Integrated Machine Learning Approaches to Explore the Role of Glycosylation-Related Genes in Idiopathic Pulmonary Fibrosis.

Abstract

Idiopathic pulmonary fibrosis (IPF) is a progressive chronic lung disease with an unclear etiology, and the contribution of glycosylation-related genes (GRGs) to its pathogenesis remains poorly understood. This study focuses on elucidating the potential mechanisms of GRGs in IPF, identifying key biomarkers, and developing a diagnostic model using bioinformatics and machine learning. Transcriptomic data from the GEO database were integrated with GRGs to investigate their role in IPF. Differentially expressed genes (DEGs) were identified and subjected to functional enrichment and protein-protein interaction (PPI) analyses. A machine learning workflow combining LASSO regression, support vector machine-recursive feature elimination (SVM-RFE), and XGBoost was applied to identify key genes and construct a diagnostic model using the GSE150910 training dataset. Model performance was subsequently evaluated in four independent validation datasets. Additional analyses, including gene set enrichment analysis (GSEA), immune infiltration analysis, drug-gene interaction analysis, molecular docking, and RT-qPCR validation using peripheral blood samples from IPF patients and healthy controls, were performed to investigate the biological relevance of the identified genes. A total of 126 glycosylation-related DEGs were identified, and 10 key genes were selected. The diagnostic model achieved area under the curve (AUC) values of 0.963 and 0.814 on lung tissue datasets, and 0.681 and 0.706 on blood datasets. Immune infiltration analysis revealed differences in B-cell abundance between patient subgroups, and RT-qPCR validation confirmed the differential expression of selected genes in clinical samples. These findings provide insight into the potential involvement of GRGs in IPF and support their relevance as candidate diagnostic genes.