Neutrophil extracellular trapping network-associated biomarkers in liver fibrosis: machine learning and experimental validation.

ABSTRACT:

Background

The diagnostic and therapeutic potential of neutrophil extracellular traps (NETs) in liver fibrosis (LF) has not been fully explored. We aim to screen and verify NETs-related liver fibrosis biomarkers through machine learning.

Methods

In order to obtain NETs-related differentially expressed genes (NETs-DEGs), differential analysis and WGCNA analysis were performed on the GEO dataset (GSE84044, GSE49541) and the NETs dataset. Enrichment analysis and protein interaction analysis were used to reveal the candidate genes and potential mechanisms of NETs-related liver fibrosis. Biomarkers were screened using SVM-RFE and Boruta machine learning algorithms, followed by immune infiltration analysis. A multi-stage model of fibrosis in mice was constructed, and neutrophil in

SUBMITTER: Li Y 

PROVIDER: S-EPMC12502331 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature