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

Chenxi Pan, YI He, He Wang, Yang Yu, Lu Li, Lingling Huang, Mengge Lv, Weigang Ge, Bo Yang, Yaoting Sun, Tiannan Guo, Zhiyu Liu
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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.
鉴别从激素敏感到去势抵抗性前列腺癌快速进展的患者:一项回顾性研究
背景:前列腺癌(PCa)是男性癌症相关死亡的第二大常见恶性肿瘤和第五大原因。一个关键的挑战是确定从激素敏感型前列腺癌(HSPC)快速发展为致命的去势抵抗型前列腺癌(CRPC)的风险人群。方法:我们收集了78例HSPC活检,并使用压力循环技术和脉冲数据独立采集管道测量其蛋白质组。蛋白质组学数据和临床元数据用于生成HSPC患者分类模型并预测每个病例的发展。结果:通过HSPC活检,我们定量了7961个蛋白。在长期或短期进展为CRPC的患者中,共有306种蛋白存在差异表达。使用随机森林模型,我们确定了10种显著区分长期和短期病例的蛋白质,用于对PCa患者进行分类,准确率为86%。接下来,两个临床参数(Gleason sum和total PSA)和五个蛋白(DPT, ARGEF1, UTP23, CMAS和ANAPC4)被发现与疾病的快速进展显著相关。使用这七个特征生成了一个nomogram模型,将患者按显著的进展差异进行分组。结论:我们发现了与CRPC快速进展和不良预后相关的蛋白。基于这些蛋白质,我们的机器学习和nomogram模型将HSPC分为高风险和低风险组,并预测其预后。这些工具可以帮助临床医生预测患者的进展,指导个性化的临床管理和决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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