Development of a prognostic risk model for predicting biochemical recurrence-free survival in patients with prostate cancer based on lysine acetylation.

Background: Lysine acetylation plays a critical role in prostate cancer (PCa) by modulating androgen receptor (AR) signaling. However, the exact mechanisms by which lysine acetylation impacts PCa prognosis remain unclear. The aim of this study was to investigate the mechanism by which lysine acetylation affects PCa prognosis by modulating the AR signaling pathway.

Methods: Data from The Cancer Genome Atlas-Prostate Adenocarcinoma (TCGA-PRAD), GSE54460, and lysine acetylation-related genes (LARGs) were obtained from public databases and literature. Differentially expressed genes (DEGs) were identified in TCGA-PRAD, and key module genes associated with LARGs were selected using weighted gene co-expression network analysis (WGCNA). Candidate genes were identified by overlapping DEGs and key module genes. A biochemical recurrence-free (BCR-free) prognostic model was constructed and validated using BCR-free survival data from patients with PCa. Prognostic genes were further confirmed through machine learning. PCa samples were stratified into high- and low-risk subgroups based on the median risk score. A nomogram model was developed integrating clinicopathological features and risk scores to identify independent prognostic factors. Enrichment analysis, tumor microenvironment profiling, and drug sensitivity assessments were performed for the two risk subgroups.

Results: A total of 2,658 DEGs and 723 key module genes were analyzed, yielding 105 overlapping candidate genes. Five genes-UBXN10, ACOX2, PLCL1, PLS3, and SLIT3-were identified as BCR-free-related prognostic markers in TCGA-PRAD. The prognostic risk model revealed significantly lower BCR-free survival rates in the high-risk subgroup compared to the low-risk subgroup. A nomogram incorporating Gleason score, tumor stage (T stage), and risk score effectively predicted BCR-free survival in patients with PCa. Notably, natural killer (NK) cells, myeloid dendritic cells, endothelial cells, and fibroblasts were significantly correlated with PLS3 (|Cor| >0.3, P<0.05). Drugs such as cisplatin, MK-1775, and ulixertinib were identified as potential therapeutic agents for PCa.

Conclusions: Five BCR-free-related prognostic genes were identified as potential therapeutic targets. Additionally, a BCR-free-related prognostic risk model was developed, offering a robust tool for predicting BCR-free survival in patients with PCa.