ABSTRACT
Background: 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. Methods: 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 in vitro experiments were used to validate these analytical results. 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: AURKA, BTN3A2, CXCL10, ERAP2, MARCO, and PLA2G7. CXCL10 and MARCO are closely associated with diverse immune cells in RA. However, apart from PLA2G7, the expression levels of the other five genes were associated with the composition of the majority of immune cells in MDD. Molecular docking and in vitro studies have revealed that Aucubin (AU) exerts the therapeutic effect through the downregulation of CXCL10 and BTN3A2 gene expression in PC12 cells. Conclusion: 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.
Subject(s)
Arthritis, Rheumatoid , Depressive Disorder, Major , Humans , Molecular Docking Simulation , Depression , Quality of Life , Arthritis, Rheumatoid/metabolism , Comorbidity , Computational Biology/methods , AminopeptidasesABSTRACT
Protein tyrosine kinases (PTKs) are attractive targets in searching for therapeutic agents against many diseases. In this study, a series of dehydroabietylamine derivatives were first determined to show PTK inhibitory activity using a high-throughput screening (HTS) method based on homogeneous time-resolved fluorescence (HTRF) technology. The structure-activity relationships of the dehydroabietylamine derivatives were established, and it was found that the compounds with a nitrogen-containing side chain had better inhibitory activity. Further studies showed that the compounds substituted with halogen in the phenyl ring resulted in higher inhibitory activity on the epidermal growth factor receptor (EGFR), and can be a guide to modify the structure of dehydroabietylamine derivatives. Dehydroabietylamine derivatives might be a new class of multi-targeted and effective PTK inhibitors with structure modifications.