[Frontiers in Bioscience E3, 33-45, January 1, 2011]

Single nucleotide polymorphisms in IL-4Ra,IL-13 and STAT6 genes occurs in brain glioma

Zhenchao Ruan1, Yao Zhao3, Lili Yan1, Hongyan Chen3, Weiwei Fan3, Juxiang Chen4, Qihan Wu5, Ji Qian3, Tianbao Zhang6, KeKe Zhou2, Yin Mao2, Liangfu Zhou2, Yan Huang1, Daru Lu3

1State Key Laboratory of Genetic Engineering, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, 200433, People's Republic of China, 2Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, 201206, People 's Republic of China, 3State Key Laboratory of Genetic Engineering and Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, 200433, People 's Republic of China, 4Department of Neurosurgery, ChangZheng Hospital, Second Military Medical University, Shanghai Neurosurgical Institute, Shanghai 200003, People's Republic of China, 5School of Life Science, East China Normal University, Shanghai 200062, People 's Republic of China, 6Department of Toxicology, Shanghai Second Military Medical University, Shanghai, 200433, People 's Republic of China


3.Methods and materials
3.1. Study population
3.3.Laboratory genotyping
3.4. Statistical analysis
4.1.Characteristics of study populations
4.2.Association study without stratification
4.3.Association analysis after stratification
4.4.Haplotype analysis for STAT6
4.5. Gene-Gene interaction between IL-13 and IL-4Ra for susceptibility to glioma


Gliomas are aggressive brain tumor. Association studies were consistent for an inverse association between asthma and allergic conditions (IgE levels) and risk of glioma. Studies reported that the IL-4Ra, IL-13 and STAT6 genes played a relatively strong role in IgE production or allergy. This population-based case-control study aimed to find potential association between single nucleotide polymorphisms IL-13rs20541, IL-4Rars1801275 and glioma susceptibility in population, as well as STAT6 rs1059513 and STAT6 rs324015. Among non-smokers, homozygote GG of STAT6 4610A/G showed an increased association with risk of glioma compared with AA (adjusted OR=1.691, 95%CI=1.152-2.481, p=0.007, corrected p=0.028), and the haplotype with A allele at rs1059513 and G allele at rs324015 was revealed to increase glioma risk significantly (OR=1.321,95%CI= 1.081-1.614, p=0.007,corrected p=0.028). GG genotype of STAT6 4610A/G was a significant risk factor compared with AA in glioblastoma (adjusted OR=1.856, 95%CI=1.153-2.987, p=0.011, corrected p=0.044). GG of STAT6 4610A/G was significantly related to increased WHO IV risk compared with AA (adjusted OR=1.591,95%CI=1.030-2.459, p=0.036, corrected p=0.144). Interaction between IL-13 Arg130Gln and IL-4Ra Gln576Arg was observed in decreasing glioma risk (p=0.045).


Gliomas are aggressive lethal solid brain tumor arising from support cells in the central nervous system, which can be divided into astrocytic tumors, oligodendrogliomas and oligoastrocytomas. According to the WHO classification, gliomas can be graded into four hitological degrees of malignancy (1). Astrocytomas, amounting to 80-85% of all gliomas, are tumors composed of neoplastic astrocytes predominantly and graded from low (grade I) to high (grade IV) according to hallmarks of the tumor histological aberrations (2). Grade IV astrocytomas are known as glioblastoma multiforme (GBM), the most common type of adult glioma with a poor prognosis. Inherited syndromes account for only a small proportion of glioblastoma but familial aggregation of this tumor has been observed (3) Oligodendrogliomas and oligoastrocytomas are tiered into grade II, and anaplastic is grade III lesions. Despite the development of therapy technology, the death rate of glioblastoma patients decreases a little (4). The strongest known environmental risk factor for glioma is exposure to therapeutic doses of ionizing radiation (5) and genetic factors such as single nucleotide polymorphisms (SNPs) might also associate with glioma risk (6). Evidence from cohort and case-control studies was consistent for an inverse association between self-reported asthma and allergic conditions and risk of glioma (7-9). Wiemels et al. examined whether allergic disease reduces brain tumor risk by comparing serum IgE levels (10). Although lower serum IgE levels were found in glioma cases than controls, the possibility that the immunosuppression by the tumor itself or by its standard treatment related to lower IgE levels or eliminate allergies can not be excluded.

Genes IL-4 and IL-13 share a common receptor component, IL-4Ra chain, and code immunoregulatory cytokines that share functions. Many of the actions of IL-13 closely resemble those of IL-4. Both cytokines play a central role in allergy by stimulating IgE synthesis in B lymphocytes (11) and reduce production of pro-inflammatory cytokines by macrophages (12). Previous studies showed they have strong antitumor activity in mice and inhibit proliferation of astrocytoma and low-grade glioma in human cell lines (13, 14). The pleiotropic effects of IL-13 and IL-4 are mediated through the IL-4R, which is composed of the common IL4-Ra subunit and either a gamma subunit or IL-13 receptor (IL-13R) alpha subunit (low-affinity IL-13Ra1 (15)or a high-affinity IL-13Ra2 subunit (16)). IL-13Ra2 acts as an inhibitor of IL-4-dependent signal transduction and STAT6-responsive gene expression. The inhibition is likely mediated through physical interaction between cytoplasmic domain of the IL-4Ra chain and short intracellular domain of IL-13Ra2 protein (17). Human gene signal transducer and activator of transcription 6 (STAT6) spans 19 kb of genomic DNA containing 23 exons in region 12q12.3-14.1 (18). The phosphorylation of the intracellular molecule STAT6 through the activated Janus tyrosine kinases leaded to a homodimerisation of STAT6, which migrated to the nucleus and bound to a specific region found within promoters of IL-4-inducible and IL-13-inducible genes (19).

Studies of germ line polymorphisms of the IL-4Ra, IL-13 and STAT6 genes provide relatively strong support for a role of these genes in IgE production or allergy (20-22). Previous analysis of data from a population-based case-control study in Sweden indicated genetic variants that increase the risk of IgE or allergies also decrease glioblastoma risk. Polymorphism in IL-4Ra Gln576Arg increased GBM risk (p=0.02) (23), but this difference was not confirmed by analysis in larger population in European (24) or United States (25). Although the combination of IL-13 and IL-4Ra relate to asthma and plasma IgE concentration (26, 27), no previous studies have indicated the roles of their interaction in glioma susceptibility. For SNPs in STAT6, association studies were conducted for asthma and IgE level (28, 29), but no association study in STAT6 had been reported in glioma. Here we reported population-based case-control study aimed to address potential association of SNPs IL-13 rs20541, IL-4Ra rs1801275 and their interaction in glioma susceptibility, as well as STAT6 SNPs rs324015 and rs1059513.


3.1. Study population

The population in this case-control study was similar to Liu et al. (30). All subjects were genetically unrelated ethnic Han from Shanghai and the surrounding provinces (Zhejiang, Jiangsu and Anhui) in east China. Patients diagnosed with histopathologically confirmed glioma were consecutively recruited between October 2004 and May 2006 in the Department of Neurosurgery at Huashan Hospital of Fudan University (Shanghai, China) with no restrictions of age, gender and histology. Cancer-free controls were recruited during the same period including trauma outpatients (20%) from the Emergency Medical Centre and hospital visitors (80%) who came to the health examination clinic for an annual check-up at the same hospital (Huashan Hospital). The exclusion criteria for healthy subjects included central nervous system-related disease, self-reported history of any cancer and previous radiotherapy and chemotherapy for unknown disease conditions. All the control subjects were frequency matched to the cases on age (+/-5 years), gender and residence area (urban or rural). The research protocol was reviewed and approved by the Fudan University Ethics Committee for Human Subject Research.

3.2. Questionnaire

Each eligible subject was interviewed by trained personnel who were not aware of the case and control with a structured questionnaire to obtain detailed information on demographic factors, family history of cancer (fmc), smoking status, and health characteristics. Fmc was defined as any self-reported cancer in the first-degree relatives (parents, siblings, or children). Never-smokers were defined as those who had smoked less than one cigarette per day and less than 1 year in their lifetime. Smokers were classified into ever-smokers and current-smokers. After interview, 3-5ml venous blood specimen was collected from each subject after the informed consent was obtained.

3.3. Laboratory genotyping

EDTA-containing tubes were used to collect blood samples and then Qiagen Blood Kit (Qiagen, Chatsworth, CA, USA) was applied to extract genomic DNA. Polymerase chain reaction-ligation detection reaction (PCR-LDR) method was used to perform the genotyping.

Specific primers were summarized in Table 1 (Table 1). PCR was conducted on the ABI 9600 (Applied Biosystems, Foster City, CA, USA) in a system with total volume of 15 ul containing 1 ul genomic DNA, 1.5 ul 10�PCR Buffer, 0.13 uM each primer, 0.2 mM dNTP, 0.25 ul Taq DNA polymerase (Qlagen Gmbh, Hllden, Germany) and 7.5 ul H2O. The cycling parameters were: 94 �C for 1 min; 35 cycles at 94 �C for 10 s, 56 �C for 20 s, 72 �C for 40 s; and a final extension step at 72 �C for 3 min. For each PCR product, the ligation reaction was performed in a final volume of 10 ul including 2 ul of PCR product, 1 ul 10�Taq DNA ligase buffer, 0.02 uM of probe mixture, 5 U Taq DNA ligase (New England Biolabs, Beverly, Mass, USA) and 6 ul H2O. The LDR parameters were as follows: 25 cycles at 94 �C for 30 s and 55 �C for 4 min. The LDR reaction products were analyzed on ABI 377 DNA Sequencer (Applied Biosystems). To confirm the accuracy of PCR-LDR genotyping method, direct DNA sequencing of randomly selected PCR products was performed. The proportion of the sequencing samples was about 5%.

3.4. Statistical analysis

To examine Hardy-Weinberg Equilibrium, a Chi-Square test for goodness of fit was performed using a web-based program (http://ihg2.helmholtz-muenchen.de/cgi-bin/hw/hwa1.pl). The Fisher's exact Chi-Square test was conducted to compare the frequency distribution of age, gender, smoking status and fmc between cases and controls. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by univariate logistic regression analyses and multivariate logistic regression analyses adjusted for age and sex, or age, fmc and smoking status under dominant genetic model, recessive genetic model and additive model.

The patients were stratified into three subgroups according to histology: glioblastoma, astrocytomas (including diffuse astrocytomas, anaplastic astrocytomas or other astrocytomas except for glioblastoma) and other gliomas (including oligodendroglimas, enpendymomas or mixed glioma). Patients were also grouped with WHO grade to glioma: WHOI, WHOII, WHOIII and WHOIV. Subgroup analyses according to smoking status, histology and WHO grade were performed to estimate the specific ORs and CIs. Haplotype frequencies for STAT6 were estimated from genotype data using the expectation-maximization algorithm in Haploview 4.1 (Broad Institute, Cambridge, MA). Gene-Gene interaction analysis between IL13 and IL4Ra was performed by introducing an interaction term into the univariate and multivariate logistic regression which was adjusted by age, sex, fmc, smoking status and genotype.

All the regression analyses were performed using SPSS 16.0 (SPSS Inc., Chicago, IL, USA). QUANTO (version 1.2.4) software was used to calculate the statistic power. All p-values were two-sided, and considered significant if a p-value is less than 0.05. We adjusted multiple test using Bonferroni corrections.


4.1. Characteristics of study populations

806 glioma cases and 910 control subjects were recruited without the restrictions of age, sex and glioma histology. Among all participants, DNA samples and questionnaires were available from 677 cases and 698 cancer-free control subjects representing an 84.0% and 76.7% of all eligible case and control subjects, respectively.

Table 2 showed the summarized characteristics of case patients and control subjects included in our study (Table 2). The mean ages were 41.6 years (+/-16.3 years, ranging from 2-79 yeas) for the cases and 39.6 years (+/-18.3 years, ranging from 1-86 years) for the controls. No significant differences on age, sex and smoking status between cases and controls (p=0.355 for age stratification, 0.141 for sex and 0.093 for smoking status) were observed. Among 677 case patients, 256 (37.8%) had astrocytomas, 220 (32.5%) had glioblastoma and 201 (29.7%) had other gliomas.

4.2. Association study without stratification

The genotype distributions of controls were in Hardy-Weinberg equilibrium (data not shown). No significant association between IL-13 Arg130Gln (CT and TT genotypes) and glioma risk comparing to CC was observed in the univariate and multivariate logistic regression analysis The similar result was also observed in IL-4Ra Gln576Arg (rs1801275) when AG and GG genotypes comparing to AA. STAT6 4219A/G (rs324015) and STAT6 4610A/G (rs1059513) showed no significant association to glima risk either (Table 3). Dominant genetic model, recessive genetic model and additive genetic model were used to estimate specific ORs and no significant association was found. For each SNP, statistical powers of dominant model, recessive model and additive model were evaluated, and additive model was found with highest statistical power in the study (data not shown). Thus, additive model was used for all the following association analysis.

4.3. Association analysis after stratification

Stratification analyses were performed by smoking status, histology subtypes and WHO grade. Subjects were grouped as never-, ever- and current-smokers as described above. Among never smokers, homozygote GG of STAT6 4610A/G showed an increased association with risk of glioma compared with AA (adjusted OR=1.691, 95% CI=1.152-2.481, p=0.007, corrected p=0.028) (Table 4). No significant association was observed among ever- and current-smokers. Table 5 summarized logistic regression analysis stratified by histology subtypes (Table 5). GG genotype of STAT6 4610A/G was a significant risk factor compared with AA in glioblastoma (adjusted OR=1.856, 95%CI= 1.153-2.987, p=0.011, corrected p=0.044). No significant association was found in astrocytomas or other gliomas including oligodendroglimas, enpendymomas and mixed glioma. In stratification analysis by WHO grade, GG genotype of STAT6 4610A/G was significantly related to increased WHO IV risk compared with AA (adjusted OR=1.591,95%CI=1.030-2.459, p=0.036, corrected p=0.144). Additive model showed similar results (adjusted OR=1.265, 95%CI=1.017-1.575, p=0.035, corrected p=0.140) (Table 6).

4.4. Haplotype analysis for STAT6

The two tag SNPs in STAT6 were considered in the same linkage disequilibrium block, three haplotypes composed of them were constructed and identified using the Haploview software (Table 7a). Haplotype frequencies were 0.498 for AA, 0.435 for AG and 0.067 for GG. Logistic regression analysis between haplotypes and glioma was summarized in Table 7 (Table 7), using most frequent haplotype Hap1 as reference. No significant association was observed between the haplotypes and glioma risk in all subjects.

We further performed stratification analysis according to smoking status, WHO grade, and histology. Among non-smokers, Hap 2 (A allele at SNP Rs1059513 and G allele at SNP Rs324015) was revealed to increase glioma risk significantly (OR=1.321, 95%CI= 1.081-1.614, p=0.007, corrected p=0.021). We did not observe significant association in WHO stratification analysis.

4.5. Gene-Gene interaction between IL-13 and IL-4Ra for susceptibility to glioma

The gene-gene interaction between IL-13 and IL-4Ra was analyzed between glioma and control subjects. Multivariate logistic regression analysis indicated the interaction effect of IL-13 Arg130Gln and IL-4Ra Gln576Arg might be protective factor for glioma risk (crude OR=0.730, 95%CI=0.537-0.993, p=0.045), and the effect was more significant than possessing the genotype at IL-13 Arg130Gln and IL-4Ra Gln576Arg individually (Table 8). Analysis among current-smokers showed similar results with adjusted OR=0.336 (95%CI=0.134-0.842, p=0.020). The interaction between IL-13 Arg130Gln and IL-4Ra Gln576Arg might be a protective factor for glioblastoma (crude OR=0.623, 95%CI=0.392-0.991, p=0.046), as well as a decreased factor for WHO grade III glioma risk significantly (adjusted OR=0.382, 95% CI=0.199-0.736, p=0.004).


Association of IL-4Ra Gln576Arg and IL-13 Arg130Gln in glioblastoma multiform (GBM) risk susceptibility was first conducted in a Swedish data set by Schwartzbaum et al. (23), with IL-4Ra Gln576Arg genotype AG and AA were positively associated with GBM compared with GG. However, the association was not confirmed in a larger study including more European countries (24). Meanwhile, a study conducted by Wiemels et al. in United States failed to find significant association between these two SNPs and glioma (25), and they state similar insignificant results in glioblastoma cases. The different results comparing with the Swedish study by Schwartzbaum et al might due to either small sample size (111 cases in Swedish study) or regional heterogeneity. In our study, no significant associations between IL-4Ra Gln576Arg and glioma risk in genotype regression analysis was observed, as well as IL-13 Arg130Gln. However, several lines of evidence suggest a role for IL-4 and IL-13 cytokines in glioma etiology. Both IL-4 and IL-13 play a central role in allergy by inducing IgE synthesis (31). Their binding to a shared receptor (IL-4Ra) induced activation of Janus tyrosine kinase (32, 33),which eventually activated STAT6 pathway to activate inducible crucial genes for IgE synthesis (19). In addition, they suppress cell proliferation in the normal astrocytic and low-grade astrocytoma cell lines (13, 34), possibly by blocking angiogenesis (35). In IL-13, 130Gln substitution results in phosphorylation of STAT6 in monocytes, decreased affinity of IL-13 for IL-13Ra2, and was neutralized less effectively by an IL-13Ra2 decoy (36). The Gln576Arg substitution in IL-4Ra occurs immediately adjacent to a tyrosine residue ,Y575, in the cytoplasmic domain and might alter the efficiency of interaction of Y575 with signal intermediates, which could lead to a dysregulation of IL-4 responses (37). Importantly, IL-4Ra polymorphisms might alter IL-13 responses since it is a component of the IL-13 receptor signal transduction system.

Although IL-4Ra and IL-13 were biological functionally interacted, the interaction analysis of IL-4Ra Gln576Arg and IL-13 Arg130Gln has not been reported yet. Thus we did interaction analysis between the two polymorphisms and observed several significant effects. The data suggested that the combination of IL-4Rα Arg and IL-13 Gln was less susceptible to glioma risk comparing with IL-4Rα Gln IL-13 Arg. The effect was enhanced comparing to the two polymorphisms individually, which suggested the interaction effect of these two candidate loci might be more important than single locus in glioma risk. As discussed above, IL-13 130Gln results in STAT6 phosphorylation and increases the combination of IL-13 to IL-4Ra by decreasing affinity of IL-13 for IL-13 Rα2, thus IL-4/IL-13 pathway is induced. On the other hand, IL-4Ra 576Arg enhanced signaling of IL-4R by increasing IL-4Ra combination with STAT6, which also induced IL-4/IL-13 pathway. Therefore, the substitutions of IL-4Ra Gln576Arg and IL-13 Arg130Gln come to the same effect to enhance IL-4 and IL-13 expression (Figure 1). In smoking status stratification, no significant association was indicated in never- or ever-smokers, but the interaction effect was significant in current-smokers. It seemed that smoking might be a part of susceptibility factors. Glioblastoma cells overexpressed IL-13Ra2 and unlike other glia, failed to phosphorylate STAT6 after IL-13 challenge (34). In histology subtypes, the interaction only significantly associated with decreased glioblastoma risk. IL-4 and IL-13 were showed to inhibit low grade gliomas and the effect had not been indicated in higher grade gliomas (34, 38). Our WHO grade stratification analysis indicated the interaction significantly related to less glioma risk in WHOIII, as well as boardline significance in decreasing glioma risk in WHOIV. We did not observe significant interaction in WHOI or WHOII subtypes.

As a part of the IL-4/IL-13 pathway, which is essential for IgE synthesis, genetic variants in the STAT6 gene has been identified to contribute to the regulation of total serum IgE levels (28). Even though the polymorphisms tested in our study were not located in the coding region, they were very likely to be functionally related to the regulation of serum IgE level. These two SNPs both located in the 3'UTR of STAT6, and that polymorphisms in 3'UTR of a gene might influence mRNA stability was shown previously (39).

STAT6 4219A/G has already been tested for association with atopic asthma in two different case-control studies in British and Japanese populations (40). Whereas strong association was found in the Japanese population (P=0.0043) but no significant association was seen in the British population. To our knowledge, our study is the first time to examine the STAT6 polymorphism and glioma risk. STAT6 4219A/G showed significant association with increased glioma susceptibility risk among never-smokers, but we failed to observe significant associations among ever- and current-smokers. Cigarette smoking is a plausible behavioral exposure that might modulate glioma risk, and no overall association had been reported among either men or women (41, 42). It is possible that the insignificant results in ever- and current- smokers of STAT6 4219A/G might attribute to relatively small number of subjects. Specific response of glioblastoma cells and higher grade glioma cells to IL-13 as we discussed above might explain inconsistent results in different histology and WHO subgroups. It may be speculated that in the case of polymorphism, STAT6 4219A/G, G allele decreased mRNA stability, thus reducing the amount of STAT6 mRNA readily available for translation into STAT6. Thereby, STAT6 might be more difficult to be recruited to the IL-4/IL-13 pathway after ligand binding. The activation of intracellular signaling cascade might be blocked and result in the phenomenon that lower IgE level was observed in glioma cells (10). Previous study conducted by Schedel et al. in Germany showed G allele in these polymorphisms significantly and consistently contributed to elevated total serum IgE levels (22). As to 4610A/G, no significant association in polymorphism STAT6 was found in our study.

Subjects recruited in our study were all genetically unrelated, thus all the subjects were included in haplotype construction and regression analysis. Only three haplotypes in our study population were constructed, and it might due to the low frequency of G allele in STAT6 4610A/G (0.069). Compared with most frequent haplotype AA (0.498), the other two haplotypes did not indicate significant association with glioma risk. Stratification results showed only in never-smokers, haplotype AG significantly increased susceptibility of glioma risk. This effect was not observed in other subgroups and might suggested that variant allele G of STAT6 4610A/G tended to have stronger effect in non-smokers of our study.

Although our study population is one of the largest reported glioma case-control data sets, it is still possible that the sample size was not large enough to identify significant differences in distribution of these SNPs between cases and controls. In spite of importance of IL-4/IL-13 pathway, we were unable to observe overall association of IL-4Ra, IL-13, and STAT6 SNPs in glioma, respectively. Possible reasons might include our failure to simultaneously consider other genes on the IL-4/IL-13 pathway or to identify the specific SNPs involved in glioma susceptibility on IL-4Ra, IL-13 and STAT6. It has been reported that at least six SNPs were required to characterize the variability of the IL-13 gene (43), whereas four were needed to represent that of IL-4Ra(44). We evaluated only one SNP in IL-4Ra and one SNP in IL-13. We did, however, found significant association between STAT6 4219A/G and disease risk after stratification and also a suggestion of an association of IL-4Ra and IL-13 interaction with glioma susceptibility risk. Haplotyple analysis of STAT6 showed potential relation to glioma among never-smokers. It is becoming clear that SNPs or even individual genes in isolation cannot represent their effect on disease completely, but rather whole genetic pathways must be considered and investigated simultaneously, as well as pathways that perform similar functions. The relative strength of the previous biological functional evidence suggested that further research is needed to evaluate the possible role of the entire IL-4/IL-13 pathway in glioma susceptibility risk.


Zhenchao Ruan and Yao Zhao contributed equally to this work. This research was supported by Shanghai Pujiang Project 08PJ1402200, Shanghai Leading Academic Discipline Project B111, Shanghai Subject Chief Scientist for Public Health 08GWD07 and Shanghai Key Subject Project for Public Health 08GWZX0301.The authors thank Yi Wang for providing advices for manuscript revision.


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Abbreviations: SNP, single nucleotide polymorphism; OR: odds ratio; CI: confidential interval; IL-13:Interleukin-13; IL-4Ra: Interleukin-4 receptor a; STAT6: signal transducer and activator of transcription 6; GBM: glioblastoma multiforme; PCR-LDR: Polymerase chain reaction-ligation detection reaction

Key Words: Glioma, single nucleotide polymorphism, IL-13, IL-4Ra, STAT6

Send correspondence to: Yan Huang, State Key Laboratory of Genetic Engineering, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, 200433, People's Republic of China, Tel: 86-21- 65642047, Fax: 86-21-65642502, E-mail: huangyan@fudan.edu.cn