Polymorphisms in the UBC9 and PIAS3 genes of the SUMO-conjugating system and breast cancer risk
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SUMOylation consists in the covalent conjugation of small ubiquitin-related modifiers to target proteins. SUMOylation participates in processes that are tightly linked to tumorigenesis, and genetic variability in the SUMO-conjugating system may influence the development of breast cancer. We recently reported that variation in the UBC9 gene encoding the SUMO-conjugating enzyme may affect the grade of breast tumors. Following comprehensive in silico analyses for detection of putative functional polymorphisms in 14 genes of the SUMO system, we selected one coding SNP in PIAS3 and seven tag SNPs in UBC9 for association analyses. Results were based on 1,021 cases, and 1,015 matched controls from the population-based GENICA study. Odds ratios (OR) and 95% confidence intervals (CI) were estimated by conditional logistic regression. To explore the association with polymorphisms closely linked to the genotyped variants, multiple imputation based on HapMap data was applied. The study revealed associations of four UBC9 polymorphisms with risk of grade 1 tumors. Comparison of genotype and haplotype models indicated that the best representation of risk solely relied on rs7187167 under dominant penetrance. Women carrying the rare allele showed an increased risk of grade 1 tumors compared with common homozygotes (OR 1.87, 95% CI 1.18–2.95). This effect appeared to be stronger in women with a family history of breast or ovarian cancer. Imputation of polymorphisms in a 300-kb region around the genotyped polymorphisms identified no variants with stronger associations. Our findings suggest that genetic variation in UBC9 may affect the risk of grade 1 breast tumors.
KeywordsUBC9 and PIAS3 polymorphisms SUMOylation Breast cancer risk Tumor grade Multiple imputation
We are indebted to all women participating in the GENICA study. We gratefully acknowledge support by interviewers as well as physicians and pathologists of the study region. We thank Axel Benner for his contribution to the statistical analysis and Agnes Hotz-Wagenblatt as well as Karl-Heinz Glatting for their support in using the software PromoterSweep. Further gratitude goes to Antje Seidel-Renkert for expert technical assistance. This work was supported by the Federal Ministry of Education and Research (BMBF) Germany grants 01KW9976/8, 01KW9975/5, 01KW9977/0 and 01KW0114, the Deutsches Krebsforschungszentrum, Heidelberg, the Robert Bosch Foundation of Medical Research, Stuttgart, BGFA-Forschungsinstitut für Arbeitsmedizin der Deutschen Gesetzlichen Unfallversicherung, Bochum, and the Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany.
The authors declare that they have no competing interests.
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