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

• DOI: 10.1101/2022.10.23.22281406
• Publication Date: 2022-10-27T08:45:13Z
• Authors: Chenxi Pan; Yi He; He Wang; Yang Yu; Lu Li; Lingling Huang; Mengge Lyu; Weigang Ge; Bo Yang; Yaoting Sun; Tiannan Guo; Zhiyu Liu
• Keywords: Nomogram; Tumor progression; Proteome

Abstract Background Prostate cancer (PCa) is the second most prevalent malignancy and fifth cause of cancer-related deaths in men. A crucial challenge identifying population at risk rapid progression from hormone-sensitive PCa (HSPC) to lethal castration-resistant (CRPC). Methods We collected 78 HSPC biopsies measured their proteomes using pressure cycling technology a pulsed data-independent acquisition pipeline. The proteomics data clinical metadata were used generate models for classifying patients predicting development each case. Results quantified 7,961 proteins biopsies. total 306 differentially expressed between with long- or short-term CRPC. Using random forest model, we identified ten that significantly discriminated long-from cases, which classify an 86% accuracy. Next, two parameters (Gleason sum PSA) five (DPT, ARGEF1, UTP23, CMAS, ANAPC4) found be associated disease progression. nomogram model these seven features was generated stratifying into groups significant disparities ( p -value = 5.2 × 10 −9 ). Conclusion fast CRPC unfavorable prognosis. Based on proteins, our machine learning stratified high- low-risk predict prognoses. These tools may aid clinicians patients, guiding individualized management decisions.