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Sökning: WFRF:(Cannon Albright Lisa A) > (2015-2019)

  • Resultat 11-15 av 15
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11.
  • Adams, Charleen, et al. (författare)
  • Circulating Metabolic Biomarkers of Screen-Detected Prostate Cancer in the ProtecT Study.
  • 2018
  • Ingår i: Cancer Epidemiology, Biomarkers and Prevention. - 1055-9965 .- 1538-7755.
  • Tidskriftsartikel (refereegranskat)abstract
    • BACKGROUND: Whether associations between circulating metabolites and prostate cancer are causal is unknown. We report on the largest study of metabolites and prostate cancer (2,291 cases and 2,661 controls) and appraise causality for a subset of the prostate cancer-metabolite associations using two-sample Mendelian randomization (MR).MATERIALS AND METHODS: The case-control portion of the study was conducted in nine UK centres with men aged 50-69 years who underwent prostate-specific antigen (PSA) screening for prostate cancer within the Prostate testing for cancer and Treatment (ProtecT) trial. Two data sources were used to appraise causality: a genome-wide association study (GWAS) of metabolites in 24,925 participants and a GWAS of prostate cancer in 44,825 cases and 27,904 controls within the Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium.RESULTS: Thirty-five metabolites were strongly associated with prostate cancer (p <0.0014, multiple-testing threshold). These fell into four classes: i) lipids and lipoprotein subclass characteristics (total cholesterol and ratios, cholesterol esters and ratios, free cholesterol and ratios, phospholipids and ratios, and triglyceride ratios); ii) fatty acids and ratios; iii) amino acids; iv) and fluid balance. Fourteen top metabolites were proxied by genetic variables, but MR indicated these were not causal.CONCLUSIONS: We identified 35 circulating metabolites associated with prostate cancer presence, but found no evidence of causality for those 14 testable with MR. Thus, the 14 MR-tested metabolites are unlikely to be mechanistically important in prostate cancer risk.IMPACT: The metabolome provides a promising set of biomarkers that may aid prostate cancer classification.
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12.
  • Matejcic, Marco, et al. (författare)
  • Germline variation at 8q24 and prostate cancer risk in men of European ancestry
  • 2018
  • Ingår i: Nature Communications. - 2041-1723 .- 2041-1723. ; 9
  • Tidskriftsartikel (refereegranskat)abstract
    • Chromosome 8q24 is a susceptibility locus for multiple cancers, including prostate cancer. Here we combine genetic data across the 8q24 susceptibility region from 71,535 prostate cancer cases and 52,935 controls of European ancestry to define the overall contribution of germline variation at 8q24 to prostate cancer risk. We identify 12 independent risk signals for prostate cancer (p < 4.28 x 10(-15)), including three risk variants that have yet to be reported. From a polygenic risk score (PRS) model, derived to assess the cumulative effect of risk variants at 8q24, men in the top 1% of the PRS have a 4-fold (95% CI = 3.62-4.40) greater risk compared to the population average. These 12 variants account for similar to 25% of what can be currently explained of the familial risk of prostate cancer by known genetic risk factors. These findings highlight the overwhelming contribution of germline variation at 8q24 on prostate cancer risk which has implications for population risk stratification.
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13.
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14.
  • Wu, Lang, et al. (författare)
  • Identification of Novel Susceptibility Loci and Genes for Prostate Cancer Risk : A Transcriptome-Wide Association Study in over 140,000 European Descendants
  • 2019
  • Ingår i: Cancer Research. - : AMER ASSOC CANCER RESEARCH. - 0008-5472 .- 1538-7445. ; 79:13, s. 3192-3204
  • Tidskriftsartikel (refereegranskat)abstract
    • Genome-wide association study-identified prostate cancer risk variants explain only a relatively small fraction of its familial relative risk, and the genes responsible for many of these identified associations remain unknown. To discover novel prostate cancer genetic loci and possible causal genes at previously identified risk loci, we performed a transcriptome-wide association study in 79,194 cases and 61,112 controls of European ancestry. Using data from the Genotype-Tissue Expression Project, we established genetic models to predict gene expression across the transcriptome for both prostate models and cross-tissue models and evaluated model performance using two independent datasets. We identified significant associations for 137 genes at P < 2.61 x 10(-6), a Bonferroni-corrected threshold, including nine genes that remained significant at P < 2.61 x 10(-6) after adjusting for all known prostate cancer risk variants in nearby regions. Of the 128 remaining associated genes, 94 have not yet been reported as potential target genes at known loci. We silenced 14 genes and many showed a consistent effect on viability and colony-forming efficiency in three cell lines. Our study provides substantial new information to advance our understanding of prostate cancer genetics and biology. Significance: This study identifies novel prostate cancer genetic loci and possible causal genes, advancing our understanding of the molecular mechanisms that drive prostate cancer.
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15.
  • Szulkin, Robert, et al. (författare)
  • Prediction of individual genetic risk to prostate cancer using a polygenic score.
  • 2015
  • Ingår i: The Prostate. - 0270-4137 .- 1097-0045. ; 75:13, s. 1467-74
  • Tidskriftsartikel (refereegranskat)abstract
    • BACKGROUND: Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction.METHODS: We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls.RESULTS: The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P = 0.0012) and the net reclassification index with 0.21 (P = 8.5E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk.CONCLUSIONS: Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction.
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