Department of Neurosurgery, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Neurosurgery, Jinan 250014, China; Medical Science and Technology Innovation Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250117, China.
Department of Neurosurgery, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Neurosurgery, Jinan 250014, China.
J Affect Disord. 2024 Feb 1;346:273-284. doi: 10.1016/j.jad.2023.11.030. Epub 2023 Nov 11.
This study aims to investigate the molecular mechanisms underlying the interaction of major depressive disorder (MDD) and COVID-19, and on this basis, diagnostic biomarkers and potential therapeutic drugs are further explored.
Differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify common key genes involved in the pathogenesis of COVID-19 and MDD. Correlations with clinical features were explored. Detailed mechanisms were further investigated through protein interaction networks, GSEA, and immune cell infiltration analysis. Finally, Enrichr's Drug Signature Database and Coremine Medical were used to predict the potential drugs associated with key genes.
The study identified 18 genes involved in both COVID-19 and MDD. Four key genes (MBP, CYP4B1, ERMN, and SLC26A7) were selected based on clinical relevance. A multi-gene prediction model showed good diagnostic efficiency for the two diseases: AUC of 0.852 for COVID-19 and 0.915 for MDD. GO and GSEA analyses identified specific biological functions and pathways associated with key genes in COVID-19 (axon guidance, metabolism, stress response) and MDD (neuron ensheathment, biosynthesis, glutamatergic neuron differentiation). The key genes also affected immune infiltration. Potential therapeutic drugs, including small molecules and traditional Chinese medicines, targeting these genes were identified.
This study provides insights into the complex biological mechanisms underlying COVID-19 and MDD, develops an effective diagnostic model, and predicts potential therapeutic drugs, which may contribute to the prevention and treatment of these two prevalent diseases.
本研究旨在探讨重度抑郁症(MDD)和 COVID-19 相互作用的分子机制,并在此基础上进一步探索诊断生物标志物和潜在的治疗药物。
采用差异基因表达分析和加权基因共表达网络分析(WGCNA)鉴定与 COVID-19 和 MDD 发病机制相关的共同关键基因。探讨与临床特征的相关性。通过蛋白质相互作用网络、GSEA 和免疫细胞浸润分析进一步研究详细机制。最后,使用 Enrichr 的药物特征数据库和 Coremine Medical 预测与关键基因相关的潜在药物。
该研究确定了 18 个与 COVID-19 和 MDD 均相关的基因。基于临床相关性,选择了四个关键基因(MBP、CYP4B1、ERMN 和 SLC26A7)。多基因预测模型对两种疾病均具有良好的诊断效率:COVID-19 的 AUC 为 0.852,MDD 的 AUC 为 0.915。GO 和 GSEA 分析确定了与 COVID-19 (轴突导向、代谢、应激反应)和 MDD (神经元包绕、生物合成、谷氨酸能神经元分化)关键基因相关的特定生物学功能和途径。关键基因还影响免疫浸润。确定了针对这些基因的潜在治疗药物,包括小分子和中药。
本研究深入了解了 COVID-19 和 MDD 的复杂生物学机制,开发了有效的诊断模型,并预测了潜在的治疗药物,这可能有助于预防和治疗这两种常见疾病。