Data Sheet 2_Integrative clinical and molecular insights into the comorbidity between depression and sleep apnea syndrome.xlsx

ObjectiveTo identify and characterize overlapping genes and pathways linking Depression and Sleep Apnea Syndrome (SAS).
MethodsA 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.
ResultsClinically, 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.
ConclusionThis study reveals significant clinical and molecular links between Depression and SAS, identifying shared biological pathways and candidate targets for integrated therapeutic strategies.

研究目的：识别并表征关联抑郁障碍（Depression）与睡眠呼吸暂停综合征（Sleep Apnea Syndrome, SAS）的重叠基因及通路。
研究方法：采用三级分析策略。临床层面，采用宗氏自评抑郁量表（Zung Self-Rating Depression Scale）对29名SAS患者的抑郁严重程度进行评估；分子层面，应用AI驱动的文献挖掘方法，从PubMed及生物信息学数据库（共纳入19924个基因）中提取基因-疾病关联信息，采用校正二项式法（Adjusted Binomial Method）进行优先级排序，并通过差异表达分析完成验证；功能层面，通过蛋白质相互作用（Protein-Protein Interaction, PPI）网络、富集分析及定向通路建模对共享基因进行探究。
研究结果：临床层面，62.07%的SAS患者存在抑郁症状，依据宗氏自评抑郁量表评估，其抑郁严重程度多为轻度至中度。分子层面，在4544个抑郁相关基因与1197个SAS相关基因中，共筛选得到872个重叠基因（比值比OR=11；p=4.95×10⁻³¹⁹）。进一步优先级筛选得到24个具有显著富集效应的重叠基因（OR=10.9；p=3.32×10⁻¹⁶），该结果经多组基因表达数据集验证得以支持。上述基因构成了连接紧密的蛋白质相互作用网络（共238条边；网络密度=0.43；聚类系数=0.87），其核心枢纽基因包括CASP3、TP53、SOD2、HMOX1及MIR146A。富集分析结果显示，这些基因主要参与氧化应激、铁死亡（ferroptosis）及炎症通路。定向通路建模结果显示，SAS可通过18个基因影响抑郁障碍，反之抑郁障碍可通过5个基因影响SAS，其中MIF与SOD2为共同调控因子。
研究结论：本研究揭示了抑郁障碍与SAS之间存在显著的临床及分子关联，筛选得到了共享的生物学通路及可用于整合治疗策略的潜在靶点。