000130483 001__ 130483
000130483 005__ 20240125162931.0
000130483 0247_ $$2doi$$a10.1016/j.juro.2010.03.144
000130483 0248_ $$2sideral$$a71864
000130483 037__ $$aART-2010-71864
000130483 041__ $$aeng
000130483 100__ $$aMorote, J.
000130483 245__ $$aImproved prediction of biochemical recurrence after radical prostatectomy by genetic polymorphisms
000130483 260__ $$c2010
000130483 5203_ $$aPurpose: Single nucleotide polymorphisms are inherited genetic variations that can predispose or protect individuals against clinical events. We hypothesized that single nucleotide polymorphism profiling may improve the prediction of biochemical recurrence after radical prostatectomy.
Materials and methods: We performed a retrospective, multi-institutional study of 703 patients treated with radical prostatectomy for clinically localized prostate cancer who had at least 5 years of followup after surgery. All patients were genotyped for 83 prostate cancer related single nucleotide polymorphisms using a low density oligonucleotide microarray. Baseline clinicopathological variables and single nucleotide polymorphisms were analyzed to predict biochemical recurrence within 5 years using stepwise logistic regression. Discrimination was measured by ROC curve AUC, specificity, sensitivity, predictive values, net reclassification improvement and integrated discrimination index.
Results: The overall biochemical recurrence rate was 35%. The model with the best fit combined 8 covariates, including the 5 clinicopathological variables prostate specific antigen, Gleason score, pathological stage, lymph node involvement and margin status, and 3 single nucleotide polymorphisms at the KLK2, SULT1A1 and TLR4 genes. Model predictive power was defined by 80% positive predictive value, 74% negative predictive value and an AUC of 0.78. The model based on clinicopathological variables plus single nucleotide polymorphisms showed significant improvement over the model without single nucleotide polymorphisms, as indicated by 23.3% net reclassification improvement (p = 0.003), integrated discrimination index (p <0.001) and likelihood ratio test (p <0.001). Internal validation proved model robustness (bootstrap corrected AUC 0.78, range 0.74 to 0.82). The calibration plot showed close agreement between biochemical recurrence observed and predicted probabilities.
Conclusions: Predicting biochemical recurrence after radical prostatectomy based on clinicopathological data can be significantly improved by including patient genetic information.
000130483 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000130483 590__ $$a3.862$$b2010
000130483 591__ $$aUROLOGY & NEPHROLOGY$$b10 / 68 = 0.147$$c2010$$dQ1$$eT1
000130483 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000130483 700__ $$aDel Amo, J.
000130483 700__ $$0(orcid)0000-0003-0178-4567$$aBorque, A.$$uUniversidad de Zaragoza
000130483 700__ $$aArs, E.
000130483 700__ $$aHernndez, C.
000130483 700__ $$0(orcid)0000-0003-0018-4738$$aHerranz, F.$$uUniversidad de Zaragoza
000130483 700__ $$aArruza, A.
000130483 700__ $$aLlarena, R.
000130483 700__ $$aPlanas, J.
000130483 700__ $$aViso, M. J.
000130483 700__ $$aPalou, J.
000130483 700__ $$aRavents, C. X.
000130483 700__ $$aTejedor, D.
000130483 700__ $$aArtieda, M.
000130483 700__ $$aSimn, L.
000130483 700__ $$aMartnez, A.
000130483 700__ $$aRioja, L. A.
000130483 7102_ $$11006$$2413$$aUniversidad de Zaragoza$$bDpto. Fisiatría y Enfermería$$cÁrea Fisioterapia
000130483 7102_ $$11004$$2817$$aUniversidad de Zaragoza$$bDpto. Cirugía,Ginecol.Obstetr.$$cÁrea Urología
000130483 773__ $$g184, 2 (2010), 506-511$$pJ. urol.$$tJOURNAL OF UROLOGY$$x0022-5347
000130483 8564_ $$s181313$$uhttps://zaguan.unizar.es/record/130483/files/texto_completo.pdf$$yVersión publicada
000130483 8564_ $$s2720618$$uhttps://zaguan.unizar.es/record/130483/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000130483 909CO $$ooai:zaguan.unizar.es:130483$$particulos$$pdriver
000130483 951__ $$a2024-01-25-15:14:18
000130483 980__ $$aARTICLE