA, Higham SE, Wilson AG, Cross SS, Cox A. et al. Interleukin gene polymorphisms and breast cancer: a case control study and systematic literature review. Bmc Cancer. 2006;6:188. doi: 10.1186/1471-2407-6-188. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Brenner AV, Butler MA, Wang SS, Ruder AM, Rothman N, Schulte PA. et al. Single-nucleotide polymorphisms in selected cytokine genes and risk of adult glioma. Carcinogenesis. 2007;28:2543–7. doi: 10.1093/carcin/bgm210. [DOI] [PubMed] [Google Scholar]
  • 23.Ivansson EL, Gustavsson IM, Magnusson JJ, Steiner LL, Magnusson PK, Erlich HA. et al. Variants of chemokine receptor 2 and interleukin 4 receptor, but not interleukin 10 or Fas ligand, increase risk of cervical cancer. Int J Cancer. 2007;121:2451–7. doi: 10.1002/ijc.22989. [DOI] [PubMed] [Google Scholar]
  • 24.Landi S, Bottari F, Gemignani F, Gioia-Patricola L, Guino E, Osorio A. et al. Interleukin-4 and interleukin-4 receptor polymorphisms and colorectal cancer risk. Eur J Cancer. 2007;43:762–8. doi: 10.1016/j.ejca.2006.10.024. [DOI] [PubMed] [Google Scholar]
  • 25.Gu F, Qureshi AA, Niu T, Kraft P, Guo Q, Hunter DJ. et al. Interleukin and interleukin receptor gene polymorphisms and susceptibility to melanoma. Melanoma Res. 2008;18:330–5. doi: 10.1097/CMR.0b013e32830658b2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Zambon CF, Basso D, Marchet A, Fasolo M, Stranges A, Schiavon S. et al. IL-4 -588C>T polymorphism and IL-4 receptor alpha [Ex5+14A>G; Ex11+828A>G] haplotype concur in selecting H. pylori cagA subtype infections. Clin Chim Acta. 2008;389:139–45. doi: 10.1016/j.cca.2007.12.008. [DOI] [PubMed] [Google Scholar]
  • 27.Yang H, Gu J, Lin X, Grossman HB, Ye Y, Dinney CP. et al. Profiling of genetic variations in inflammation pathway genes in relation to bladder cancer predisposition. Clin Cancer Res. 2008;14:2236–44. doi: 10.1158/1078-0432.CCR-07-1670. [DOI] [PubMed] [Google Scholar]
  • 28.Mohan S, Mohanasenthil, Paul SF, Shroff S, Venkatesan V. Interleukin-4-receptor alpha gene polymorphism and the risk of renal cell carcinoma in a South Indian population. Asian Pac J Cancer Prev. 2009;10:295–8. [PubMed] [Google Scholar]
  • 29.Lee YS, Choi HB, Lee IK, Kim TG, Oh ST. Association between interleukin-4R and TGF-β1 gene polymorphisms and the risk of colorectal cancer in a Korean population. Colorectal Disease. 2010;12:1208–12. doi: 10.1111/j.1463-1318.2009.02080.x. [DOI] [PubMed] [Google Scholar]
  • 30.Scola L, Giacalone A, Marasà L, Mirabile M, Vaccarino L, Forte GI. et al. Genetic Determined Downregulation of Both Type 1 and Type 2 Cytokine Pathways Might Be Protective against Pancreatic Cancer. Ann Ny Acad Sci. 2010;1155:284–8. doi: 10.1111/j.1749-6632.2008.03686.x. [DOI] [PubMed] [Google Scholar]
  • 31.Ruan Z, Zhao Y, Yan L, Chen H, Fan W, Chen J. et al. Single nucleotide polymorphisms in IL-4Ra, IL-13 and STAT6 genes occurs in brain glioma. Frontiers in Bioscience. 2011;3:33–45. doi: 10.2741/e217. [DOI] [PubMed] [Google Scholar]
  • 32.Chu H, Ma L, Wang M, Shi D, Qin C, Yuan L. et al. The polymorphisms of IL-4, IL-4R and IL-13 genes and bladder cancer risk in a Chinese population: a case-control study. Mol Biol Rep. 2012;39:5349–57. doi: 10.1007/s11033-011-1334-9. [DOI] [PubMed] [Google Scholar]
  • 33.Ingram N, Northwood EL, Perry SL, Marston G, Snowden H, Taylor JC. et al. Reduced type II interleukin-4 receptor signalling drives initiation, but not progression, of colorectal carcinogenesis: evidence from transgenic mouse models and human case-control epidemiological observations. Carcinogenesis. 2013;34:2341–9. doi: 10.1093/carcin/bgt222. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Jin T, Li X, Zhang J, Hong W, Geng T, Gang L. et al. Genetic association between selected cytokine genes and glioblastoma in the Han Chinese population. Bmc Cancer. 2013;13:236. doi: 10.1186/1471-2407-13-236. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Quan L, Gong Z, Yao S, Bandera EV, Zirpoli G, Hwang H. et al. Cytokine and cytokine receptor genes of adaptive immune response are differentially associated with breast cancer risk in American women of African and European ancestry. Int J Cancer. 2014;134:1408–21. doi: 10.1002/ijc.28458. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Sousa H, Mesquita L, Ribeiro J, Catarino R, Breda E, Medeiros R. Polymorphisms in host immune response associated genes and risk of nasopharyngeal carcinoma development in Portugal. Immunobiology. 2016;221:145–52. doi: 10.1016/j.imbio.2015.09.015. [DOI] [PubMed] [Google Scholar]
  • 37.Liang G, Liu Q, Sun J, Liu W, Shen W. The rs20541 G>A polymorphism in the interleukin 13 gene is associated with a decreased risk of renal cell carcinoma in a Chinese population. 2017; 10(1): 708-16.
  • 38.Wiemels J, Wiencke J, Kt, Moghadassi M, Rice T, Urayama K, Miike R. et al. Allergy-related polymorphisms influence glioma status and serum IgE levels. Cancer Epidemiol Biomarkers Prev. 2007;16:1229–35. doi: 10.1158/1055-9965.EPI-07-0041. [DOI] [PubMed] [Google Scholar]
  • 39.Ando T, Ishikawa T, Kato H, Yoshida N, Naito Y, Kokura S. et al. Synergistic effect of HLA class II loci and cytokine gene polymorphisms on the risk of gastric cancer in Japanese patients with Helicobacter pylori infection. Int J Cancer. 2009;125:2595–602. doi: 10.1002/ijc.24666. [DOI] [PubMed] [Google Scholar]
  • 40.Xinru W, Li L, Huang T, Li H. Relationship of TGF-β and IL-4R gene polymorphisms with risk of classical Hodgkin lymphoma. Chinal Journal of Hematology. 2012;33:1015–9. [PubMed] [Google Scholar]
  • 41.Shamran HA, Hamza SJ, Yaseen NY, Al-Juboory AA, Taub DD, Price RL. et al. Impact of single nucleotide polymorphism in IL-4, IL-4R genes and systemic concentration of IL-4 on the incidence of glioma in Iraqi patients. Int J Med Sci. 2014;11:1147–53. doi: 10.7150/ijms.9412. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Crusius JB, Canzian F, Capella G, Pena AS, Pera G, Sala N. et al. Cytokine gene polymorphisms and the risk of adenocarcinoma of the stomach in the European prospective investigation into cancer and nutrition (EPIC-EURGAST) Ann Oncol. 2008;19:1894–902. doi: 10.1093/annonc/mdn400. [DOI] [PubMed] [Google Scholar]
  • 43.Wilkening S, Tavelin B, Canzian F, Enquist K, Palmqvist R, Altieri A. et al. Interleukin promoter polymorphisms and prognosis in colorectal cancer. Carcinogenesis. 2008;29:1202–6. doi: 10.1093/carcin/bgn101. [DOI] [PubMed] [Google Scholar]
  • 44.FB CA, PM TC, MC AS, Interleukin-4 receptor -3223T→C polymorphism is associated with increased gastric adenocarcinoma risk. 2012; 26: 532-6. [DOI] [PMC free article] [PubMed]
  • 45.Wells M, Chande N, Adams P, Beaton M, Levstik M, Boyce E. et al. Meta-analysis: vasoactive medications for the management of acute variceal bleeds. Aliment Pharmacol Ther. 2012;35:1267–78. doi: 10.1111/j.1365-2036.2012.05088.x. [DOI] [PubMed] [Google Scholar]
  • 46.Choi WA, Kang MJ, Kim YJ, Seo JH, Kim HY, Kwon JW. et al. Gene-gene interactions between candidate gene polymorphisms are associated with total IgE levels in Korean children with asthma. J Asthma. 2012;49:243–52. doi: 10.3109/02770903.2012.660294. [DOI] [PubMed] [Google Scholar]
  • 47.Howell WM, Rose-Zerilli MJ. Cytokine gene polymorphisms, cancer susceptibility, and prognosis. J Nutr. 2007;137:194S–9S. doi: 10.1093/jn/137.1.194S. [DOI] [PubMed] [Google Scholar]
  • 48.Aldred MA, Eng C. SNP'ing at nasopharyngeal cancer susceptibility: for whom the bell TOLLs. Cancer Biol Ther. 2006;5:1292–3. doi: 10.4161/cbt.5.10.3487. [DOI] [PubMed] [Google Scholar]
  • 49.Peng H, Zhou M, Xu WD, Xu K, Zhai Y, Li R. et al. Association of PTPN22 C1858T polymorphism and type 1 diabetes: a meta-analysis. Immunol Invest. 2012;41:484–96. doi: 10.3109/08820139.2012.664226. [DOI] [PubMed] [Google Scholar]
  • 50.Cho YA, Kim J. Association of IL4, IL13, and IL4R polymorphisms with gastrointestinal cancer risk: A meta-analysis. J Epidemiol. 2017;27:215–20. doi: 10.1016/j.je.2016.06.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Wang JY, Zhou YQ, Li XX, Jin X, Wang LL, Lei L. et al. Associations between three polymorphisms in the interleukin-4 receptor gene and risk of cancer: a meta-analysis. Asian Pac J Cancer Prev. 2012;13:6227–32. doi: 10.7314/apjcp.2012.13.12.6227. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary figures and tables.

Click here for additional data file. (2.5MB, pdf)

Articles from Journal of Cancer are provided here courtesy of Ivyspring International Publisher

RESOURCES