Potential diagnostic markers and therapeutic targets for rheumatoid arthritis with comorbid depression based on bioinformatics analysis
BackgroundRheumatoid 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 thi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1007624/full |
| _version_ | 1797897126640877568 |
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| author | Tao-tao Zhou Ji-jia Sun Li-dong Tang Ying Yuan Jian-ying Wang Lei Zhang |
| author_facet | Tao-tao Zhou Ji-jia Sun Li-dong Tang Ying Yuan Jian-ying Wang Lei Zhang |
| author_sort | Tao-tao Zhou |
| collection | DOAJ |
| description | BackgroundRheumatoid 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.MethodsWe 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.ResultsIn 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.ConclusionOur 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. |
| first_indexed | 2024-04-10T07:53:46Z |
| format | Article |
| id | doaj.art-ffa2f9f407df4d26a7736fb091dc6540 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-10T07:53:46Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-ffa2f9f407df4d26a7736fb091dc65402023-02-23T07:29:51ZengFrontiers Media S.A.Frontiers in Immunology1664-32242023-02-011410.3389/fimmu.2023.10076241007624Potential diagnostic markers and therapeutic targets for rheumatoid arthritis with comorbid depression based on bioinformatics analysisTao-tao Zhou0Ji-jia Sun1Li-dong Tang2Ying Yuan3Jian-ying Wang4Lei Zhang5Department of Mathematics and Physics, School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaDepartment of Mathematics and Physics, School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaTeaching and Research Section of Chinese Materia Medica, School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaTeaching and Research Section of Chinese Materia Medica, School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaShanghai Innovation Center of Traditional Chinese Medicine Health Service, Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaShanghai Innovation Center of Traditional Chinese Medicine Health Service, Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaBackgroundRheumatoid 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.MethodsWe 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.ResultsIn 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.ConclusionOur 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.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1007624/fullrheumatoid arthritisdepressionbioinformaticsmachine learningmolecular docking |
| spellingShingle | Tao-tao Zhou Ji-jia Sun Li-dong Tang Ying Yuan Jian-ying Wang Lei Zhang Potential diagnostic markers and therapeutic targets for rheumatoid arthritis with comorbid depression based on bioinformatics analysis Frontiers in Immunology rheumatoid arthritis depression bioinformatics machine learning molecular docking |
| title | Potential diagnostic markers and therapeutic targets for rheumatoid arthritis with comorbid depression based on bioinformatics analysis |
| title_full | Potential diagnostic markers and therapeutic targets for rheumatoid arthritis with comorbid depression based on bioinformatics analysis |
| title_fullStr | Potential diagnostic markers and therapeutic targets for rheumatoid arthritis with comorbid depression based on bioinformatics analysis |
| title_full_unstemmed | Potential diagnostic markers and therapeutic targets for rheumatoid arthritis with comorbid depression based on bioinformatics analysis |
| title_short | Potential diagnostic markers and therapeutic targets for rheumatoid arthritis with comorbid depression based on bioinformatics analysis |
| title_sort | potential diagnostic markers and therapeutic targets for rheumatoid arthritis with comorbid depression based on bioinformatics analysis |
| topic | rheumatoid arthritis depression bioinformatics machine learning molecular docking |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1007624/full |
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