Machine learning and experimental validation identified autophagy signature in hepatic fibrosis.

ABSTRACT:

Background

The molecular mechanisms of hepatic fibrosis (HF), closely related to autophagy, remain unclear. This study aimed to investigate autophagy characteristics in HF.

Methods

Gene expression profiles (GSE6764, GSE49541 and GSE84044) were downloaded, normalized, and merged. Autophagy-related differentially expressed genes (ARDEGs) were determined using the limma R package and the Wilcoxon rank sum test and then analyzed by GO, KEGG, GSEA and GSVA. The infiltration of immune cells, molecular subtypes and immune types of healthy control (HC) and HF were analyzed. Machine learning was carried out with two methods, by which, core genes were obtained. Models of liver fibrosis in vivo and in vitro were constructed to verify the expression of core genes and corre

SUBMITTER: Huang Y 

PROVIDER: S-EPMC10933073 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature