ORIGINAL RESEARCH article
Front. Genet.
Sec. Computational Genomics
Potential diagnostic markers and therapeutic targets for obstructive sleep apnea with comorbid depression based on bioinformatics analysis
Provisionally accepted
- 1Huazhong University of Science and Technology, Wuhan, China
- 2Wuhan Red Cross Hospital, Wuhan, China
- 3Wuhan Fourth Hospital, Wuhan, China
- 4Hubei Minzu University, Enshi, China
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Background: Obstructive sleep apnea (OSA) and major depressive disorder (MDD) impose substantial quality-of-life burdens and socioeconomic costs. Growing evidence indicates bidirectional disease interactions that exacerbate clinical outcomes. This study identifies diagnostic biomarkers and explores therapeutic targets underlying OSA-MDD comorbidity. Methods: We analyzed OSA/MDD-specific differentially expressed genes (DEGs) from Gene Expression Omnibus (GEO) datasets. Weighted gene co-expression network analysis (WGCNA) identified co-expressed modules. Protein-protein interaction (PPI) networks derived key genes via STRING. Diagnostic markers were established through dual-algorithm screening, with immune associations and therapeutic potential assessed. Finally, in vitro validation confirmed key findings. Results: We identified 77 comorbid OSA-MDD DEGs. Integrated WGCNA-PPI analysis revealed eight key hub genes. LASSO regression nominated three diagnostic markers, including CD74 (CD74 molecule), RPL26L1 (ribosomal protein L26 like 1), and MRPL9 (mitochondrial ribosomal protein L9). MRPL9 was excluded for low diagnostic value for OSA and MDD. CD74 and RPL26L1 markers correlated with immune cell infiltration in OSA and MDD. In vitro, intermittent hypoxia significantly upregulated CD74 and RPL26L1 in microglia versus normoxia controls. Conclusion: CD74 and RPL26L1 represent mechanistically grounded diagnostic biomarkers and therapeutic targets for OSA-MDD comorbidity. Shared pathways offer novel intervention opportunities for both conditions.
Keywords: obstructive sleep apnea, Major Depressive Disorder, Hub gene, WGCNA, Bioinformatics analysis
Received: 01 Jul 2025; Accepted: 10 Nov 2025.
Copyright: © 2025 Guo, Lu, Tang, Zhou and Lin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xiao Guo, 13554016192@163.com
Wanting Lin, xhyklwt@163.com
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