Polymorphisms in genes hydroxysteroid-dehydrogenase-17b type 2 and type 4 and endometrial cancer risk

Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115, USA.
Gynecologic Oncology (Impact Factor: 3.69). 12/2010; 121(1):54-8. DOI: 10.1016/j.ygyno.2010.11.014
Source: PubMed

ABSTRACT Hydroxysteroid-dehydrogenase-17b (HSD17b) genes control the last step in estrogen biosynthesis. The isoenzymes HSD17b2 and HSD17b4 in the uterus preferentially catalyze the conversion of estradiol, the most potent and active form of estrogen, to estrone, the inactive form of estrogen. Endometrial adenocarcinoma is linked to excessive exposure to estrogens. We hypothesized that single nucleotide polymorphisms (SNPs) in genes HSD17b2 and HSD17b4 may alter the enzyme activity, estradiol levels and risk of disease.
Pairwise tag SNPs were selected from the HapMap Caucasian database to capture all known common (minor allele frequency >0.05) genetic variation with a correlation of at least 0.80. Forty-eight SNPs were genotyped in the case-control studies nested within the Nurses' Health Study (NHS) (cases=544, controls=1296) and the Women's Health Study (WHS) (cases=130, controls=389). The associations with endometrial cancer were examined using conditional logistic regression to estimate odds ratio and 95% confidence intervals adjusted for known risk factors. Results from the two studies were using fixed effects models. We additionally investigated whether SNPs are predictive of plasma estradiol and estrone levels in the NHS using linear regression.
Four intronic SNPs were significantly associated with endometrial cancer risk (p-value<0.05). After adjustment for multiple testing, we did not observe any significant associations between SNPs and endometrial cancer risk or plasma hormone levels.
This is the first study to comprehensively evaluate variation in HSD17b2 and HSD17b4 in relation to endometrial cancer risk. Our findings suggest that variation in HSD17b2 and HSD17b4 does not substantially influence the risk of endometrial cancer in Caucasians.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Background Altered networks of gene regulation underlie many complex conditions, including cancer. Inferring gene regulatory networks from high-throughput microarray expression data is a fundamental but challenging task in computational systems biology and its translation to genomic medicine. Although diverse computational and statistical approaches have been brought to bear on the gene regulatory network inference problem, their relative strengths and disadvantages remain poorly understood, largely because comparative analyses usually consider only small subsets of methods, use only synthetic data, and/or fail to adopt a common measure of inference quality. Methods We report a comprehensive comparative evaluation of nine state-of-the art gene regulatory network inference methods encompassing the main algorithmic approaches (mutual information, correlation, partial correlation, random forests, support vector machines) using 38 simulated datasets and empirical serous papillary ovarian adenocarcinoma expression-microarray data. We then apply the best-performing method to infer normal and cancer networks. We assess the druggability of the proteins encoded by our predicted target genes using the CancerResource and PharmGKB webtools and databases. Results We observe large differences in the accuracy with which these methods predict the underlying gene regulatory network depending on features of the data, network size, topology, experiment type, and parameter settings. Applying the best-performing method (the supervised method SIRENE) to the serous papillary ovarian adenocarcinoma dataset, we infer and rank regulatory interactions, some previously reported and others novel. For selected novel interactions we propose testable mechanistic models linking gene regulation to cancer. Using network analysis and visualization, we uncover cross-regulation of angiogenesis-specific genes through three key transcription factors in normal and cancer conditions. Druggabilty analysis of proteins encoded by the 10 highest-confidence target genes, and by 15 genes with differential regulation in normal and cancer conditions, reveals 75% to be potential drug targets. Conclusions Our study represents a concrete application of gene regulatory network inference to ovarian cancer, demonstrating the complete cycle of computational systems biology research, from genome-scale data analysis via network inference, evaluation of methods, to the generation of novel testable hypotheses, their prioritization for experimental validation, and discovery of potential drug targets.
    Genome Medicine 05/2012; 4(5):41. DOI:10.1186/gm340 · 4.94 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Tetrabromobisphenol A (TBBPA), a widely used flame retardant, caused uterine tumors in rats. In this study, TBBPA was administered to male and female Wistar Han rats and B6C3F1/N mice by oral gavage in corn oil for 2 years at doses up to 1,000 mg/kg. TBBPA induced uterine epithelial tumors including adenomas, adenocarcinomas, and malignant mixed Müllerian tumors (MMMTs). In addition, endometrial epithelial atypical hyperplasia occurred in TBBPA-treated rats. Also found to be related to TBBPA treatment, but at lower incidence and at a lower statistical significance, were testicular tumors in rats, and hepatic tumors, hemangiosarcomas (all organs), and intestinal tumors in male mice. It is hypothesized that the TBBPA uterine tumor carcinogenic mechanisms involve altered estrogen levels and/or oxidative damage. TBBPA treatment may affect hydroxysteroid-dehydrogenase-17β (HSD17β) and/or sulfotransferases, enzymes involved in estrogen homeostasis. Metabolism of TBBPA may also result in the formation of free radicals. The finding of TBBPA-mediated uterine cancer in rats is of concern because TBBPA exposure is widespread and endometrial tumors are a common malignancy in women. Further work is needed to understand TBBPA cancer mechanisms. © 2014 by The Author(s).
    Toxicologic Pathology 12/2014; DOI:10.1177/0192623314557335 · 1.92 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: To evaluate the association of HSD17B1 and HSD17B2 gene polymorphisms with uterine leiomyoma in Chinese women. 121 Chinese women with clinically diagnosed uterine leiomyoma and 217 healthy normal Chinese women were investigated to compare three single nucleotide polymorphisms (SNPs) (rs605059 and rs676387 of HSD17B1 gene and rs8191246 of HSD17B2 gene) by polymerase chain reaction-restriction fragment length polymorphism and DNA sequencing method. All the SNPs were polymorphisms in Chinese women. Frequencies of rs605059 AA genotype and A allele were significantly increased in patients with uterine leiomyoma compared to healthy controls (GG vs. AA, OR 0.40, 95 % CI 0.20-0.82; G vs. A, OR 0.68, 95 % CI 0.50-0.94). The results suggest that the genotype of HSD17B1 rs605059 may play a role in the tumourgenesis of uterine leiomyoma.
    Archives of Gynecology 05/2012; 286(3):701-5. DOI:10.1007/s00404-012-2328-0 · 1.28 Impact Factor

Full-text (2 Sources)

Available from
May 20, 2014