Smooth Muscle Disruptive Feature Defines Molecular Subtyping, Risk Stratification, and Metastatic Potential in Prostate Cancer.

Journal: Annals of surgical oncology
Published Date: Dec 22, 2025

Abstract

BACKGROUND: Smooth muscle (SM) invasion represents a critical feature of prostate cancer (PCa) progression and metastasis. This study aimed to develop a SM-based molecular score for progression and metastasis forecasting for PCa. PATIENTS AND METHODS: A total of 952 patients with PCa with transcriptomic sequencing data and corresponding clinical information were included in this study to develop and validate a SM-related signature (SM score). SM-related molecular subtypes were identified on the basis of 35 smooth muscle-related gene sets using unsupervised clustering. Weighted gene coexpression network analysis (WGCNA) was employed to identify key SM-related genes. The SM score was subsequently developed using machine learning across, The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) training cohort and four independent external validation cohorts. RESULTS: Utilizing 35 gene sets related to SM function, we identified pronounced SM disruption in PCa compared with normal prostate tissues. Unsupervised consensus clustering stratified patients into two distinct molecular subtypes, which exhibited significant differences in prognosis and immune infiltration profiles. We subsequently developed a robust prognostic signature termed SM score, and multiomics analyses confirmed a strong association between the SM score and SM disruption. The SM score consistently demonstrated superior prognostic performance across all cohorts and showed potential as a biomarker for responsiveness to immunotherapy. Notably, SM score was significantly elevated in metastatic lesions compared with primary tumors and normal tissues and accurately predicted metastatic potential. CONCLUSIONS: SM disruption is associated with tumor progression in PCa. The SM score captures SM disruption and predicts PCa progression and metastasis potential, aiding personalized immunotherapy decisions.

Authors

  • Yu Luo
    Department of Radiology, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai 200081, China.
  • Zixiong Jiang
    International Medical College, Chongqing Medical University, Chongqing, China.
  • Zhangcheng Liu
    Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Xiaoqi Deng
  • Jue Wang
    State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa, Macau SAR, China.
  • Shuai Su
    Department of Urology, Urologic Surgery Center, Xinqiao Hospital, Third Military Medical University (Army Medical University), Chongqing, China. [email protected].

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