Use of Bioinformatics and Machine Learning to Identify Circadian Rhythm Disruption- and Endoplasmic Reticulum Stress-Associated Biomarkers in Nonalcoholic Fatty Liver Disease - PubMed
4 hours ago
- #NAFLD
- #circadian rhythm
- #machine learning
- Identified circadian rhythm disruption (CRD) and endoplasmic reticulum stress (ERS) as key factors in nonalcoholic fatty liver disease (NAFLD).
- Used Mendelian randomization (MR) and differential expression analysis to find causal genes and differentially expressed genes (DEGs) in NAFLD.
- Applied weighted gene coexpression network analysis (WGCNA) to identify key NAFLD-associated genes.
- Identified four biomarkers (CREB3, DERL2, LYPLAL1, ERN1) linked to CRD and ERS through machine learning and validation.
- Developed a diagnostic model with high predictive performance (AUC 0.908) and a nomogram for NAFLD risk prediction.
- Found immune cell infiltration changes in NAFLD, including elevated naive B cells, resting dendritic cells, and macrophages M1.
- Biomarkers associated with endoplasmic reticulum protein processing, unfolded protein response (UPR), and branched-chain amino acid degradation.
- Validated biomarker expression in a high-fat, high-cholesterol, and high-fructose diet (HFCFD)-induced NAFLD mouse model.