Identification of shared biomarkers for obstructive sleep apnea and sarcopenia via differential expression and WGCNA analysis.

BACKGROUND

Obstructive sleep apnea (OSA) and sarcopenia are common age-related conditions that significantly reduce quality of life. OSA is characterized by recurrent breathing interruptions and chronic intermittent hypoxia, while sarcopenia involves progressive loss of muscle mass and strength. Accumulating evidence indicates that chronic intermittent hypoxia and sleep fragmentation in OSA may promote muscle wasting via inflammatory pathways (e.g., NF-κB), oxidative stress, and impaired protein synthesis. Conversely, sarcopenia-related muscle weakness may worsen upper airway collapsibility, creating a vicious cycle. However, the molecular mechanisms linking OSA and sarcopenia remain poorly understood.

METHODS

We performed differential expression analysis on OSA and sarcopenia datasets from the Gene Expression Omnibus (GEO) and used weighted gene co-expression network analysis (WGCNA) to identify candidate biomarkers, with functional enrichment assessed under false discovery rate (FDR) correction. A diagnostic model was constructed based on key genes and validated using receiver operating characteristic (ROC), calibration, and decision curves. Immune cell infiltration was analyzed using the CIBERSORT algorithm.

RESULTS

We identified 123 differentially expressed genes shared by OSA and sarcopenia, enriched in pathways related to muscle function, cellular stress, metabolic disorders, and neurodegeneration. WGCNA identified ESF1, ZNF117, and C2orf49 as core genes. The diagnostic model showed high accuracy (area under the curve (AUC): 0.912 for OSA, and 0.967 for sarcopenia in training; 0.787 for OSA, and 0.744 for sarcopenia in validation). These genes were closely associated with pro-inflammatory immune cells.

CONCLUSION

This study identifies ESF1, ZNF117, and C2orf49 as potential shared biomarkers for OSA and sarcopenia. The robust diagnostic model and the observed tissue-specific gene dysregulation illuminate the systemic interplay between these conditions, offering insights for risk stratification and future mechanistic research.