Forskning ved Københavns Universitet - Københavns Universitet

Forside

A Novel Gene Signature-Based Model Predicts Biochemical Recurrence-Free Survival in Prostate Cancer Patients after Radical Prostatectomy

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Dokumenter

  • Run Shi
  • Xuanwen Bao
  • Weischenfeldt, Joachim Lütken
  • Christian Schaefer
  • Paul Rogowski
  • Nina-Sophie Schmidt-Hegemann
  • Kristian Unger
  • Kirsten Lauber
  • Xuanbin Wang
  • Alexander Buchner
  • Christian Stief
  • Thorsten Schlomm
  • Claus Belka
  • Minglun Li

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.

OriginalsprogEngelsk
TidsskriftCancers
Vol/bind12
Udgave nummer1
Sider (fra-til)1-12
Antal sider12
ISSN2072-6694
DOI
StatusUdgivet - 2020

ID: 237512555