Machine learning-driven identification of shared and disease-specific mitochondria-related genes in COPD, NSCLC, and NSCLC with COPD
Siyu Wu, Zelin Chen, Tongxinwei Sun, Beibei Song, Xinxiu Liu, Liwen Zhang, Jing Li, Haoran Lu, Wenhui Song, Aihong Meng
TL;DR
This study identifies blood-based mitochondrial genes that can distinguish COPD, NSCLC, and their coexistence, offering potential diagnostic and treatment options.
Contribution
The study introduces novel mitochondrial gene signatures and drug repurposing candidates for COPD, NSCLC, and their comorbidity.
Findings
Mitochondrial gene signatures (NDUFB6/BID/COX7A2) achieved high diagnostic accuracy (AUC >0.7) across COPD, NSCLC, and their overlap.
Machine learning models revealed mitochondria-immune crosstalk in disease progression and immune cell composition changes.
Metformin and ME-344 were identified as potential repurposed drugs targeting mitochondrial genes in these conditions.
Abstract
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…
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Taxonomy
TopicsMachine Learning in Bioinformatics · Chronic Obstructive Pulmonary Disease (COPD) Research · Genomics and Rare Diseases