From Local Tissue Repair to Fibrosis: Deciphering Gene Co-Expression Networks in Benign Pulmonary Nodules and Idiopathic Pulmonary Fibrosis Comorbidity via Bioinformatics and Machine Learning.

With increasing environmental pollution and a high incidence of respiratory infections, pulmonary nodules (PN) are being detected more frequently. Although most are benign, they are often accompanied by chronic inflammation and localized fibrosis, which may predispose patients to progression toward idiopathic pulmonary fibrosis (IPF). However, the biological relationship between benign pulmonary nodules (BPNs) and IPF remains poorly understood. Therefore, this study aims to investigate the shared molecular mechanisms and identify potential biomarkers linking BPN and IPF, with the goal of elucidating the pathogenic transition from BPN to IPF. In this study, microarray data from GEO datasets were systematically analyzed to explore shared molecular mechanisms, immune infiltration characteristics, and potential early intervention strategies linking BPN and IPF. Differential expression analysis, protein-protein interaction (PPI) networks, weighted gene co-expression network analysis (WGCNA), and integrative machine learning approaches identified