Integrating bulk and single-cell RNA sequencing data to dissect genetic links between periodontitis and obstructive...

Periodontitis (PD) and obstructive sleep apnea (OSA) are widespread conditions with profound health consequences. Increasing evidence suggests shared pathophysiological mechanisms between PD and OSA, prompting this study to explore their genetic connections using advanced transcriptomic approaches.

Gene expression data was obtained from GEO, integrating bulk and single-cell RNA sequencing (scRNA-seq). Differentially expressed genes (DEGs) were identified, and common DEGs were analyzed via protein-protein interaction (PPI) networks and functional enrichment. Machine learning algorithms, including LASSO, SVM-RFE, and Boruta, were used to screen out hub genes. Expression patterns, diagnostic accuracy, and immune infiltration were assessed. Then, the single-cell analysis was utilized to evaluate cell-specific expression and effects of virtual hub gene knockouts. Drug candidates were predicted using the DSigDB database.

In total, 37 common DEGs were identified, in which PECAM1, FCER1G, and THY1 were designated as hub genes. The hub genes were significantly upregulated in disease states, achieving high diagnostic accuracy (AUC > 0.85). Immune infiltration profiles showed differences between the disease and control groups, with hub gene expression positively correlated to plasma cells and M0 macrophages abundance. Single-cell annotation mapped hub gene expression to distinct cell types. Virtual hub gene knockouts highlighted disrupted pathways including oxygen transport and DNA double-strand break repair. Candidate drugs, including pergolide and aspirin, were proposed.

This study investigates genetic links between PD and OSA, identifying PECAM1, FCER1G, and THY1 as important diagnostic and therapeutic targets. Integrating multi-omics and machine learning provides a comprehensive approach to unravelling disease interplay and advancing treatment strategies.