Identification of Glycosylation-Related Biomarkers in COPD and IPF Through Integrated Machine Learning and WGCNA Analysis

Objective: This research aimed to explore key glycosylation-related genes (signature genes) and associated molecular mechanism on chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF), which further providing new perspectives for disease prognosis and diagnose. Patients and Methods: The gene expression profiles were obtained from the public GEO database. The glycosylation-related genes were identified based co-DEGs from COPD vs normal samples and IPF vs normal samples, module genes by weighted gene co-expression network analysis (WGCNA), as well as glycosylation genes from database. Signature genes were screened using machine learning methods, followed by immune infiltration, function analysis, drug-gene and transcriptional regulatory network analysis. Finally, validation analysis based on tissue samples from COPD/IPF patients were performed to test the expression of signature genes. Results: < 0.05), providing crucial evidence for biomarker research and pathogenesis exploration of the two diseases. Conclusion: We identified SULF1, ST8SIA1 and FCN3 as shared glycosylation-related biomarkers in COPD and IPF. These genes bridge fibrosis, inflammation, and immune dysregulation, offering potential diagnostic and therapeutic targets.