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Fig. 3 | Journal of Translational Medicine

Fig. 3

From: A machine learning framework develops a DNA replication stress model for predicting clinical outcomes and therapeutic vulnerability in primary prostate cancer

Fig. 3

Evaluation of the DNA replication stress signature (RSS) in multiple cohorts. Time-dependent area under the receiver operating characteristic curve (AUC) at 1-, 3-, and 5-year in the A TCGA-PRAD, B DKFZ-PRAD, C GSE70768, D GSE70769, E GSE94767 datasets. F Forest plots demonstrate the hazard ratio (HR), 95% confidence interval (CI), and the corresponding P values of both univariate Cox regression analysis (shown in the pink shading area) and multivariate Cox regression analysis (shown in the blue shading area) in 5 prostate cancer cohorts. Kaplan–Meier plots of the G TCGA-PRAD, H DKFZ-PRAD, I GSE70768, J GSE70769, K GSE94767 datasets. High- and low-risk groups are determined by the universal cutoff of 0.536. P values are derived from log-rank test. PSA stands for prostate-specific antigen; pT refers to the pathological T stage; pN refers to the pathological N stage; RSS represents replication stress signature

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