Identifying patients with rapid progression from hormone-sensitive to castration-resistant prostate cancer: a retrospective study

Abstract

Background: Prostate cancer (PCa) is the second most prevalent malignancy and the fifth cause of cancer-related deaths in men. A crucial challenge is identifying the population at risk of rapid progression from hormone-sensitive PCa (HSPC) to the lethal castration-resistant PCa (CRPC). Methods: We collected 78 HSPC biopsies and measured their proteomes using pressure cycling technology and a pulsed data-independent acquisition pipeline. The proteomics data and clinical metadata were used to generate models for classifying HSPC patients and predicting the development of each case. Results: We quantified 7,961 proteins using the HSPC biopsies. A total of 306 proteins were differentially expressed between patients with a long- or short-term progression to CRPC. Using a random forest model, we identified ten proteins that significantly discriminated long- from short-term cases, which were used to classify PCa patients with an 86% accuracy. Next, two clinical parameters (Gleason sum and total PSA) and five proteins (DPT, ARGEF1, UTP23, CMAS, and ANAPC4) were found to be significantly associated with rapid disease progression. A nomogram model using these seven features was generated for stratifying patients into groups with significant progression disparities. Conclusion: We identified proteins associated with a fast progression to CRPC and an unfavorable prognosis. Based on these proteins, our machine learning and nomogram models stratified HSPC into high- and low-risk groups and predict their prognoses. These tools may aid clinicians in predicting the progression of patients, guiding individualized clinical management and decisions.