Integrative clinical and molecular insights into the comorbidity between depression and sleep apnea syndrome
Year: 2025
Type: article
Abstract: Objective To identify and characterize overlapping genes and pathways linking Depression and Sleep Apnea Syndrome (SAS). Methods A three-level analysis was conducted. Clinically, depression severity in 29 SAS patients was assessed using the Zung Self-Rating Depression Scale. Molecularly, an AI-driven literature mining approach was applied to extract gene–disease associations from PubMed and bioinformatics databases (19,924 genes), with prioritization using the Adjusted Binomial Method and validation via differential expression analysis. Functionally, shared genes were explored through protein–protein interaction (PPI) networks, enrichment analysis, and directional pathway modeling. Results Clinically, 62.07% of SAS patients exhibited depressive symptoms, with mild to moderate severity based on the Zung Self-Rating Depression Scale. Molecularly, 872 genes were found to be shared between 4,544 Depression-related and 1,197 SAS-related genes (OR = 11; p = 4.95 × 10-319). Further prioritization identified 24 overlapping genes with strong enrichment (OR = 10.9; p = 3.32 × 10-16), supported by validation in multiple gene expression datasets. These genes formed a densely connected protein–protein interaction network (238 edges; density = 0.43; clustering coefficient = 0.87), with core hubs including CASP3, TP53, SOD2, HMOX1, and MIR146A. Enrichment analysis highlighted involvement in oxidative stress, ferroptosis, and inflammatory pathways. Directional pathway modeling indicated that SAS may influence Depression via 18 genes and vice versa via 5 genes, with MIF and SOD2 acting as shared regulators. Conclusion This study reveals significant clinical and molecular links between Depression and SAS, identifying shared biological pathways and candidate targets for integrated therapeutic strategies.
Source: Frontiers in Psychiatry
Authors: HaiHua Chen, LanMei Zhuang, ZhiJuan Ji, Xing Sun, Ting Zhu, Bo Wang, Jin Wang
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W4414933581