.png){kind=icon}
.png){kind=icon}
Return
A data-driven framework reconstructs the molecular continuum of human MASLD progression
.png){kind=icon}
OA
AI
I
T
C
A
H
N
Y
Q
M
I
A
E
DOI：10.1038/s42255-026-01543-7.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. A trajectory-based molecular framework reveals coordinated temporal shifts in metabolic, inflammatory and fibrogenic mechanisms across the human MASLD spectrum.
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
20.8
Papers:
1.6K
Citations:
1.4W
Organization
Cited Papers