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

Area of Science:

• Biomedical research
• Genomics
• Pulmonary medicine

Background:

• Chronic obstructive pulmonary disease (COPD) involves airway inflammation and remodeling.
• The role of anoikis resistance in COPD pathogenesis is not well understood.

Purpose of the Study:

• Identify anoikis resistance-related hub genes in COPD.
• Evaluate the clinical utility of these genes for COPD phenotyping and prognosis.

Main Methods:

• Bioinformatics analysis of gene expression datasets (GSE11906, GSE19407).
• Utilized LASSO regression and machine learning (RF, SVM, XGB, GLM) to identify hub genes.
• Validated findings in vitro (CSE-stimulated cells), in vivo (mouse models), and in clinical samples (PBMCs).

Main Results:

• Identified five core hub genes: UCHL1, ME1, SLC2A1, BMP4, and CRABP2.
• ME1, SLC2A1, and BMP4 were consistently upregulated in COPD and correlated with emphysema and airway wall thickness.
• Developed diagnostic models for emphysema-predominant COPD (AUC=0.860) and disease staging (AUC=0.882).
• Created a prognostic model for hospitalization duration (AUC=0.867).

Conclusions:

• ME1, SLC2A1, and BMP4 are critical biomarkers in COPD, influencing immune response and structural changes.
• The developed models offer precise phenotyping, severity stratification, and personalized prognosis for COPD patients.
• These findings support precision medicine approaches in COPD management.