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 MME and ANKRD23 as key hub genes associated with the transition from BPN to IPF. Both genes demonstrated strong diagnostic performance, with Area Under the Curve (AUC) values exceeding 0.7, and were significantly correlated with immune cell infiltration, particularly effector memory CD8+ T cells. Functional enrichment and gene set enrichment analyses indicated that these genes were mainly involved in immune-related processes in BPN, while in IPF, ANKRD23 was linked to cytoskeletal organization and genomic stability, and MME was enriched in profibrotic pathways such as TGF-β signaling. The diagnostic value of these biomarkers was further validated in a bleomycin-induced IPF mouse model using quantitative polymerase chain reaction (qPCR). In addition, drug–gene interaction prediction and molecular docking analyses highlighted several naturally derived compounds with favorable binding affinity and anti-inflammatory properties, among which folic acid, curcumin, and arbutin emerged as promising candidates for safe early intervention. Collectively, these findings identify MME and ANKRD23 as potential biomarkers for early identification of BPN patients at risk of developing IPF and provide a theoretical basis for early diagnosis and targeted preventive strategies.