Identification and Validation of Mannose Metabolism-Related Biomarkers in COPD Through Integrated Bioinformatics and Machine Learning Analysis: A Pilot Study

Abstract

Background Chronic obstructive pulmonary disease (COPD) represents a progressive respiratory disorder marked by sustained airflow restriction and ongoing inflammatory processes. Recently, mannose metabolism has emerged as a significant factor in chronic disease development. This investigation explored how mannose metabolism-related genes (MMRGs) contribute to COPD pathogenesis and evaluated their utility as candidate diagnostic and therapeutic targets. Methods We obtained blood sample gene expression data from COPD patients and healthy controls via the GEO database. Differentially expressed genes (DEGs) were identified and intersected with MMRGs to obtain candidate genes. Three machine learning algorithms combined with expression validation across independent datasets were applied to identify biomarkers. A nomogram prediction model was constructed and its diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis. Subsequently, gene set enrichment analysis (GSEA), immune infiltration analysis, drug prediction, and molecular docking were performed. Results Nineteen candidate genes were identified from 1685 DEGs and subsequently screened for two biomarkers: MAN1C1 and MAN2B2. A nomogram model constructed on the basis of the two showed moderate discriminatory efficacy (area under the curve (AUC) = 0.701). In addition, GSEA analysis showed that both were co-enriched in pathways such as TNF’s target up-regulated gene sets. The immune infiltration results revealed significant differences (p < 0.05) between COPD and controls in a total of 12 categories of immune cells, such as activated B cells. Finally, drug prediction revealed 12 and 3 potential drugs for MAN1C1 and MAN2B2, respectively, with trichostatin A showing a potential binding conformation. Conclusion This study revealed the potential roles of MMRGs in COPD and identified novel biomarkers. These findings provided new insights and research foundations for the early diagnosis, personalized treatment, and drug development of COPD.