Integrated transcriptomics-metabolomics analysis reveals biomarkers and metabolic dysregulation characteristics of parenteral nutrition-associated liver disease.

Background: Parenteral nutrition-associated liver disease (PNALD) is the most severe complication of long-term parenteral nutrition. It has a high incidence rate and can cause serious harm to patient health. Biomarkers and metabolites associated with PNALD are poorly characterized. This study aimed to identify biomarkers and key metabolites associated with PNALD progression. Methods: A PNALD mouse model was established, and liver tissues were collected for RNA sequencing and non-targeted metabolomics. Differentially expressed genes (DEGs) and differentially expressed metabolites (DEMs) were identified. Candidate biomarkers were identified using machine-learning algorithms (least absolute shrinkage and selection operator and support vector machine-recursive feature elimination). Gene set enrichment analysis (GSEA) and immune cell infiltration analysis were conducted. Finally, the expression of identified biomarkers in clinical samples were validated using reverse transcription quantitative polymerase chain reaction. Results: Histopathological analysis revealed disordered hepatocyte arrangement and mild inflammatory infiltration in PNALD livers, along with significantly increased liver function markers. Transcriptomic and metabolomic analyses revealed 142 DEGs and 18 DEMs. Using the dual machine-learning screening strategy, Conclusion: By leveraging machine learning-aided multi-omics integration, this study identified five biomarkers and three key metabolites that provide novel insights into potential therapeutic targets for PNALD.