Department of Mathematics and Physics, School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Teaching and Research Section of Chinese Materia Medica, School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Front Immunol. 2023 Feb 23;14:1007624. doi: 10.3389/fimmu.2023.1007624. eCollection 2023.
Rheumatoid arthritis (RA) and depression are prevalent diseases that have a negative impact on the quality of life and place a significant economic burden on society. There is increasing evidence that the two diseases are closely related, which could make the disease outcomes worse. In this study, we aimed to identify diagnostic markers and analyzed the therapeutic potential of key genes.
We assessed the differentially expressed genes (DEGs) specific for RA and Major depressive disorder (MDD) and used weighted gene co-expression network analysis (WGCNA) to identify co-expressed gene modules by obtaining the Gene expression profile data from Gene Expression Omnibus (GEO) database. By using the STRING database, a protein-protein interaction (PPI) network constructed and identified key genes. We also employed two types of machine learning techniques to derive diagnostic markers, which were assessed for their association with immune cells and potential therapeutic effects. Molecular docking and experiments were used to validate these analytical results.
In total, 48 DEGs were identified in RA with comorbid MDD. The PPI network was combined with WGCNA to identify 26 key genes of RA with comorbid MDD. Machine learning-based methods indicated that RA combined with MDD is likely related to six diagnostic markers: , , , , , and . and are closely associated with diverse immune cells in RA. However, apart from , the expression levels of the other five genes were associated with the composition of the majority of immune cells in MDD. Molecular docking and studies have revealed that Aucubin (AU) exerts the therapeutic effect through the downregulation of and gene expression in PC12 cells.
Our study indicates that six diagnostic markers were the basis of the comorbidity mechanism of RA and MDD and may also be potential therapeutic targets. Further mechanistic studies of the pathogenesis and treatment of RA and MDD may be able to identify new targets using these shared pathways.
类风湿关节炎(RA)和抑郁症是常见疾病,它们对生活质量有负面影响,并给社会带来巨大的经济负担。越来越多的证据表明,这两种疾病密切相关,这可能使疾病结果恶化。在这项研究中,我们旨在确定诊断标志物,并分析关键基因的治疗潜力。
我们评估了 RA 和重度抑郁症(MDD)特有的差异表达基因(DEGs),并通过从基因表达综合数据库(GEO)数据库中获得基因表达谱数据,使用加权基因共表达网络分析(WGCNA)来识别共表达基因模块。通过使用 STRING 数据库构建和识别关键基因的蛋白质-蛋白质相互作用(PPI)网络。我们还采用了两种类型的机器学习技术来推导诊断标志物,并评估其与免疫细胞的关联和潜在的治疗效果。使用分子对接和实验验证这些分析结果。
共鉴定出 48 个 RA 合并 MDD 的 DEGs。PPI 网络与 WGCNA 相结合,确定了 26 个 RA 合并 MDD 的关键基因。基于机器学习的方法表明,RA 合并 MDD 可能与六个诊断标志物有关:、、、、和。和与 RA 中的多种免疫细胞密切相关。然而,除了外,其他五个基因的表达水平与 MDD 中大多数免疫细胞的组成有关。分子对接和实验研究表明,澳贝丁(AU)通过下调 PC12 细胞中基因的表达发挥治疗作用。
我们的研究表明,六个诊断标志物是 RA 和 MDD 共病机制的基础,也可能是潜在的治疗靶点。对 RA 和 MDD 发病机制和治疗的进一步机制研究可能能够利用这些共享途径确定新的靶点。