Identification and verification of mitochondria-related genes biomarkers associated with immune infiltration for COPD using WGCNA and machine learning algorithms

Mitochondrial dysfunction plays a pivotal role in the pathogenesis of chronic obstructive pulmonary disease (COPD). This study combines bioinformatics analysis with machine learning to elucidate potential key mitochondrial-related genes associated with COPD and its immune microenvironment. We utilized the limma package and Weighted Gene Co-expression Network Analysis (WGCNA) to analyze datasets from the Gene Expression Omnibus (GEO) database (GSE57148), identifying 12 key differentially expressed mitochondrial genes (MitoDEGs). Using 12 distinct machine learning algorithms (comprising 143 predictive models), we identified the optimal diagnostic model, which includes five pivotal MitoDEGs: ERN1, FASTK, HIGD1B, NDUFA7 and NDUFB7. The diagnostic specificity and sensitivity of each gene, as well as the diagnostic model itself, were evaluated using Receiver operating characteristic (ROC) curves. This model demonstrated high specificity in the validation cohorts (GSE76925, GSE151052, GSE239897). Expression analysis revealed upregulation of ERN1 and downregulation of FASTK, HIGD1B, NDUFA7 and NDUFB7 in COPD patients. Spearman's correlation analysis indicated a significant association between MitoDEGs and immune cell infiltration, with ERN1 expression positively correlated with neutrophil infiltration and the other genes negatively correlated. The GABA receptor modulator androstenol was identified as a potential therapeutic candidate. In vivo studies confirmed reduced mRNA expression of HIGD1B and NDUFB7 in COPD mice. These findings elucidate mitochondrial-immune interactions in COPD and highlight novel diagnostic and therapeutic targets.