Identification of signature gene set as highly accurate determination of metabolic dysfunction-associated steatotic liver disease progression.

ABSTRACT:

Background/aims

Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by fat accumulation in the liver. MASLD encompasses both steatosis and MASH. Since MASH can lead to cirrhosis and liver cancer, steatosis and MASH must be distinguished during patient treatment. Here, we investigate the genomes, epigenomes, and transcriptomes of MASLD patients to identify signature gene set for more accurate tracking of MASLD progression.

Methods

Biopsy-tissue and blood samples from patients with 134 MASLD, comprising 60 steatosis and 74 MASH patients were performed omics analysis. SVM learning algorithm were used to calculate most predictive features. Linear regression was applied to find signature gene set that distinguish the stage of MASLD and to validate th

SUBMITTER: Oh S 

PROVIDER: S-EPMC11016492 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature