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<p>Hepatocellular carcinoma (HCC) has a poor prognosis, particularly with spinal metastases. Current prognostic scores (e.g., Revised Tokuhashi, New England Spinal Metastasis Score) lack integration of tumor microenvironment (TME)-based molecular subtypes, limiting their utility in precision medicine. This study evaluated the prognostic value of these subtypes and whether they enhance established scoring systems.&#xa0;In a single-center retrospective cohort of 117 HCC patients undergoing surgery for spinal metastases (2009–2024), patients were stratified into three TME subtypes: immune-inflamed (n = 39), immune-excluded (n = 53), and immune-desert (n = 25). Overall survival (OS) was analyzed using Kaplan-Meier and Cox regression. The discriminative ability of four prognostic scores was assessed with time-dependent ROC curves. Recursive partitioning analysis (RPA) integrated molecular subtypes with clinical scores to develop novel decision trees.&#xa0;Median OS for the cohort was 13.1 months. TME subtype was a powerful independent prognostic factor, with immune-inflamed, immune-excluded, and immune-desert subtypes showing median OS of 17.2, 12.1, and 8.8 months, respectively (P &lt; 0.001). Multivariable analysis confirmed this association (e.g., immune-desert aHR = 9.52, P &lt; 0.001). The Revised Tokuhashi score showed the highest baseline discriminative ability for 1-year survival (AUROC = 0.726). Integrating TME subtype and postoperative systemic therapy significantly improved predictive accuracy across all models (AUROCs &gt; 0.92). RPA generated clinically actionable decision trees, defining three distinct prognostic groups. TME-based molecular subtypes are critical independent survival determinants in HCC with spinal metastases. Their integration with clinical scores using RPA produces highly accurate predictive models and practical decision aids, advocating for a biology-augmented approach to personalize patient management.</p>

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Prognostic value of tumor microenvironment-based molecular subtypes in hepatocellular carcinoma patients undergoing surgery for spinal metastases: refining conventional scoring systems

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月9日

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  • Bing Liang,
  • Annan Hu,
  • Jian Zhou,
  • Juan Li,
  • Jian Dong

摘要

Hepatocellular carcinoma (HCC) has a poor prognosis, particularly with spinal metastases. Current prognostic scores (e.g., Revised Tokuhashi, New England Spinal Metastasis Score) lack integration of tumor microenvironment (TME)-based molecular subtypes, limiting their utility in precision medicine. This study evaluated the prognostic value of these subtypes and whether they enhance established scoring systems. In a single-center retrospective cohort of 117 HCC patients undergoing surgery for spinal metastases (2009–2024), patients were stratified into three TME subtypes: immune-inflamed (n = 39), immune-excluded (n = 53), and immune-desert (n = 25). Overall survival (OS) was analyzed using Kaplan-Meier and Cox regression. The discriminative ability of four prognostic scores was assessed with time-dependent ROC curves. Recursive partitioning analysis (RPA) integrated molecular subtypes with clinical scores to develop novel decision trees. Median OS for the cohort was 13.1 months. TME subtype was a powerful independent prognostic factor, with immune-inflamed, immune-excluded, and immune-desert subtypes showing median OS of 17.2, 12.1, and 8.8 months, respectively (P < 0.001). Multivariable analysis confirmed this association (e.g., immune-desert aHR = 9.52, P < 0.001). The Revised Tokuhashi score showed the highest baseline discriminative ability for 1-year survival (AUROC = 0.726). Integrating TME subtype and postoperative systemic therapy significantly improved predictive accuracy across all models (AUROCs > 0.92). RPA generated clinically actionable decision trees, defining three distinct prognostic groups. TME-based molecular subtypes are critical independent survival determinants in HCC with spinal metastases. Their integration with clinical scores using RPA produces highly accurate predictive models and practical decision aids, advocating for a biology-augmented approach to personalize patient management.