An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk
Wu, L.; Yang, Y.; Guo, X.; Shu, X. O.; Cai, Q.; Shu, X.; Li, B.; Tao, R.; Wu, C.; Nikas, J. B.; Sun, Y.; Zhu, J.; Roobol, M. J.; Giles, G. G.; Brenner, H.; John, E. M.; Clements, J.; Grindedal, E. M.; Park, J. Y.; Stanford, J. L.; Kote-Jarai, Z.; Haiman, C. A.; Eeles, R. A.; Zheng, W.; Long, J.; Henderson, B. E.; Schumacher, F. R.; Easton, D.; Benlloch, S.; Olama, A. A. A.; Muir, K.; Berndt, S. I.; Conti, D. V.; Wiklund, F.; Chanock, S.; Gapstur, S. M.; Stevens, V. L.; Tangen, C. M.; Batra, J.; Gronberg, H.; Pashayan, N.; Schleutker, J.; Albanes, D.; Weinstein, S.; Wolk, A.; West, C.; Mucci, L.; Cancel-Tassin, G.; Koutros, S.; Sorensen, K. D.; Neal, D. E.; Hamdy, F. C.; Donovan, J. L.; Travis, R. C.; Hamilton, R. J.; Ingles, S. A.; Rosenstein, B. S.; Lu, Y. J.; Kibel, A. S.; Vega Gliemmo, Ana; Kogevinas, M.; Penney, K. L.; Cybulski, C.; Nordestgaard, B. G.; Maier, C.; Kim, J.; Teixeira, M. R.; Neuhausen, S. L.; De Ruyck, K.; Razack, A.; Newcomb, L. F.; Gamulin, M.; Kaneva, R.; Usmani, N.; Claessens, F.; Townsend, P. A.; Gago Dominguez, Manuela; Menegaux, F.; Khaw, K. T.; Cannon-Albright, L.; Pandha, H.; Thibodeau, S. N.; Hunter, D. J.; Blot, W. J.; Riboli, E.
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Identificadores
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Autor corporativo
PRACTICAL consortium; CRUK Consortium; BPC3 Consortium; CAPS Consortium; PEGASUS ConsortiumFecha de publicación
2020Título de revista
Nature Communications
Tipo de contenido
Journal Article
DeCS
estudios de asociación genética | estudios de casos y controles | neoplasias de la próstata | factores de riesgo | humanos | islas CpG | metilación del ADN | predisposición genética a la enfermedadMeSH
Risk Factors | Humans | Genetic Association Studies | Prostatic Neoplasms | DNA Methylation | Case-Control Studies | Genetic Predisposition to Disease | CpG IslandsResumen
It remains elusive whether some of the associations identified in genome-wide association studies of prostate cancer (PrCa) may be due to regulatory effects of genetic variants on CpG sites, which may further influence expression of PrCa target genes. To search for CpG sites associated with PrCa risk, here we establish genetic models to predict methylation (N = 1,595) and conduct association analyses with PrCa risk (79,194 cases and 61,112 controls). We identify 759 CpG sites showing an association, including 15 located at novel loci. Among those 759 CpG sites, methylation of 42 is associated with expression of 28 adjacent genes. Among 22 genes, 18 show an association with PrCa risk. Overall, 25 CpG sites show consistent association directions for the methylation-gene expression-PrCa pathway. We identify DNA methylation biomarkers associated with PrCa, and our findings suggest that specific CpG sites may influence PrCa via regulating expression of candidate PrCa target genes.
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