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.

T.G. is a shareholder of Westlake Omics, Inc. L.H. and W.G. are employees of Westlake Omics Inc, C.P., Y.H., H.W., Y.Y., L.L., M.L., B.Y., Y.S., T.G., and Z.L. have applied for a patent on this project. The remaining authors declare no competing interest.

This study was funded by grants from the National Key R&D Program of China (No. 2021YFA1301601, 2021YFA1301602, 2020YFE0202200) to T.G.; 1+X program for Clinical Competency enhancement Clinical Research Incubation Project and the Second Hospital of Dalian Medical University (2022LCYJZD02) to B.Y.; United Fund of the Second Hospital of Dalian Medical University and Dalian Institute of Chemical Physics and Chinese Academy of Sciences (UF ZD 202014) to Y.Y.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethics committee of the Second Hospital of Dalian Medical University gave ethical approval for this work.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

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The mass spectrometry proteomics data have been deposited to the iProX with the dataset identifier IPX0005031000 (the data will be publicly released upon publication).