Computational identification of key genetic drivers in COPD: A first step towards uncovering candidate biomarkers in smokers.

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a leading challenge of global public health that predominantly affects developing countries. Although smoking is the main risk factor, only a fraction of smokers develop COPD. This study aimed to identify biomarkers or therapeutic targets that would effectively aid early diagnosis and treatment of smoking-induced COPD.

METHODS AND RESULTS

After retrieving GSE27597, GSE38974, GSE47460, GSE76925, and GSE239897 from the Gene Expression Omnibus, never-smokers were excluded from each dataset. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to discern a reliable gene list. Subsequently, integrated data, which incorporated 120 control and 349 COPD samples, was analyzed by random forest (RF) and least absolute shrinkage and selection operator (LASSO) methods to identify key genes. Lastly, 6 genes with the area under the receiver operating characteristic curve exceeding 0.7 were selected as potential biomarkers of smoking-induced COPD. , , , , , and exhibited similar expression patterns across the datasets, reflecting their prominent contribution to COPD pathogenesis.

CONCLUSION

These results suggested , , , , , and as key genes for the development of COPD among smokers. While these potential biomarkers were identified by computational analyses, experimental studies are needed to evaluate their clinical applicability.