Examination of polymorphic glutathione S-transferase (GST) genes, tobacco smoking and prostate cancer risk among Men of African Descent: A case-control study
1 Department of Pharmacology & Toxicology, University of Louisville (UofL), School of Medicine, 500 South Preston Street, Room 1319 Research Tower, UofL Health Science Center, Louisville, KY 40202, USA
2 James Graham Brown Cancer Center, Cancer Prevention & Control Program, UofL, 529 South Jackson Street, Louisville, KY 40202, USA
3 Department of Bioinformatics & Biostatistics, UofL School of Public Health and Information Sciences, 485 East Gray Street, Louisville, KY 40202, USA
4 Center for Genetics and Molecular Medicine, UofL School of Medicine, Delia B. Baxter Biomedical Research Building, Suite 221, Louisville, Kentucky 40202, USA
5 Center for Environmental Genomics and Integrative Biology, UofL, Delia B. Baxter Biomedical Research Building, Suite 221, Louisville, Kentucky 40202, USA
6 Department of Medicine, University of Chicago, 5841 S. Maryland Ave, MC Chicago, Illinois 606037, USA
7 Dartmouth -Hitchcock Medical Center, Dartmouth Medical School, 706 Rubin Building, HB 7937 One, Medical Center Drive, Dartmouth-Hitchcock Medical Center Lebanon, NH 03756, USA
8 Department of Epidemiology and Public Health, UofL School of Public Health and Information Sciences, 485 East Gray Street, Louisville, KY 40202, USA
BMC Cancer 2009, 9:397 doi:10.1186/1471-2407-9-397Published: 16 November 2009
Polymorphisms in glutathione S-transferase (GST) genes may influence response to oxidative stress and modify prostate cancer (PCA) susceptibility. These enzymes generally detoxify endogenous and exogenous agents, but also participate in the activation and inactivation of oxidative metabolites that may contribute to PCA development. Genetic variations within selected GST genes may influence PCA risk following exposure to carcinogen compounds found in cigarette smoke and decreased the ability to detoxify them. Thus, we evaluated the effects of polymorphic GSTs (M1, T1, and P1) alone and combined with cigarette smoking on PCA susceptibility.
In order to evaluate the effects of GST polymorphisms in relation to PCA risk, we used TaqMan allelic discrimination assays along with a multi-faceted statistical strategy involving conventional and advanced statistical methodologies (e.g., Multifactor Dimensionality Reduction and Interaction Graphs). Genetic profiles collected from 873 men of African-descent (208 cases and 665 controls) were utilized to systematically evaluate the single and joint modifying effects of GSTM1 and GSTT1 gene deletions, GSTP1 105 Val and cigarette smoking on PCA risk.
We observed a moderately significant association between risk among men possessing at least one variant GSTP1 105 Val allele (OR = 1.56; 95%CI = 0.95-2.58; p = 0.049), which was confirmed by MDR permutation testing (p = 0.001). We did not observe any significant single gene effects among GSTM1 (OR = 1.08; 95%CI = 0.65-1.82; p = 0.718) and GSTT1 (OR = 1.15; 95%CI = 0.66-2.02; p = 0.622) on PCA risk among all subjects. Although the GSTM1-GSTP1 pairwise combination was selected as the best two factor LR and MDR models (p = 0.01), assessment of the hierarchical entropy graph suggested that the observed synergistic effect was primarily driven by the GSTP1 Val marker. Notably, the GSTM1-GSTP1 axis did not provide additional information gain when compared to either loci alone based on a hierarchical entropy algorithm and graph. Smoking status did not significantly modify the relationship between the GST SNPs and PCA.
A moderately significant association was observed between PCA risk and men possessing at least one variant GSTP1 105 Val allele (p = 0.049) among men of African descent. We also observed a 2.1-fold increase in PCA risk associated with men possessing the GSTP1 (Val/Val) and GSTM1 (*1/*1 + *1/*0) alleles. MDR analysis validated these findings; detecting GSTP1 105 Val (p = 0.001) as the best single factor for predicting PCA risk. Our findings emphasize the importance of utilizing a combination of traditional and advanced statistical tools to identify and validate single gene and multi-locus interactions in relation to cancer susceptibility.