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<p>Prostate cancer (PCa) exhibits marked metabolic heterogeneity, yet the prognostic implications of amino acid metabolism remain insufficiently characterized. In this study, we developed a 9-gene amino acid metabolic risk signature through an integrative analysis of single-cell and bulk transcriptomic datasets, leveraging machine learning to stratify patients into distinct prognostic subgroups. The model demonstrated robust predictive accuracy in both TCGA and independent GEO cohorts, with significant associations to immune microenvironment remodeling and therapeutic vulnerabilities. Mechanistically, multi-omics analyses (SCENIC, CellChat, pseudotime trajectory) delineated regulatory networks underlying amino acid metabolic dysregulation, highlighting FUS as a potential oncogenic regulator. Experimental validation across cellular, murine, and human models supported a role for FUS in promoting tumor aggressiveness. Through bioinformatic analysis, we identified potential signaling pathways underlying FUS involvement in prostate cancer progression. Our study establishes a clinically actionable amino acid metabolic signature and nomogram for PCa risk stratification, while suggesting FUS as a candidate therapeutic target. These findings bridge computational discovery with mechanistic validation, providing novel insights into the amino acid metabolic dependencies that govern prostate cancer (PCa) progression.</p>

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Integrated scRNA-seq and bulk transcriptomics identify an amino acid metabolism-associated prognostic signature and highlight FUS as a potential driver in prostate cancer progression

  • 期刊论文

  • 发表日期：

    2026年3月2日

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  • Jiangbei Yuan,
  • Dawei Shen,
  • Zixiang Pan,
  • Fei Lv,
  • Yue Zhao,
  • Wei Zheng,
  • Qiaoqiao Yin,
  • LanJie Wu,
  • Jianli Yu,
  • Cheng’an Xu,
  • Qiang He,
  • Hongying Pan

摘要

Prostate cancer (PCa) exhibits marked metabolic heterogeneity, yet the prognostic implications of amino acid metabolism remain insufficiently characterized. In this study, we developed a 9-gene amino acid metabolic risk signature through an integrative analysis of single-cell and bulk transcriptomic datasets, leveraging machine learning to stratify patients into distinct prognostic subgroups. The model demonstrated robust predictive accuracy in both TCGA and independent GEO cohorts, with significant associations to immune microenvironment remodeling and therapeutic vulnerabilities. Mechanistically, multi-omics analyses (SCENIC, CellChat, pseudotime trajectory) delineated regulatory networks underlying amino acid metabolic dysregulation, highlighting FUS as a potential oncogenic regulator. Experimental validation across cellular, murine, and human models supported a role for FUS in promoting tumor aggressiveness. Through bioinformatic analysis, we identified potential signaling pathways underlying FUS involvement in prostate cancer progression. Our study establishes a clinically actionable amino acid metabolic signature and nomogram for PCa risk stratification, while suggesting FUS as a candidate therapeutic target. These findings bridge computational discovery with mechanistic validation, providing novel insights into the amino acid metabolic dependencies that govern prostate cancer (PCa) progression.