A Synthetic Lethality-Informed Multi-Omic Framework for Identifying a Five-Gene Diagnostic Signature in Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) lacks reliable molecular biomarkers for early diagnosis and risk stratification beyond conventional spirometry-based assessment. Synthetic lethality (SL)-related gene prioritization provides a biologically informed framework for identifying disease-associated candidate biomarkers in COPD. In this study, we integrated public transcriptomic datasets, SL-related gene sets, and machine learning approaches to identify a diagnostic signature for COPD. Using GSE47460 as the training cohort (220 COPD and 108 controls) and GSE57148 as the external validation cohort (98 COPD and 91 controls), we identified 74 SL-related differentially expressed genes enriched in inflammatory signaling and extracellular matrix organization. LASSO regression and random forest analysis yielded a five-gene diagnostic signature consisting of CYP1B1, VEGFA, RET, FGG, and S100A9. The integrated nomogram showed good diagnostic performance in the validation cohort, with an AUC of 0.8311 (95% CI: 0.7839–0.8783), outperforming individual genes and supporting its potential use as an adjunctive molecular tool for COPD diagnosis and risk assessment. Single-cell RNA sequencing, immune infiltration analysis, and preliminary in vitro experiments further supported the biological relevance of the identified genes. Overall, this study supports SL-related gene prioritization combined with multi-omic integration as a useful strategy for COPD biomarker discovery while generating testable hypotheses regarding disease-associated vulnerability pathways.