Cuproptosis-related gene PROK1 predicts the diagnosis and prognosis of prostate cancer based on multiple machine...

Cuproptosis, a newly identified form of cell death, influences the development, progression, and prognosis of prostate cancer (PCa). Identifying key genes associated with cuproptosis and developing robust predictive models through machine learning approaches are crucial for personalized PCa treatment. In our study, multiple machine learning methods and their combinations were employed for the construction of diagnostic and prognostic models for PCa, which were then validated in multiple external independent cohorts. The model key gene, PROK1, was selected for further analysis, and its expression was compared in clinical samples and cell lines. Additionally, the anticancer effect of PROK1 was explored through regulating the expression of PROK1. Most cuproptosis-related genes (CRGs) showed differential expression between PCa and normal prostate tissues. The two clusters derived from the Consensus Clustering method, based on cuproptosis gene expression characteristics, exhibit distinct clinical features and immune microenvironment infiltration patterns. Models constructed based on machine learning methods showed promising diagnostic capabilities for PCa and were associated with the prediction of biochemical recurrence-free survival and disease-free survival of patients. Inhibiting PROK1 expression promoted PCa cell proliferation and invasion, while its overexpression had the opposite effect. Furthermore, pathway exploration revealed that PROK1 inhibits tumor growth by mediating apoptosis under copper ion stress. Its association with cuproptosis warrants further investigation to elucidate the precise mechanism.