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Potential diagnostic markers and therapeutic targets for rheumatoid arthritis with comorbid depression based on bioinformatics analysis
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 t...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9995708/ https://www.ncbi.nlm.nih.gov/pubmed/36911710 http://dx.doi.org/10.3389/fimmu.2023.1007624 |
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author | Zhou, Tao-tao Sun, Ji-jia Tang, Li-dong Yuan, Ying Wang, Jian-ying Zhang, Lei |
author_facet | Zhou, Tao-tao Sun, Ji-jia Tang, Li-dong Yuan, Ying Wang, Jian-ying Zhang, Lei |
author_sort | Zhou, Tao-tao |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9995708 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99957082023-03-10 Potential diagnostic markers and therapeutic targets for rheumatoid arthritis with comorbid depression based on bioinformatics analysis Zhou, Tao-tao Sun, Ji-jia Tang, Li-dong Yuan, Ying Wang, Jian-ying Zhang, Lei Front Immunol Immunology 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. Frontiers Media S.A. 2023-02-23 /pmc/articles/PMC9995708/ /pubmed/36911710 http://dx.doi.org/10.3389/fimmu.2023.1007624 Text en Copyright © 2023 Zhou, Sun, Tang, Yuan, Wang and Zhang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Immunology Zhou, Tao-tao Sun, Ji-jia Tang, Li-dong Yuan, Ying Wang, Jian-ying Zhang, Lei Potential diagnostic markers and therapeutic targets for rheumatoid arthritis with comorbid depression based on bioinformatics analysis |
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 | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9995708/ https://www.ncbi.nlm.nih.gov/pubmed/36911710 http://dx.doi.org/10.3389/fimmu.2023.1007624 |
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