Establishment of a five-enzalutamide-resistance-related-gene-based classifier for recurrence-free survival predicting of prostate cancer.

To identify prostate cancer (PCa) patients with a high risk of recurrence is critical before delivering adjuvant treatment. We developed a classifier based on the Enzalutamide treatment resistance-related genes to assist the currently available staging system in predicting the recurrence-free survival (RFS) prognosis of PCa patients. We overlapped the DEGs from two datasets to obtain a more convincing Enzalutamide-resistance-related-gene (ERRG) cluster. The five-ERRG-based classifier obtained good predictive values in both the training and validation cohorts. The classifier precisely predicted RFS of patients in four cohorts, independent of patient age, pathological tumour stage, Gleason score and PSA levels. The classifier and the clinicopathological factors were combined to construct a nomogram, which had an increased predictive accuracy than that of each variable alone. Besides, we also compared the differences between high- and low-risk subgroups and found their differences were enriched in cancer progression-related pathways. The five-ERRG-based classifier is a practical and reliable predictor, which adds value to the existing staging system for predicting the RFS prognosis of PCa after radical prostatectomy, enabling physicians to make more informed treatment decisions concerning adjuvant therapy.