Development of Diagnostic and Predictive Models for COPD Based on Anoikis Resistance

Chronic obstructive pulmonary disease (COPD) pathogenesis involves persistent airway inflammation and remodeling, yet the role of anoikis resistance remains poorly characterized. This study aimed to identify anoikis resistance-related hub genes and evaluate their clinical utility in COPD phenotyping and prognosis.

Integrated bioinformatics analysis of the GSE11906 dataset identified anoikis resistance-related differentially expressed genes (DEGs). Functional enrichment, LASSO regression, and machine learning (RF, SVM, XGB, GLM) were employed to pinpoint core hub genes. Multi-level validation included external datasets (GSE19407), in vitro (CSE-stimulated 16HBE cells), in vivo (cigarette smoke-exposed mice), and clinical samples (PBMCs). Diagnostic and prognostic models were developed using logistic regression.

Five core hub genes (UCHL1, ME1, SLC2A1, BMP4, CRABP2) were identified, with ME1, SLC2A1, and BMP4 consistently upregulated in COPD across models and strongly correlated with emphysema index (negative, R = -0.41 to -0.45) and airway wall thickness (positive, R = 0.40-0.45). These genes exhibited significant associations with peribronchial immune cell infiltration. Diagnostic models for emphysema-predominant COPD (AUC = 0.860) and disease staging (AUC = 0.882), along with a prognostic model for hospitalization duration (AUC = 0.867), demonstrated robust clinical performance.

ME1, SLC2A1, and BMP4 are pivotal anoikis resistance-related biomarkers in COPD, driving immune dysregulation and structural remodeling. The developed models enable precise phenotyping, severity stratification, and personalized prognosis prediction, advancing precision medicine strategies for COPD management.