Integrating multimodal data to predict the progression of hormone-sensitive prostate cancer.

Journal: Clinical proteomics
Published Date: May 29, 2025

Abstract

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.

Authors

  • Xiangfu Lu
    Department of Urology, 967 th hospital of PLA Joint Logistics Support Force, No.80 Shengli Road, Dalian, 116014, PR China.
  • Chenxi Pan
    State key laboratory of fine chemicals, Frontiers Science Center for Smart Materials Oriented Chemical Engineering, School of Bioengineering, Dalian University of Technology, Dalian, 116023, PR China.
  • Luhan Yao
    School of Information and Communication Engineering, Dalian University of Technology, Dalian, Dalian, 116023, PR China.
  • Jiayu Wan
    School of Information and Communication Engineering, Dalian University of Technology, Dalian, Dalian, 116023, PR China.
  • Xiaolong Xu
    Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, China.
  • Wei Wang
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.
  • Xiangying Wang
    State key laboratory of fine chemicals, Frontiers Science Center for Smart Materials Oriented Chemical Engineering, School of Bioengineering, Dalian University of Technology, Dalian, 116023, PR China.
  • Xiaoyun Liu
    Department of General Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Zhonghua Jin
    Department of chest surgery, The Second Hospital of Dalian Medical University, No.467 Zhongshan Road, Dalian, 116023, PR China.
  • Hongyu Wang
    School of Information Science and Technology, Northwest University, Xi'an, Shaanxi, China.
  • Yi He
    National Institutes for Food and Drug Control, 2 Tiantan Xili, Beijing 100050, China.
  • Bo Yang
    Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang Province 311121, China.

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