Identification of hub genes associated with severe COPD via WGCNA and immune infiltration analysis

Abstract

Abstract Chronic Obstructive Pulmonary Disease (COPD) is a progressive respiratory disorder characterized by persistent inflammation and airflow limitation. This study aimed to investigate immune cell infiltration patterns and identify key hub genes associated with severe COPD using integrative bioinformatics analysis. We analyzed transcriptomic data from the GSE76925 dataset, comprising lung tissue samples from 111 individuals with severe COPD (GOLD stage 3–4) and 40 healthy controls. Bioinformatic approaches included weighted gene co-expression network analysis (WGCNA), immune cell infiltration estimation via CIBERSORT, random forest classification, hierarchical clustering, and correlation with clinical parameters such as FEV1 and FEV1/FVC ratios. Our analysis revealed distinct immune infiltration patterns and identified several hub genes significantly correlated with COPD severity. Notably, FEV1/FVC remained a robust clinical marker of disease progression. The hub genes SUMO1, HMGB1, and RBM39 were found to be strongly associated with immune-related pathways and disease severity. This study highlights the value of integrating immune infiltration analysis and gene co-expression networks to better understand the pathogenesis of severe COPD. The identification of key hub genes, including SUMO1, HMGB1, and RBM39, provides insights into potential biomarkers and therapeutic targets for this respiratory disease. Further validation using independent cohorts and functional experiments is warranted to confirm their clinical utility.