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

ABSTRACT: 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 we

SUBMITTER: Peng M 

PROVIDER: S-EPMC12022275 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature