Machine learning-driven identification of shared and disease-specific mitochondria-related genes in COPD, NSCLC, and NSCLC with COPD.

Chronic obstructive pulmonary disease (COPD) and non-small-cell lung cancer (NSCLC) often coexist; here, the shared mitochondrial drivers were investigated. Serum from 30 subjects (seven controls, nine COPD, eight NSCLC, and six NSCLC with COPD) underwent RNA-seq, integrated with 1,136 MitoCarta 3.0-derived mitochondrial-related genes (MRGs). DESeq2 identified 25, 124, and 58 mitochondria-related differentially expressed genes (MR-DEGs) in COPD, NSCLC, and their comorbidity, respectively, with 15 and 58 overlapping genes in relevant pairs. SVM-RFE selected two biomarker sets (3-gene and 5-gene), showing excellent diagnostic performance via ROC (AUC 0.89-0.92) and accurate multivariate logistic regression models. GSEA highlighted immune-inflammatory and oxidative phosphorylation pathways; CIBERSORT revealed altered immune cell proportions (e.g., elevated monocytes in COPD) with biomarker-immune cell correlations. CTD linked to NSCLC, and DGIdb identified metformin/ME-344 as potential drugs. These mitochondrial gene signatures, validated in blood as robust classifiers of COPD, NSCLC, and their overlap, simultaneously furnish diagnostic biomarkers and actionable therapeutic targets, underscoring the translational value of mitochondria-immune crosstalk.