Risk Model for Prostate Cancer Using Environmental and Genetic Factors in the Spanish Multi-Case- Control (MCC) Study

Author

Gomez-Acebo, Ines

Dierssen-Sotos, Trinidad

Fernandez Navarro, Pablo

Palazuelos, Camilo

Morros, Rosa

Aragonés, Nuria

Castaño-Vinyals, Gemma

Jiménez Monleón, Jose J.

Ruiz Cerdá, Jose Luis

Pérez-Gómez, Beatriz

Ruiz-Dominguez, José Manuel

Alonso Molero, Jessica

Pollán, Marina

Kogevinas, M..

Llorca, Javier

Universitat Autònoma de Barcelona

Publication date

2017

Abstract

Altres ajuts: Fundación Marqués de Valdecilla (API 10/09); Junta de Castilla y León (LE22A10-2); Consejería de Salud de la Junta de Andalucía (2009-S0143); Conselleria de Sanitat de la Generalitat Valenciana (AP 061/10); Recercaixa (2010ACUP 00310); Gobierno Vasco; Consejería de Sanidad de la Región de Murcia; Fundación científica de la Asociacion Española Contra el Cáncer; Fundación Caja de Ahorros de Asturias; Universidad de Oviedo; Societat Catalana de Digestologia.


Prostate cancer (PCa) is the second most common cancer among men worldwide. Its etiology remains largely unknown compared to other common cancers. We have developed a risk stratification model combining environmental factors with family history and genetic susceptibility. 818 PCa cases and 1,006 healthy controls were compared. Subjects were interviewed on major lifestyle factors and family history. Fifty-six PCa susceptibility SNPs were genotyped. Risk models based on logistic regression were developed to combine environmental factors, family history and a genetic risk score. In the whole model, compared with subjects with low risk (reference category, decile 1), those carrying an intermediate risk (decile 5) had a 265% increase in PCa risk (OR = 3.65, 95% CI 2.26 to 5.91). The genetic risk score had an area under the ROC curve (AUROC) of 0.66 (95% CI 0.63 to 0.68). When adding the environmental score and family history to the genetic risk score, the AUROC increased by 0.05, reaching 0.71 (95% CI 0.69 to 0.74). Genetic susceptibility has a stronger risk value of the prediction that modifiable risk factors. While the added value of each SNP is small, the combination of 56 SNPs adds to the predictive ability of the risk model.

Document Type

Article

Language

English

Publisher

 

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Scientific reports ; Vol. 7 Núm. 8994 (august 2017), p. 1-10

Rights

open access

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