Identifying the population at risk of rapid progression from hormone-sensitive prostate cancer (HSPC) to lethal castration-resistant prostate cancer (CRPC) is a challenge. This work has highlighted important prognostic insights based on proteomics data, magnetic resonance imaging (MRI) and histopathological specimens. We retrospectively developed a multi-omics-based model based on 77 patients with HSPC. In order to identify the features related to survival time under each mode, we used the Boruta algorithm for feature screening. In order to demonstrate the effectiveness of our selected features, we used six machine learning methods to validate the classification of the selected features for each mode. A total of 63 proteome signatures, 60 HE signatures, 56 T2WI signatures, and 54 ADC signatures were identified as features related to the speed of HSPC progression. Ultimately, 30 multi-omics-based features were determined by the least absolute shrinkage and selection operator (LASSO) method and multivariate cox regression. In order to stratify patients with significant disparities in progress, a nomogram model was developed, of which the C-index was 0.906. Accordingly, the developed model could help identify patients who are at a high risk of rapid CRPC progression, and aid clinicians in guiding personalized clinical management and decision-making.

Keywords: Hormone-sensitive prostate cancer, Histopathology, Proteome, Magnetic resonance imaging, Machine learning.

识别从激素敏感性前列腺癌（HSPC）快速进展为致命的去势抵抗性前列腺癌（CRPC）的高风险人群是一个挑战。本研究通过基于蛋白质组学数据、磁共振成像（MRI）和病理组织样本的重要预后洞察，对此进行了探讨。我们回顾性地开发了一种多组学模型，该模型基于77名HSPC患者的资料。为了识别每个模式下与生存时间相关的特征，我们使用了Boruta算法进行特征筛选。为了证明所选特征的有效性，我们采用了六种机器学习方法来验证选定的每个模式下的分类特征。总共确定了63个蛋白质组标志物、60个HE（苏木素和伊红染色）标志物、56个T2WI（横向弛豫加权成像）标志物和54个ADC（表观扩散系数）标志物与HSPC进展速度相关。最终，通过最小绝对收缩和选择算子（LASSO）方法和多变量Cox回归分析确定了30个多组学特征。为了区分具有显著差异的患者群体，开发了一个列线图模型，其C指数为0.906。因此，所开发的模型可以帮助识别快速进展至CRPC的高风险患者，并帮助临床医生指导个性化的临床管理和决策。

关键词：激素敏感性前列腺癌、病理组织学、蛋白质组、磁共振成像、机器学习。