[Using MSR model to analyze the impact of gene-gene interaction with related to the genetic polymorphism of metabolism enzymes on the risk of breast cancer].
ABSTRACT To identify the interactions of susceptive genes with related to the genetic polymorphism of metabolism enzymes (CYP1A1, GSTT1 and GSTM1) and their impacts on the risk of breast cancer; and to test the feasibility of using Multifactor Dimensionality Reduction (MDR) model in analyzing gene-gene interactions.
A paired case-control study, matched by age and menstruate state, was conducted. From December 2003 to September 2004, 78 pairs of people with and without breast cancers were investigated. The variant genotypes of CYP1A1 Msp I and GSTT1/M1 were identified by PCR-RFLP and multiplex PCR assays. The gene-gene interactions were analyzed with the MDR model. Based on the result of the MDR model, a conditional logistic regression model was constructed as the final cause-effect interpretative model.
The interaction between CYP1A1 Msp I variant genotype (vv) and GSTT1 null genotype gave the best MDR model with statistical significance (Sign Test, P = 0.05). The model Testing Balance Accuracy was 0. 5920. The Cross-Validation consistency was 10/10. The final conditional logistic regression based on the MDR model showed that passive smoking, abortion and gene-gene interaction were risks of breast cancers, with an OR (95% confidence interval) of 12.234 (1.7459-85.7279), 4.554 (1.3250-15.6507) and 9.597 (1.5783-58.3599), respectively.
The MDR model may be an effective method for estimating risks of breast cancers due to gene-gene and gene-environment interactions. The gene-gene interaction with related to the genetic polymorphism of metabolism enzymes (CYP1A1 and GSTT1) may increase the risk of breast cancer by disturbing the metabolism of estrogen.
Article: Four polymorphisms in cytochrome P450 1A1 (CYP1A1) gene and breast cancer risk: a meta-analysis.[show abstract] [hide abstract]
ABSTRACT: Cytochrome P450s are enzymes which catalyze Phase-I metabolism reactions; cytochrome P450 1A1 (CYP1A1) is a member of the CYP1 family and participates in the metabolism of a vast number of xenobiotics, as well as endogenous substrates. Four single nucleotide polymorphisms in CYP1A1 have been studied concerning their potential implication in terms of breast cancer risk: T3801C, T3205C, A2455G (Ile462Val), and C2453A (Thr461Asp); controversy exists regarding their role. This meta-analysis aims to examine whether the four aforementioned polymorphisms are associated with breast cancer risk. Separate analyses were performed on Caucasian, Chinese, and African populations, as well as on premenopausal and postmenopausal women. Eligible articles were identified by a search of MEDLINE bibliographical database for the period up to October 2009. Concerning T3801C, 32 studies were eligible (11,909 cases and 16,179 controls), 29 studies (12,257 cases and 20,379 controls) were eligible for A2455G, 11 studies (7,189 cases and 8,491 controls) were eligible for C2453A, and eight studies were eligible for T3205C (1,378 cases and 1,642 controls). Pooled odds ratios (OR) were appropriately derived from fixed- or random-effect models. Sensitivity analysis excluding studies whose genotype frequencies in controls significantly deviated from Hardy-Weinberg equilibrium was performed. Homozygous subjects of Caucasian origin carrying the A2455G G allele exhibited elevated breast cancer risk (pooled OR = 2.185, 95% CI 1.253-3.808, fixed effects), whereas heterozygous carriers did not (pooled OR = 1.062, 95% CI 0.852-1.323, random effects). A2455G polymorphism status was not associated with breast cancer risk in Chinese subjects or specifically in premenopausal/postmenopausal women. T3801C, T3205C, and C2453A status were not associated with breast cancer risk at any analysis. In conclusion, this meta-analysis points to the A2455G G allele as a risk factor for breast cancer among Caucasian subjects. On the contrary, T3801C, T3205C, and C2453A status does not seem capable of modifying breast cancer risk.Breast Cancer Research and Treatment 07/2010; 122(2):459-69. · 4.43 Impact Factor