Identifying Patients With Rapid Progression From Hormone-Sensitive to Castration-Resistant Prostate Cancer: A Retrospective Study

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 prostate cancer (HSPC) to lethal castration-resistant prostate cancer (CRPC). We collected 78 HSPC biopsies and measured their proteomes using pressure cycling technology and a pulsed data-independent acquisition pipeline. We quantified 7355 proteins using these HSPC biopsies. A total of 251 proteins showed differential expression between patients with a long- or short-term progression to CRPC. Using a random forest model, we identified seven proteins that significantly discriminated long- from short-term progression patients, which were used to classify PCa patients with an area under the curve of 0.873. Next, one clinical feature (Gleason sum) and two proteins (BGN and MAPK11) were found to be significantly associated with rapid disease progression. A nomogram model using these three features was generated for stratifying patients into groups with significant progression differences (p-value = 1.3×10−4). To conclude, 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 predicted their prognoses. These models may aid clinicians in predicting the progression of patients, guiding individualized clinical management and decisions. [Display omitted] •Seventy-eight HSPC patients' proteome with follow-up data on the progression to CRPC.•Two hundred fifty-one proteins showed differential expression between long- and short-term progressions.•A 7-protein random forest model can discriminate long- from short-term cases.•A nomogram model was used for predicting the progression term to CRPC. A crucial challenge is to identify the population at risk of rapid progression from hormone-sensitive prostate cancer (HSPC) to lethal castration-resistant prostate cancer (CRPC). Here, we quantified 7355 proteins from 78 patients and identified 251 differential proteins associated with a fast progression to CRPC. Next, we established a machine learning classifier using seven proteins and a nomogram model containing two proteins and a clinical feature to identify patients with rapid progression and predict their prognoses. These models may guide individualized clinical management and decisions.