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Shi, R.* ; Bao, X. ; Weischenfeldt, J.* ; Schaefer, C.* ; Rogowski, P.* ; Schmidt-Hegemann, N.S.* ; Unger, K. ; Lauber, K. ; Wang, X.* ; Buchner, A.* ; Stief, C.* ; Schlomm, T.* ; Belka, C. ; Li, M.*

A novel gene signature-based model predicts biochemical recurrence-free survival in prostate cancer patients after radical prostatectomy.

Cancers 12:1 (2020)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Currently, decision-making regarding biochemical recurrence (BCR) following prostatectomy relies solely on clinical parameters. We therefore attempted to develop an integrated prediction model based on a molecular signature and clinicopathological features, in order to forecast the risk for BCR and guide clinical decision-making for postoperative therapy. Using highthroughput screening and least absolute shrinkage and selection operator (LASSO) in the training set, a novel gene signature for biochemical recurrence-free survival (BCRFS) was established. Validation of the prognostic value was performed in five other independent datasets, including our patient cohort. Multivariate Cox regression analysis was performed to evaluate the importance of risk for BCR. Time-dependent receiver operating characteristic (tROC) was used to evaluate the predictive power. In combination with relevant clinicopathological features, a decision tree was built to improve the risk stratification. The gene signature exhibited a strong capacity for identifying high-risk BCR patients, and multivariate Cox regression analysis demonstrated that the gene signature consistently acted as a risk factor for BCR. The decision tree was successfully able to identify the high-risk subgroup. Overall, the gene signature established in the present study is a powerful predictor and risk factor for BCR after radical prostatectomy.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Biochemical Recurrence-free Survival ; Gene Signature ; Prostate Cancer ; Radical Prostatectomy ; Risk Stratification
ISSN (print) / ISBN 2072-6694
Journal Cancers
Quellenangaben Volume: 12, Issue: 1 Pages: , Article Number: 1 Supplement: ,
Publisher MDPI
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Radiation Biology (ISB)
Research Unit Radiation Cytogenetics (ZYTO)
CCG Personalized Radiotherapy in Head and Neck Cancer (KKG-KRT)