Bioinformatics-led discovery of liver-specific genes and macrophage infiltration in acute liver injury

Background Acute liver injury (ALI) is an important global health concern, primarily caused by widespread hepatocyte cell death, coupled with a complex immune response and a lack of effective remedies. This study explores the underlying mechanisms, immune infiltration patterns, and potential targets for intervention and treatment ALI. Methods The datasets of acetaminophen (APAP), carbon tetrachloride (CCl4), and lipopolysaccharide (LPS)-induced ALI were obtained from the GEO database. Differentially expressed genes (DEGs) were individually identified using the limma packages. Functional enrichment analysis was performed using KEGG, GO, and GSEA methods. The overlapping genes were extracted from the three datasets, and hub genes were identified using MCODE and CytoHubba algorithms. Additionally, PPI networks were constructed based on the String database. Immune cell infiltration analysis was conducted using ImmuCellAI, and the correlation between hub genes and immune cells was determined using the Spearman method. The relationship between hub genes, immune cells, and biochemical indicators of liver function (ALT, AST) was validated using APAP and triptolide (TP) -induced ALI mouse models. Results Functional enrichment analysis indicated that all three ALI models were enriched in pathways linked to fatty acid metabolism, drug metabolism, inflammatory response, and immune regulation. Immune analysis revealed a significant rise in macrophage infiltration. A total of 79 overlapping genes were obtained, and 10 hub genes were identified that were consistent with the results of the biological information analysis after screening and validation. Among them, Clec4n, Ms4a6d, and Lilrb4 exhibited strong associations with macrophage infiltration and ALI.