摘要Objective Prostate cancer (PCa) is one of the most common malignancies among elderly males. However, effective prognostic biomarkers are currently lacking. Bioinformatic analysis was used to identify patients at high risk of biochemical recurrence (BCR). Methods In our study, RNA sequencing and clinical data were downloaded from The Cancer Genome Atlas (TCGA) dataset to serve as the training and internal validation sets. The GSE84042 dataset was used as the external validation set. Batch effects were removed and normalized for the two datasets using "sva" package. Univariate Cox, least absolute shrinkage and selection operator (LASSO) Cox, and multivariate Cox regression analyses were successively performed to identify the redox-related gene (RRG) signature. After performing univariate Cox, LASSO Cox, and multivariate Cox regression analyses, a signature consisting of seven RRGs was established to predict BCR of patients with PCa, which included TP53, ADH5, SRRT, SLC24A2, COL1A1, CSF3R, and TEX19. Kaplan-Meier and receiver operating characteristic curve analyses showed good performance for the prognostic signature in the training and validation datasets. Results Univariate and multivariate Cox analyses showed that the RRG signature was an independent prognostic factor for BCR of patients with PCa. Thereafter, the nomogram results revealed that it was able to predict BCR of patients with PCa with high efficiency. Conclusion This study identified an independent prognostic signature and established a nomogram to predict BCR in PCa. This signature can be used to identify patients with PCa with a high risk of BCR, and personalized treatment can be applied.