Abstract
Background/Objectives: Idiopathic pulmonary fibrosis (IPF) is a progressive disease characterized by lung scarring, impaired function, and high mortality. Effective therapies to reverse fibrosis are lacking. This study aims to uncover the molecular mechanisms of IPF, explore diagnostic biomarkers, and identify therapeutic targets. Methods: Multi-omics data were integrated to identify biomarkers with causal associations to IPF using Mendelian randomization and transcriptomic analysis. Machine learning was employed to construct a diagnostic model, and single-cell transcriptomic analysis determined gene expression patterns in fibrotic lung tissue. Results: Seven core genes (GREM1, UGT1A6, CDH2, TDO2, HS3ST1, ADGRF5, and MPO) were identified, showing strong diagnostic potential (AUC = 0.987, 95% CI: 0.972-0.987). These genes exhibited distinct distribution patterns in fibroblasts, endothelial cells, epithelial cells, macrophages, and dendritic cells. Conclusions: This study highlights key genes driving IPF, involved in pathways related to metabolism, immunity, and inflammation. However, their utility as fluid-based biomarkers remains unproven and requires protein-level validation in prospective cohorts. By integrating genomic, immunological, and cellular insights, it provides a framework for targeted therapies and advances mechanism-based precision medicine for IPF.