Single and Multigenic Analysis of the Association between Variants in 12 Steroid Hormone Metabolism Genes and Risk of Prostate Cancer
ABSTRACT To estimate the prostate cancer risk conferred by individual single nucleotide polymorphisms (SNPs), SNP-SNP interactions, and/or cumulative SNP effects, we evaluated the association between prostate cancer risk and the genetic variants of 12 key genes within the steroid hormone pathway (CYP17, HSD17B3, ESR1, SRD5A2, HSD3B1, HSD3B2, CYP19, CYP1A1, CYP1B1, CYP3A4, CYP27B1, and CYP24A1). A total of 116 tagged SNPs covering the group of genes were analyzed in 2,452 samples (886 cases and 1,566 controls) in three ethnic/racial groups. Several SNPs within CYP19 were significantly associated with prostate cancer in all three ethnicities (P = 0.001-0.009). Genetic variants within HSD3B2 and CYP24A1 conferred increased risk of prostate cancer in non-Hispanic or Hispanic Caucasians. A significant gene-dosage effect for increasing numbers of potential high-risk genotypes was found in non-Hispanic and Hispanic Caucasians. Higher-order interactions showed a seven-SNP interaction involving HSD17B3, CYP19, and CYP24A1 in Hispanic Caucasians (P = 0.001). In African Americans, a 10-locus model, with SNPs located within SRD5A2, HSD17B3, CYP17, CYP27B1, CYP19, and CYP24A1, showed a significant interaction (P = 0.014). In non-Hispanic Caucasians, an interaction of four SNPs in HSD3B2, HSD17B3, and CYP19 was found (P < 0.001). These data are consistent with a polygenic model of prostate cancer, indicating that multiple interacting genes of the steroid hormone pathway confer increased risk of prostate cancer.
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ABSTRACT: Steroid 5-alpha reductase type 2 (SRD5A2) modifies testosterone to dihydrotestosterone (DHT) in the prostate. Single-nucleotide polymorphisms (SNPs) of the SRD5A2 gene might affect DHT. We sought to understand the relationship of SRD5A2 SNPs to prostate cancer in the Korean population. Twenty-six common SNPs in the SRD5A2 gene were assessed in 272 prostate cancer cases and 173 controls. Single-locus analyses were conducted by using conditional logistic regression. Additionally, we performed a haplotype analysis for the SRD5A2 SNPs tested. Among the 20 SNPs and 4 haplotypes, there were no statistically significant results in the prostate cancer patients and the controls. In the logistic analysis of SRD5A2 polymorphisms with prostate-specific antigen (PSA) criteria, two SNPs (rs508562, rs11675297) and haplotype 1 displayed significant results (odds ratio [OR], 1.76; p=0.05; OR, 1.88-2.02; p=0.01-0.04; OR, 0.59; p=0.02, respectively). rs508562, rs11675297, rs2208532, and haplotype 1 (OR, 1.49; p=0.05; OR, 2.02; p=0.05; OR, 2.01; p=0.04; OR, 0.56-0.64, p=0.03-0.04, respectively) had significant associations with Gleason score. rs508562, rs11675297, and haplotype 1 (OR, 1.41-2.34; p=0.004-0.05; OR, 1.74-1.82; p=0.03-0.05; OR, 0.42-0.67; p=0.0005-0.03, respectively) were significantly associated with clinical stage. We conclude that there was no significant association between SRD5A2 SNPs and the risk of prostate cancer in the Korean population. However, we found that some SNPs and 1 haplotype influenced PSA level, Gleason score, and clinical stage.Korean journal of urology 01/2015; 56(1):19-30. DOI:10.4111/kju.2015.56.1.19
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ABSTRACT: MicroRNAs (miRNAs) are important regulators of eukaryotic gene expression. They have been implicated in a broad range of biological processes, and miRNA-related genetic alterations probably underlie several human diseases. Single nucleotide polymorphisms of transcripts may modulate the posttranscriptional regulation of gene expression by miRNAs and explain interindividual variability in cancer risk and in chemotherapy response. On the basis of recent association studies published in the literature, the present review mainly summarizes the potential role of miRNAs as molecular biomarkers for disease susceptibility, diagnosis, prognosis, and drug-response prediction in tumors. Many clues suggest a role for polymorphisms within the 3' untranslated regions of KRAS rs61764370, SET8 rs16917496, and MDM4 rs4245739 as SNPs in miRNA binding sites highly promising in the biology of human cancer. However, more studies are needed to better characterize the composite spectrum of genetic determinants for future use of markers in risk prediction and clinical management of diseases, heading toward personalized medicine.Pharmacogenomics and Personalized Medicine 07/2014; 7:173-191. DOI:10.2147/PGPM.S61693
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ABSTRACT: Background: A personalized medicine approach provides opportunities for predictive and preventive medicine. Using genomic, clinical, environmental, and behavioral data, the tracking and management of individual wellness is possible. A prolific way to carry this personalized approach into routine practices can be accomplished by integrating clinical interpretations of genomic variations into electronic medical records (EMRs)/electronic health records (EHRs). Today, various central EHR infrastructures have been constituted in many countries of the world, including Turkey. Objective: As an initial attempt to develop a sophisticated infrastructure, we have concentrated on incorporating the personal single nucleotide polymorphism (SNP) data into the National Health Information System of Turkey (NHIS-T) for disease risk assessment, and evaluated the performance of various predictive models for prostate cancer cases. We present our work as a three part miniseries: (1) an overview of requirements, (2) the incorporation of SNP data into the NHIS-T, and (3) an evaluation of SNP data incorporated into the NHIS-T for prostate cancer. Methods: In the third article of this miniseries, we have evaluated the proposed complementary capabilities (ie, knowledge base and end-user application) with real data. Before the evaluation phase, clinicogenomic associations about increased prostate cancer risk were extracted from knowledge sources, and published predictive genomic models assessing individual prostate cancer risk were collected. To evaluate complementary capabilities, we also gathered personal SNP data of four prostate cancer cases and fifteen controls. Using these data files, we compared various independent and model-based, prostate cancer risk assessment approaches. Results: Through the extraction and selection processes of SNP-prostate cancer risk associations, we collected 209 independent associations for increased risk of prostate cancer from the studied knowledge sources. Also, we gathered six cumulative models and two probabilistic models. Cumulative models and assessment of independent associations did not have impressive results. There was one of the probabilistic, model-based interpretation that was successful compared to the others. In envirobehavioral and clinical evaluations, we found that some of the comorbidities, especially, would be useful to evaluate disease risk. Even though we had a very limited dataset, a comparison of performances of different disease models and their implementation with real data as use case scenarios helped us to gain deeper insight into the proposed architecture. Conclusions: In order to benefit from genomic variation data, existing EHR/EMR systems must be constructed with the capability of tracking and monitoring all aspects of personal health status (genomic, clinical, environmental, etc) in 24/7 situations, and also with the capability of suggesting evidence-based recommendations. A national-level, accredited knowledge base is a top requirement for improved end-user systems interpreting these parameters. Finally, categorization using similar, individual characteristics (SNP patterns, exposure history, etc) may be an effective way to predict disease risks, but this approach needs to be concretized and supported with new studies.08/2014; 2(2):e21. DOI:10.2196/medinform.3560