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: were further screened and identified as biomarkers, whereas 6-n-octylaminouracil, 6β-hydroxy-hydromorphone, and α teresantalic acid were identified as key metabolites. GSEA indicated that these biomarkers were enriched in the cytokine-cytokine receptor interaction and oxidative phosphorylation pathways. Immune infiltration analysis revealed positive correlations between the biomarkers and 19 cell types, particularly myeloid-derived suppressor cells. Clinical validation confirmed consistent expression trends for all five biomarkers with prior analyses. 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.