Dataset Information

CD44 rs13347 C>T polymorphism predicts breast cancer risk and prognosis in Chinese populations.

ABSTRACT: INTRODUCTION: It has been demonstrated that the interplay of adhesion molecule CD44 and its ligands can regulate cancer cell proliferation, migration and invasion, as well as tumor-associated angiogenesis and is related to breast cancer patient survival. In this two-stage, case control study, we determined whether common functional tagSNPs (single nucleotide polymorphisms) are associated with breast cancer risk and prognosis. METHODS: Five tagSNPs of CD44 (rs10836347C>T, rs13347C>T, rs1425802A>G, rs11821102G>A, rs713330T>C) were selected and genotyped in 1,853 breast cancer patients and 1,992 healthy control subjects in Eastern and Southern populations. Potential function of rs13347C>T and association between this variation and breast cancer were further studied. RESULTS: Compared with the most common rs13347CC genotype, variant genotypes (CT and TT) increased an individual's susceptibility to breast cancer, especially in estrogen receptor (ER) negative patients (odds ratio (OR) = 1.37, 95%CI = 1.17 to 1.59 for ER positive patients; OR = 2.37, 95% CI = 2.00 to 2.80 for ER negative patients). We also found that rs13347CT+ TT genotypes predicts lower five-year survival rate (hazard ratio (HR) = 1.85, 95% CI = 1.09 to 3.15, P = 0.023), with the lowest survival probability in ER negative T allele carriers. Furthermore, our reporter assay findings, although preliminary and rather modest, showed that miR-509-3p may suppress CD44 expression more strongly in C allele carriers than T allele carriers (P < 0.01). Similarly, rs13347 variant genotypes (CT and TT) carriers were shown to have more CD44 expression than CC carriers in both immunohistochemistry (P < 0.001) and western blotting (P = 0.001) results. CONCLUSION: These findings suggest that CD44 rs13347C>T polymorphism may affect breast cancer development and prognosis by increasing CD44 expression.

SUBMITTER: Jiang L

PROVIDER: S-EPMC3680922 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

CD44 rs13347 C>T polymorphism predicts breast cancer risk and prognosis in Chinese populations.

Jiang Lan L Deng Jieqiong J Zhu Xun X Zheng Jian J You Yonghe Y Li Na N Wu Hongchun H Lu Jiachun J Zhou Yifeng Y

Breast cancer research : BCR 20120712 4

<h4>Introduction</h4>It has been demonstrated that the interplay of adhesion molecule CD44 and its ligands can regulate cancer cell proliferation, migration and invasion, as well as tumor-associated angiogenesis and is related to breast cancer patient survival. In this two-stage, case control study, we determined whether common functional tagSNPs (single nucleotide polymorphisms) are associated with breast cancer risk and prognosis.<h4>Methods</h4>Five tagSNPs of CD44 (rs10836347C>T, rs13347C>T, ...[more]

PMID: 22788972

Similar Datasets

Project description:BACKGROUND: Cell surface biomarker CD44 plays an important role in breast cancer cell growth, differentiation, invasion, angiogenesis and tumour metastasis. Therefore, we aimed to investigate the role of CD44 gene polymorphisms in breast cancer risk and prognosis in North Indian population. MATERIALS & METHODS: A total of 258 breast cancer patients and 241 healthy controls were included in the case-control study for risk prediction. According to RECIST, 114 patients who received neo-adjuvant chemotherapy were recruited for the evaluation of breast cancer prognosis. We examined the association of tagging SNP (rs353639) of Hapmap Gujrati Indians in Houston (GIH population) in CD44 gene along with a significant reported SNP (rs13347) in Chinese population by genotyping using Taqman allelic discrimination assays. Statistical analysis was done using SPSS software, version 17. In-silico analysis for prediction of functional effects was done using F-SNP and FAST-SNP. RESULTS: No significant association of both the genetic variants of the CD44 gene polymorphisms was found with breast cancer risk. On performing univariate analysis with clinicopathological characteristics and treatment response, we found significant association of genotype (CT+TT) of rs13347 polymorphism with earlier age of onset (P = 0.029, OR = 0.037). However, significance was lost in multivariate analysis. For rs353639 polymorphism, significant association was seen with clinical tumour size, both at the genotypic (AC+CC) (P = 0.039, OR = 3.02) as well as the allelic (C) (P = 0.042, OR = 2.87) levels. On performing multivariate analysis, increased significance of variant genotype (P = 0.017, OR = 4.29) and allele (P = 0.025, OR = 3.34) of rs353639 was found with clinical tumour size. In-silico analysis using F-SNP, showed altered transcriptional regulation for rs353639 polymorphism. CONCLUSIONS: These findings suggest that CD44 rs353639 genetic variants may have significant effect in breast cancer prognosis. However, both the polymorphisms- rs13347 and rs353639 had no effect on breast cancer susceptibility.

Project description:BackgroundChanges in gene expression are associated with malignancy. Analysis of gene expression data could be used to reveal cancer subtypes, key molecular drivers, and prognostic characteristics and to predict cancer susceptibility, treatment response, and mortality. It has been reported that inflammation plays an important role in the occurrence and development of tumors. Our aim was to establish a risk signature model of breast cancer with inflammation-related genes (IRGs) to evaluate their survival prognosis.MethodsWe downloaded 200 IRGs from the Molecular Signatures Database (MSigDB). The data of breast cancer were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Differential gene expression analysis, the least absolute shrinkage and selection operator (LASSO), Cox regression analysis, and overall survival (OS) analysis were used to construct a multiple-IRG risk signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were carried out to annotate functions of the differentially expressed IRGs (DEIRGs) The predictive accuracy of the prognostic model was evaluated by time-dependent receiver operating characteristic (ROC) curves. Subsequently, nomograms were constructed to guide clinical application according to the univariate and multivariate Cox proportional hazards regression analyses. Eventually, we applied gene set variation analysis (GSVA), mutation analysis, immune infiltration analysis, and drug response analysis to compare the differences between high- and low-risk patients.ResultsTotally, 65 DEIRGs were obtained after comparing 1,092 breast cancer tissues with 113 paracancerous tissues in TCGA. Among them, 11 IRGs (IL18, IL12B, RASGRP1, HPN, CLEC5A, SCARF1, TACR3, VIP, CCL2, CALCRL, ABCA1) were screened with nonzero coefficient by LASSO regression analysis to construct the prognostic model, which was validated in GSE96058.The 11-gene IRGs risk signature model stratified patients into high- or low-risk groups, with those in the low-risk group having longer survival time and less deaths. Multivariate Cox analysis manifested that risk score, age, and stage were the three independent prognostic factors for breast cancer patients. There were 12 pathways with higher activities and 24 pathways with lower activities in the high-risk group compared with the low-risk group, yet no difference of gene mutation load was observed between the two groups. In immune infiltration analysis, we noted that the proportion of T cells showed a decreased trend according to the increase of risk score and most of the immune cells were enriched in the low-risk group. Inversely, macrophages M2 were more highly distributed in the high-risk group. We identified 67 approved drugs that showed a different effect between the high- and low-risk patients and the top 2 gene-drug pairs were IL12B-sunitinib and SCARF1-ruxolitinib.ConclusionsThe 11-IRG risk signature model is a promising tool to predict the survival of breast cancer patients and the expressions of IL12B and SCARF1 may serve as potential targets for therapy of breast cancer.

Project description:Breast cancer is one of the most widespread malignancies in females, and the incidence rate has been increasing in recent years in the world. Genetic factors play an important role in the occurrence of breast cancer. Our study aimed to investigate the relationship between single nucleotide polymorphisms (SNPs) of claudin 10 (CLDN10) (rs1325774, rs7333503, rs3751334) and breast cancer and the clinical characteristics of patients.A total of 104 patients with breast cancer and 118 healthy controls were recruited in our study between 2013 and 2015. The SNPscan system was used for genotyping. Demographic information, health status, anthropometric parameters and clinical data were considered in analysis. Statistical analysis of the data was carried out using the Student t test, the Chi-square test (X2) or Fisher exact test and unconditional logistic regression analysis. The Gene Expression Profiling Interactive Analyses application (GEPIA) was used to analysis the expression of CLDN10 in breast cancer tissues and normal samples.The polymorphism of rs1325774 was significantly associated with an increased risk of breast cancer (T/G vs T/T: OR = 2.073, 95% CI = 1.095-3.927, P = .025). After adjusting for age, the association remained statically significant (T/G vs T/T: OR = 2.067, 95% CI = 1.070-3.867, P = .026). Furthermore, harbouring G allele in rs1325774 position was significantly associated with increased risk of breast cancer (OR = 1.993, 95% CI = 1.107-3.589, P = .022). However, no significant association among rs7333503, rs3751334, and breast cancer. The expression level of CLDN10 was reduced in breast cancer tissues compared with normal breast tissues according to the analysis of The Cancer Genome Atlas (TCGA) data through GEPIA.Our results suggest that the polymorphism of rs1325774 associate with increase the breast cancer risk. No significant relationship between rs1325774 polymorphism and clinical as well as pathological characteristics in patients.

Dataset Information

CD44 rs13347 C>T polymorphism predicts breast cancer risk and prognosis in Chinese populations.

Publications

CD44 rs13347 C>T polymorphism predicts breast cancer risk and prognosis in Chinese populations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets