Abstract
This study aimed to identify key biomarkers for obstructive sleep apnoea (OSA) by analysing RNA-seq (GSE75097: 6 control and 42 OSA) and single-cell RNA-seq (GSE214865: 11 control and 11 OSA) datasets. Differential expression analysis revealed 575 DEGs between OSA and control groups. Using weighted gene co-expression network analysis (WGCNA) combined with machine learning approaches (Random Forest, SVM-RFE and LASSO), we identified four hub genes (C17ORF91, HBEGF, PGBD2 and ZNF616) as candidate biomarkers, showing exploratory discriminatory performance (AUC > 0.7). Gene set enrichment analysis (GSEA) revealed that these genes are associated with pathways related to hypoxia response, atherosclerosis and metabolic reprogramming (NES as low as -2.049, adj. p p < .05). Single-cell RNA sequencing analysis further demonstrated that cMonocytes play a central role in OSA progression, exhibiting the highest scores for hypoxia, metabolic dysfunction and atherosclerosis risk. An exploratory nomogram incorporating these hub genes showed favourable internal performance within the discovery cohort. These findings provide hypothesis-generating insights into the molecular features associated with OSA and identify these genes as candidate biomarkers that require independent external and functional validation. Further validation of these genes may provide a basis for evaluating their potential diagnostic and therapeutic relevance in OSA.