Literature DB >> 25667489

Improving the prediction of pathologic outcomes in patients undergoing radical prostatectomy: the value of prostate cancer antigen 3 (PCA3), prostate health index (phi) and sarcosine.

Matteo Ferro1, Giuseppe Lucarelli2, Dario Bruzzese3, Sisto Perdonà4, Claudia Mazzarella5, Giuseppe Perruolo5, Ada Marino5, Vincenzo Cosimato5, Emilia Giorgio5, Virginia Tagliamonte5, Danilo Bottero1, Ottavio De Cobelli1, Daniela Terracciano6.   

Abstract

BACKGROUND/AIM: Several efforts have been made to find biomarkers that could help clinicians to preoperatively determine prostate cancer (PCa) pathological characteristics and choose the best therapeutic approach, avoiding over-treatment. On this effort, prostate cancer antigen 3 (PCA3), prostate health index (phi) and sarcosine have been presented as promising tools. We evaluated the ability of these biomarkers to predict the pathologic PCa characteristics within a prospectively collected contemporary cohort of patients who underwent radical prostatectomy (RP) for clinically localized PCa at a single high-volume Institution.
MATERIALS AND METHODS: The prognostic performance of PCA3, phi and sarcosine were evaluated in 78 patients undergoing RP for biopsy-proven PCa. Receiver operating characteristic (ROC) curve analyses tested the accuracy (area under the curve (AUC)) in predicting PCa pathological characteristics. Decision curve analyses (DCA) were used to assess the clinical benefit of the three biomarkers.
RESULTS: We found that PCA3, phi and sarcosine levels were significantly higher in patients with tumor volume (TV)≥0.5 ml, pathologic Gleason sum (GS)≥7 and pT3 disease (all p-values≤0.01). ROC curve analysis showed that phi is an accurate predictor of high-stage (AUC 0.85 [0.77-0.93]), high-grade (AUC 0.83 [0.73-0.93]) and high-volume disease (AUC 0.94 [0.88-0.99]). Sarcosine showed a comparable AUC (0.85 [0.76-0.94]) only for T3 stage prediction, whereas PCA3 score showed lower AUCs, ranging from 0.74 (for GS) to 0.86 (for TV).
CONCLUSION: PCA3, phi and sarcosine are predictors of PCa characteristics at final pathology. Successful clinical translation of these findings would reduce the frequency of surveillance biopsies and may enhance acceptance of active surveillance (AS). Copyright
© 2015 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Entities:  

Keywords:  Gleason score; PCA3; phi; sarcosine; tumor stage; tumor volume

Mesh:

Substances:

Year:  2015        PMID: 25667489

Source DB:  PubMed          Journal:  Anticancer Res        ISSN: 0250-7005            Impact factor:   2.480


  19 in total

1.  Prostate Health Index improves multivariable risk prediction of aggressive prostate cancer.

Authors:  Stacy Loeb; Sanghyuk S Shin; Dennis L Broyles; John T Wei; Martin Sanda; George Klee; Alan W Partin; Lori Sokoll; Daniel W Chan; Chris H Bangma; Ron H N van Schaik; Kevin M Slawin; Leonard S Marks; William J Catalona
Journal:  BJU Int       Date:  2016-11-22       Impact factor: 5.588

Review 2.  The role of prostate cancer biomarkers in undiagnosed men.

Authors:  Hasan Dani; Stacy Loeb
Journal:  Curr Opin Urol       Date:  2017-05       Impact factor: 2.309

3.  Prostate Cancer Antigen 3 Score Does Not Predict for Adverse Pathologic Features at Radical Prostatectomy or for Progression-free Survival in Clinically Localized, Intermediate- and High-risk Prostate Cancer.

Authors:  John V Hegde; Darlene Veruttipong; Jonathan W Said; Robert E Reiter; Michael L Steinberg; Christopher R King; Amar U Kishan
Journal:  Urology       Date:  2017-05-25       Impact factor: 2.649

Review 4.  Circulating metabolite biomarkers: a game changer in the human prostate cancer diagnosis.

Authors:  Sabareeswaran Krishnan; Shruthi Kanthaje; Devasya Rekha Punchappady; M Mujeeburahiman; Chandrahas Koumar Ratnacaram
Journal:  J Cancer Res Clin Oncol       Date:  2022-06-28       Impact factor: 4.553

5.  Spectrophotometric photodynamic diagnosis of prostate cancer cells excreted in voided urine using 5-aminolevulinic acid.

Authors:  Yasushi Nakai; Makito Miyake; Satoshi Anai; Shunta Hori; Yoshihiro Tatsumi; Yosuke Morizawa; Sayuri Onisi; Nobumichi Tanaka; Kiyohide Fujimoto
Journal:  Lasers Med Sci       Date:  2018-05-04       Impact factor: 3.161

Review 6.  Whom to Biopsy: Prediagnostic Risk Stratification with Biomarkers, Nomograms, and Risk Calculators.

Authors:  Stacy Loeb; Hasan Dani
Journal:  Urol Clin North Am       Date:  2017-11       Impact factor: 2.241

Review 7.  Biomarkers in localized prostate cancer.

Authors:  Matteo Ferro; Carlo Buonerba; Daniela Terracciano; Giuseppe Lucarelli; Vincenzo Cosimato; Danilo Bottero; Victor M Deliu; Pasquale Ditonno; Sisto Perdonà; Riccardo Autorino; Ioman Coman; Sabino De Placido; Giuseppe Di Lorenzo; Ottavio De Cobelli
Journal:  Future Oncol       Date:  2016-01-15       Impact factor: 3.404

8.  Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-Assisted Decision-Support Models.

Authors:  Hailang Liu; Kun Tang; Ejun Peng; Liang Wang; Ding Xia; Zhiqiang Chen
Journal:  Cancer Manag Res       Date:  2020-12-22       Impact factor: 3.989

9.  Predicting Pathological Features at Radical Prostatectomy in Patients with Prostate Cancer Eligible for Active Surveillance by Multiparametric Magnetic Resonance Imaging.

Authors:  Ottavio de Cobelli; Daniela Terracciano; Elena Tagliabue; Sara Raimondi; Danilo Bottero; Antonio Cioffi; Barbara Jereczek-Fossa; Giuseppe Petralia; Giovanni Cordima; Gilberto Laurino Almeida; Giuseppe Lucarelli; Carlo Buonerba; Deliu Victor Matei; Giuseppe Renne; Giuseppe Di Lorenzo; Matteo Ferro
Journal:  PLoS One       Date:  2015-10-07       Impact factor: 3.240

10.  Increased Expression of the Autocrine Motility Factor is Associated With Poor Prognosis in Patients With Clear Cell-Renal Cell Carcinoma.

Authors:  Giuseppe Lucarelli; Monica Rutigliano; Francesca Sanguedolce; Vanessa Galleggiante; Andrea Giglio; Simona Cagiano; Pantaleo Bufo; Eugenio Maiorano; Domenico Ribatti; Elena Ranieri; Margherita Gigante; Loreto Gesualdo; Matteo Ferro; Ottavio de Cobelli; Carlo Buonerba; Giuseppe Di Lorenzo; Sabino De Placido; Silvano Palazzo; Carlo Bettocchi; Pasquale Ditonno; Michele Battaglia
Journal:  Medicine (Baltimore)       Date:  2015-11       Impact factor: 1.817

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