JoVE Video Dataset

Acetaminophen-induced liver injury is a significant public health concern, yet reliable early biomarkers are lacking. This study aimed to identify candidate biomarkers for acetaminophen-induced hepatotoxicity using a computational approach integrating network toxicology, transcriptomics, and machine learning. Potential acetaminophen targets were predicted using online platforms, yielding 140 candidates. Hepatotoxicity-related genes (n = 657) were retrieved from GeneCards, and 38 overlapping genes were identified. Differentially expressed genes from the GSE74000 dataset (n = 1,978) were analyzed. Functional enrichment was performed to identify relevant pathways. A random forest model prioritized 20 feature genes, and molecular docking evaluated binding affinities with acetaminophen. DEGs were primarily associated with mitochondrial dysfunction and ribosome biogenesis. Functional enrichment highlighted xenobiotic metabolism and oxidative stress pathways. Estrogen receptor 1 and purine nucleoside phosphorylase were top-ranked feature genes, showing significant expression differences and strong docking interactions with acetaminophen. This computational protocol systematically predicts candidate biomarkers for acetaminophen-induced liver injury, providing molecular insights and candidates for experimental validation.