Development and external validation of a mitophagy-related diagnostic model for biliary atresia based on cellular infiltration patterns.

BACKGROUND: Biliary atresia (BA) is a progressive disorder that aggravates liver inflammation and fibrosis in infants. Despite its clinical significance, the involvement of mitophagy in the pathogenesis and progression of BA remains poorly understood. Our study aims to explore mitophagy-related diagnostic model for BA. METHODS: This study delineated, for the first time, the specific infiltration states of 13 cell types in BA liver tissue. Using weighted gene co-expression network analysis (WGCNA), we identified 29 mitophagy-related genes correlated with cellular infiltration patterns. Subsequently, least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms were applied to refine a diagnostic model comprising four key genes. The diagnostic performance of the model was validated using two independent Gene Expression Omnibus (GEO) datasets. Expression patterns of the model genes in BA were further verified through GEO datasets, single-cell transcriptomics, and immunohistochemistry. Finally, drug screening based on these genes was conducted using Alphafold3 and molecular interaction simulations to identify potential therapeutic agents. RESULTS: The Biliary Atresia Cell Infiltration Mitophagy Diagnostic Gene Model (BA-CIMDGM) incorporated four key genes: NEDD4L, IGF2BP3, ALDH2, and PPIB, and robust performance in both external GEO datasets, with area under the curve (AUC) values exceeding 0.83. Expression validation confirmed BA-specific profiles for three of the model genes. Through computational drug screening, Cyclosporine was identified as a promising candidate for BA treatment. CONCLUSIONS: This study establishes the BA-CIMDGM model as a valuable diagnostic tool for BA and proposes Cyclosporine as a potential pharmacological intervention. These findings underscore the clinical relevance of mitophagy-related genes and offer novel insights for improving both diagnosis and therapy in BA.