Screening and identification of potential biomarkers for obstructive sleep apnea via microarray analysis

Area of Science:

• Genomics
• Bioinformatics
• Sleep Medicine

Background:

• Obstructive sleep apnea (OSA) is a prevalent chronic condition with significant cardiovascular, metabolic, and neuropsychiatric implications.
• OSA imposes a substantial socioeconomic burden due to its widespread impact on public health.

Purpose of the Study:

• To identify key genes influencing the mechanisms and consequences of obstructive sleep apnea.
• To uncover potential novel biomarkers for OSA diagnosis and management.

Main Methods:

• Utilized Gene Expression Omnibus (GEO) database for OSA-related gene expression profiles.
• Screened differentially expressed genes (DEGs) in adipose tissue using R software.
• Constructed a protein-protein interaction (PPI) network and identified hub genes using STRING and Cytoscape.
• Validated hub genes in an independent dataset and performed receiver operating characteristic (ROC) and Pearson correlation analyses.

Main Results:

• Identified 373 DEGs in OSA samples, primarily associated with olfactory receptor activity and transduction.
• GDNF, SLC2A2, PRL, and SST were identified as key hub genes through PPI network analysis.
• Decreased expression of these hub genes correlated with OSA occurrence and showed diagnostic potential via ROC analysis.
• Strong correlations among hub genes suggest synergistic action in OSA pathogenesis.

Conclusions:

• Identified GDNF, SLC2A2, PRL, and SST as potential key genes in OSA.
• These four genes may serve as novel biomarkers for obstructive sleep apnea and associated conditions.
• Further research into these genes could elucidate OSA mechanisms and inform therapeutic strategies.