000130009 001__ 130009
000130009 005__ 20240118092026.0
000130009 0247_ $$2doi$$a10.1111/j.1464-410X.2012.11333.x
000130009 0248_ $$2sideral$$a79268
000130009 037__ $$aART-2013-79268
000130009 041__ $$aeng
000130009 100__ $$0(orcid)0000-0003-0178-4567$$aBorque, Ángel$$uUniversidad de Zaragoza
000130009 245__ $$aGenetic predisposition to early recurrence in clinically localized prostate cancer
000130009 260__ $$c2013
000130009 5060_ $$aAccess copy available to the general public$$fUnrestricted
000130009 5203_ $$aWhat's known on the subject? and What does the study add?
Currently available nomograms to predict preoperative risk of early biochemical recurrence (EBCR) after radical prostatectomy are solely based on classic clinicopathological variables. Despite providing useful predictions, these models are not perfect. Indeed, most researchers agree that nomograms can be improved by incorporating novel biomarkers. In the last few years, several single nucleotide polymorphisms (SNPs) have been associated with prostate cancer, but little is known about their impact on disease recurrence.
We have identified four SNPs associated with EBCR. The addition of SNPs to classic nomograms resulted in a significant improvement in terms of discrimination and calibration. The new nomogram, which combines clinicopathological and genetic variables, will help to improve prediction of prostate cancer recurrence.
Objectives
To evaluate genetic susceptibility to early biochemical recurrence (EBCR) after radical prostatectomy (RP), as a prognostic factor for early systemic dissemination.
To build a preoperative nomogram to predict EBCR combining genetic and clinicopathological factors.
Patients and Methods
We evaluated 670 patients from six University Hospitals who underwent RP for clinically localized prostate cancer (PCa), and were followed-up for at least 5 years or until biochemical recurrence.
EBCR was defined as a level prostate-specific antigen >0.4 ng/mL within 1 year of RP; preoperative variables studied were: age, prostate-specific antigen, clinical stage, biopsy Gleason score, and the genotype of 83 PCa-related single nucleotide polymorphisms (SNPs).
Univariate allele association tests and multivariate logistic regression were used to generate predictive models for EBCR, with clinicopathological factors and adding SNPs.
We internally validated the models by bootstrapping and compared their accuracy using the area under the curve (AUC), net reclassification improvement, integrated discrimination improvement, calibration plots and Vickers' decision curves.
Results
Four common SNPs at KLK3, KLK2, SULT1A1 and BGLAP genes were independently associated with EBCR.
A significant increase in AUC was observed when SNPs were added to the model: AUC (95% confidence interval) 0.728 (0.674–0.784) vs 0.763 (0.708–0.817).
Net reclassification improvement showed a significant increase in probability for events of 60.7% and a decrease for non-events of 63.5%.
Integrated discrimination improvement and decision curves confirmed the superiority of the new model.
Conclusions
Four SNPs associated with EBCR significantly improved the accuracy of clinicopathological factors.
We present a nomogram for preoperative prediction of EBCR after RP.
000130009 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000130009 590__ $$a3.13$$b2013
000130009 591__ $$aUROLOGY & NEPHROLOGY$$b17 / 74 = 0.23$$c2013$$dQ1$$eT1
000130009 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000130009 700__ $$adel Amo, Jokin
000130009 700__ $$0(orcid)0000-0002-3007-302X$$aEsteban, Luis M.
000130009 700__ $$aArs, Elilsabet
000130009 700__ $$aHernández, Carlos
000130009 700__ $$aPlanas, Jacques
000130009 700__ $$aArruza, Antonio
000130009 700__ $$aLlarena, Roberto
000130009 700__ $$aPalou, Joan
000130009 700__ $$aHerranz, Felipe
000130009 700__ $$aRaventós, Carles X.
000130009 700__ $$aTejedor, Diego
000130009 700__ $$aArtieda, Marta
000130009 700__ $$aSimon, Laureano
000130009 700__ $$aMartínez, Antonio
000130009 700__ $$aCarceller, Elena
000130009 700__ $$aSuárez, Miguel
000130009 700__ $$aAllué, Marta
000130009 700__ $$0(orcid)0000-0002-6474-2252$$aSanz, Gerardo$$uUniversidad de Zaragoza
000130009 700__ $$aMorote, Juan
000130009 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDpto. Métodos Estadísticos$$cÁrea Estadís. Investig. Opera.
000130009 7102_ $$11004$$2817$$aUniversidad de Zaragoza$$bDpto. Cirugía,Ginecol.Obstetr.$$cÁrea Urología
000130009 773__ $$g111, 4 (2013), 549-558$$pBJU int.$$tBJU international$$x1464-4096
000130009 8564_ $$s606079$$uhttps://zaguan.unizar.es/record/130009/files/texto_completo.pdf$$yPostprint
000130009 8564_ $$s1418655$$uhttps://zaguan.unizar.es/record/130009/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000130009 909CO $$ooai:zaguan.unizar.es:130009$$particulos$$pdriver
000130009 951__ $$a2024-01-18-09:05:57
000130009 980__ $$aARTICLE