Dataset Information

A miRNA binding site single-nucleotide polymorphism in the 3'-UTR region of the IL23R gene is associated with breast cancer.

ABSTRACT:

Background

Research into the etiology of breast cancer has recently focused on the role of the immunity and inflammation. Interleukin-23 and its receptor (IL23R) guide T cells towards the Th17 phenotype. IL23R single nucleotide polymorphisms (SNPs) have been shown to be associated with digestive system cancers. To evaluate the influences of IL23R gene polymorphisms on the risk of sporadic breast cancer, a case-control study was conducted in Chinese Han women.

Methodology and principal findings

We genotyped two tag SNPs (rs10889677 in the 3'-UTR region and nonsynonymous variants rs1884444 in exon 2) in IL23R gene of 491 breast cancer patients and 502 matched healthy controls. The genotypes were determined using the SNaPshot technique. The differences in the genotypic distribution between breast cancer patients and healthy controls were analyzed with the Chi-square test for trends. For rs10889677 in IL23R, the frequencies of the AA genotype and the A allele were statistical significant higher in breast cancer patients than in controls (P?=?0.0084 and P?=?0.0171, respectively), whereas the C allele was associated with an earlier age of breast cancer onset (50.6 years for AA, 48.7 years for AC and 46.0 years for CC (P?=?0.0114)) in case-only study. The clinical features analysis demonstrated significant associations between rs1884444 in IL23R and human epidermal growth factor receptor 2 (Her-2) and tumor size status.

Conclusions and significance

Our results suggest that a miRNA binding site SNP in the 3'-UTR region of the IL23R gene may be associated with the risk of breast cancer and contribute to the early development of breast cancer in Chinese women.

SUBMITTER: Wang L

PROVIDER: S-EPMC3519811 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Publications

A miRNA binding site single-nucleotide polymorphism in the 3'-UTR region of the IL23R gene is associated with breast cancer.

Wang Lihong L Liu Wei W Jiang Wei W Lin Jing J Jiang Yongdong Y Li Bo B Pang Da D

PloS one 20121211 12

<h4>Background</h4>Research into the etiology of breast cancer has recently focused on the role of the immunity and inflammation. Interleukin-23 and its receptor (IL23R) guide T cells towards the Th17 phenotype. IL23R single nucleotide polymorphisms (SNPs) have been shown to be associated with digestive system cancers. To evaluate the influences of IL23R gene polymorphisms on the risk of sporadic breast cancer, a case-control study was conducted in Chinese Han women.<h4>Methodology and principal ...[more]

PMID: 23239971

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Project description:PURPOSE:Numerous clinical studies have suggested that chemopreventive drugs for breast cancer such as tamoxifen and exemestane can effectively reduce the incidence of estrogen receptor (ER)-positive breast cancer. However, it remains unclear how to identify those who are susceptible to ER-positive breast cancer. Accordingly, there is a great demand for a probe into the predisposing factors so as to provide precise chemoprevention. Recent evidence has indicated that ER? expression can be regulated by microRNAs (miRNAs), such as miR-206, in breast cancer. We assumed that single-nucleotide polymorphisms (SNPs) in the miR-206-binding sites of the target genes may be associated with breast cancer susceptibility with different ER statuses. METHODS:We genotyped the SNPs that reside in and around the miR-206-binding sites of two target genes - heparan sulfatase 1 (SULF1) and RPTOR-independent companion of mammalian target of rapamycin Complex 2 (RICTOR) - which were related to the progression or metastasis of breast cancer cells in 710 breast cancer patients and 294 controls by the matrix-assisted laser desorption ionization-time of flight mass spectrometry method. Modified odds ratios (ORs) with their 95% confidence intervals (CIs) were calculated by a multivariate logistic regression analysis to evaluate the potential association between the SNPs and breast cancer susceptibility. RESULTS:For rs3802278, which is located in the 3'-untranslated region (3'-UTR) of SULF1, the frequency of the AA genotype was less in breast cancer patients than that in the controls as compared to that of the GG + GA genotype not only for ER-positive breast cancer patients (adjusted OR =0.663, P=0.032) but also for hormone receptor-positive breast cancer patients (adjusted OR =0.610, P=0.018). Besides, the frequency of the AA genotype was less than that of the GG genotype between the ER-positive breast cancer patients and the controls (adjusted OR =0.791, P=0.038). For rs66916453, which is located in the 3'-UTR of RICTOR, no significant difference was observed between the case and the control group for the genotypes or alleles (P>0.05). CONCLUSION:The SNPs in the miRNA-binding sites within the 3'-UTR of SULF1 may serve as protective factors against the susceptibility to breast cancer, especially to ER-positive breast cancer in the Chinese population. These SNPs are promising candidate biomarkers to predict the susceptibility of breast cancer and guide the administration of targeted preventive endocrine therapy.

Project description:BACKGROUND: This paper introduces and applies a genome wide predictive study to learn a model that predicts whether a new subject will develop breast cancer or not, based on her SNP profile. RESULTS: We first genotyped 696 female subjects (348 breast cancer cases and 348 apparently healthy controls), predominantly of Caucasian origin from Alberta, Canada using Affymetrix Human SNP 6.0 arrays. Then, we applied EIGENSTRAT population stratification correction method to remove 73 subjects not belonging to the Caucasian population. Then, we filtered any SNP that had any missing calls, whose genotype frequency was deviated from Hardy-Weinberg equilibrium, or whose minor allele frequency was less than 5%. Finally, we applied a combination of MeanDiff feature selection method and KNN learning method to this filtered dataset to produce a breast cancer prediction model. LOOCV accuracy of this classifier is 59.55%. Random permutation tests show that this result is significantly better than the baseline accuracy of 51.52%. Sensitivity analysis shows that the classifier is fairly robust to the number of MeanDiff-selected SNPs. External validation on the CGEMS breast cancer dataset, the only other publicly available breast cancer dataset, shows that this combination of MeanDiff and KNN leads to a LOOCV accuracy of 60.25%, which is significantly better than its baseline of 50.06%. We then considered a dozen different combinations of feature selection and learning method, but found that none of these combinations produces a better predictive model than our model. We also considered various biological feature selection methods like selecting SNPs reported in recent genome wide association studies to be associated with breast cancer, selecting SNPs in genes associated with KEGG cancer pathways, or selecting SNPs associated with breast cancer in the F-SNP database to produce predictive models, but again found that none of these models achieved accuracy better than baseline. CONCLUSIONS: We anticipate producing more accurate breast cancer prediction models by recruiting more study subjects, providing more accurate labelling of phenotypes (to accommodate the heterogeneity of breast cancer), measuring other genomic alterations such as point mutations and copy number variations, and incorporating non-genetic information about subjects such as environmental and lifestyle factors.

Dataset Information

A miRNA binding site single-nucleotide polymorphism in the 3'-UTR region of the IL23R gene is associated with breast cancer.

Background

Methodology and principal findings

Conclusions and significance

Publications

A miRNA binding site single-nucleotide polymorphism in the 3'-UTR region of the IL23R gene is associated with breast cancer.

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