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

Area of Science:

• Hepatology and Molecular Biology
• Data-driven Disease Modeling
• Biomarker Discovery

Background:

• Metabolic dysfunction-associated steatotic liver disease (MASLD) is a progressive condition, but current staging methods are static and fail to capture its continuous nature.
• Conventional frameworks rely on histology-defined stages, limiting understanding of dynamic disease progression and underlying molecular changes.

Purpose of the Study:

• To develop a data-driven framework for reconstructing MASLD progression as a continuous molecular trajectory.
• To identify non-invasive biomarkers for patient stratification and precise disease staging.

Main Methods:

• Utilized cross-sectional liver transcriptomic profiles to build a molecular trajectory of MASLD progression.
• Integrated transcriptomic trajectory with liver-plasma proteomics data.
• Developed and validated a 57-gene plasma biomarker panel.

Main Results:

• Established a continuous molecular trajectory for MASLD progression, revealing ordered activation of regulatory programs and pathways.
• Identified a 57-gene plasma biomarker panel that accurately predicts advanced fibrosis.
• Demonstrated superior performance of the biomarker panel over established clinical scores in independent cohorts for patient positioning along the disease trajectory.

Conclusions:

• The trajectory-based framework provides a generalizable approach to understanding MASLD pathophysiology.
• The identified biomarker panel enables precise, non-invasive staging and supports mechanistically informed biomarker discovery.
• This work lays the foundation for stage-aware therapeutic prioritization in MASLD management.