Identifying disease progression biomarkers in metabolic associated steatotic liver disease (MASLD) through weighted gene co-expression network analysis and machine learning.

ABSTRACT:

Background

Metabolic Associated Steatotic Liver Disease (MASLD), encompassing conditions simple liver steatosis (MAFL) and metabolic associated steatohepatitis (MASH), is the most prevalent chronic liver disease. Currently, the management of MASLD is impeded by the lack of reliable diagnostic biomarkers and effective therapeutic strategies.

Methods

We analyzed eight independent clinical MASLD datasets from the GEO database. Differential expression and weighted gene co-expression network analyses (WGCNA) were used to identify 23 genes related to inflammation. Five hub genes were selected using machine learning techniques (SVM-RFE, LASSO, and RandomForest) combined with a literature review. Nomograms were created to predict MASLD incidence, and the diagnostic potential of the

SUBMITTER: Zhang W 

PROVIDER: S-EPMC12020260 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature