A Novel Gene Signature-Based Model Predicts Biochemical Recurrence-Free Survival in Prostate Cancer Patients after Radical Prostatectomy
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A Novel Gene Signature-Based Model Predicts Biochemical Recurrence-Free Survival in Prostate Cancer Patients after Radical Prostatectomy. / Shi, Run; Bao, Xuanwen; Weischenfeldt, Joachim; Schaefer, Christian; Rogowski, Paul; Schmidt-Hegemann, Nina-Sophie; Unger, Kristian; Lauber, Kirsten; Wang, Xuanbin; Buchner, Alexander; Stief, Christian; Schlomm, Thorsten; Belka, Claus; Li, Minglun.
In: Cancers, Vol. 12, No. 1, 2020, p. 1-12.Research output: Contribution to journal › Journal article › Research › peer-review
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T1 - A Novel Gene Signature-Based Model Predicts Biochemical Recurrence-Free Survival in Prostate Cancer Patients after Radical Prostatectomy
AU - Shi, Run
AU - Bao, Xuanwen
AU - Weischenfeldt, Joachim
AU - Schaefer, Christian
AU - Rogowski, Paul
AU - Schmidt-Hegemann, Nina-Sophie
AU - Unger, Kristian
AU - Lauber, Kirsten
AU - Wang, Xuanbin
AU - Buchner, Alexander
AU - Stief, Christian
AU - Schlomm, Thorsten
AU - Belka, Claus
AU - Li, Minglun
PY - 2020
Y1 - 2020
N2 - Abstract: 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 high-throughput 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.
AB - Abstract: 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 high-throughput 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.
U2 - 10.3390/cancers12010001
DO - 10.3390/cancers12010001
M3 - Journal article
C2 - 31861273
VL - 12
SP - 1
EP - 12
JO - Cancers
JF - Cancers
SN - 2072-6694
IS - 1
ER -
ID: 237512555