Skip to main content
Fig. 8 | Journal of Translational Medicine

Fig. 8

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

Fig. 8

Identification of potential therapeutic targets and agents for RSS-high patients. Dot plots of the correlation coefficients derived from Spearman’s rank correlation analysis between RSS and druggable mRNA expression in the A TCGA-PRAD and B DKFZ-PRAD datasets. Light-colored dots represent potential targets that pass the threshold in Spearman’s rank correlation analysis (R > 0.3 and adjusted P < 0.05), while dark-colored dots indicate targets that were also selected by CERES analysis. C The distribution of CERES scores of identified targets in prostate cancer cell lines. D The composition of chemical compounds selected by CMap analysis. Only the top 10 drug categories are displayed. The inferred AUC values of irinotecan and topotecan were compared between RSS-high and RSS-low patients in the E TCGA-PRAD and F DKFZ-PRAD datasets. The inferred AUC values of ADT, taxanes, and PARP inhibitors were compared between RSS-high and RSS-low patients in the G TCGA-PRAD and H DKFZ-PRAD datasets. The upper and lower bounds of the boxes represented 75th and 25th percentiles while the center lines in the boxes indicate the median values. The asterisks represented the statistical P-value (*P < 0.05; **P < 0.01; ***P < 0.001, ****P < 0.0001)

Back to article page