Abstract
For prostate cancer (PCa) patients, biochemical recurrence (BCR) is the first sign of disease relapse and the subsequent metastasis. TP53 mutations are relatively prevalent in advanced PCa forms. We aimed to utilize this knowledge to identify robust transcriptomic signatures for BCR prediction in patients with Gleason score ≥ 7 cancers, which cause most PCa deaths. Using the TCGA-PRAD dataset and the novel data-driven stochastic approach proposed in this study, we identified a 25-gene signature from the genes whose expression in tumors was associated with TP53 mutation statuses. The predictive strength of the signature was assessed by AUC and Fisher's exact test p-value according to the output of support vector machine-based cross validation. For the TCGA-PRAD dataset, the AUC and p-value were 0.837 and 5 × 10(-13), respectively. For five external datasets, the AUCs and p-values ranged from 0.632 to 0.794 and 6 × 10(-2) to 5 × 10(-5), respectively. The signature also performed well in predicting relapse-free survival (RFS). The signature-based transcriptomic risk scores (TRS) explained 28.2% of variation in RFS on average. The combination of TRS and clinicopathologic prognostic factors explained 23-72% of variation in RFS, with a median of 54.5%. Our method and findings are useful for developing new prognostic tools in PCa and other cancers.