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<p>Metabolic dysfunction-associated steatotic liver disease (MASLD) progresses along a continuum from simple steatosis to steatohepatitis, fibrosis, cirrhosis and hepatocellular carcinoma. However, current clinical and research frameworks rely primarily on static, histology-defined stages that fail to capture the continuous nature of disease progression. Here, we present a data-driven framework that reconstructs MASLD progression as a continuous molecular trajectory from cross-sectional liver transcriptomic profiles. By positioning patients along this trajectory, we move beyond conventional stage-based classifications and resolve the ordered activation of regulatory programmes, signalling pathways and cellular remodelling processes underlying disease progression. To enable non-invasive patient stratification, we integrate the inferred molecular trajectory with paired liver-plasma proteomics data and identify a 57-gene plasma-accessible biomarker panel that accurately predicts advanced fibrosis and continuously positions patients along the disease trajectory across independent cohorts, outperforming established non-invasive clinical scores. Together, this work establishes a generalizable trajectory-based framework for understanding MASLD pathophysiology and provides a foundation for mechanistically informed biomarker discovery, precision staging and stage-aware therapeutic prioritization.</p>

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A data-driven framework reconstructs the molecular continuum of human MASLD progression

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月14日

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  • Ioannis Kamzolas,
  • Thodoris Koutsandreas,
  • Charlie George Barker,
  • Anna Vathrakokoili Pournara,
  • Harry Weston,
  • Naoto Fujiwara,
  • Yujin Hoshida,
  • Quentin M. Anstee,
  • Michele Vacca,
  • Irene Papatheodorou,
  • Antonio Vidal-Puig,
  • Evangelia Petsalaki

摘要

Metabolic dysfunction-associated steatotic liver disease (MASLD) progresses along a continuum from simple steatosis to steatohepatitis, fibrosis, cirrhosis and hepatocellular carcinoma. However, current clinical and research frameworks rely primarily on static, histology-defined stages that fail to capture the continuous nature of disease progression. Here, we present a data-driven framework that reconstructs MASLD progression as a continuous molecular trajectory from cross-sectional liver transcriptomic profiles. By positioning patients along this trajectory, we move beyond conventional stage-based classifications and resolve the ordered activation of regulatory programmes, signalling pathways and cellular remodelling processes underlying disease progression. To enable non-invasive patient stratification, we integrate the inferred molecular trajectory with paired liver-plasma proteomics data and identify a 57-gene plasma-accessible biomarker panel that accurately predicts advanced fibrosis and continuously positions patients along the disease trajectory across independent cohorts, outperforming established non-invasive clinical scores. Together, this work establishes a generalizable trajectory-based framework for understanding MASLD pathophysiology and provides a foundation for mechanistically informed biomarker discovery, precision staging and stage-aware therapeutic prioritization.