Biomarkers of mitochondrial dynamics in idiopathic pulmonary fibrosis: Identification and validation through transcriptomic and single-cell analyses

by Yongbin Wang, Peng Cheng, Zixiang Meng, Ying Zhang, Yan Li, Feifei Feng Background Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic lung disease that has increasingly been associated with dysregulated mitochondrial quality control and dynamics. However, the molecular mechanisms underlying these alterations remain incompletely understood.

This study aimed to systematically identify and validate candidate biomarkers related to mitochondrial dynamics in IPF and to characterize their cell-type specificity and putative regulatory relationships. Methods We integrated bulk transcriptomic datasets from the Gene Expression Omnibus (GEO), single-cell RNA sequencing (scRNA-seq) data, and literature-derived mitochondrial dynamics gene sets.

Candidate genes were identified through differential expression analysis and consensus clustering, followed by functional enrichment and protein–protein interaction (PPI) network analyses. A total of 101 machine-learning model combinations—including random forest, LASSO, and support vector machine—were constructed to select optimal feature genes.

Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis and further evaluated with artificial neural network (ANN) modeling.