Potential diagnostic markers and therapeutic targets for rheumatoid arthritis with comorbid depression based on bioinformatics analysis

Tao-Tao Zhou; Ji-Jia Sun; Li-Dong Tang; Ying Yuan; Jian-Ying Wang; Lei Zhang

doi:10.3389/fimmu.2023.1007624

Potential diagnostic markers and therapeutic targets for rheumatoid arthritis with comorbid depression based on bioinformatics analysis

Front Immunol. 2023 Feb 23:14:1007624. doi: 10.3389/fimmu.2023.1007624. eCollection 2023.

Authors

Tao-Tao Zhou¹, Ji-Jia Sun¹, Li-Dong Tang², Ying Yuan², Jian-Ying Wang³, Lei Zhang³

Affiliations

¹ Department of Mathematics and Physics, School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
² Teaching and Research Section of Chinese Materia Medica, School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
³ Shanghai Innovation Center of Traditional Chinese Medicine Health Service, Shanghai University of Traditional Chinese Medicine, Shanghai, China.

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.

Keywords: bioinformatics; depression; machine learning; molecular docking; rheumatoid arthritis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Aminopeptidases
Arthritis, Rheumatoid* / metabolism
Comorbidity
Computational Biology / methods
Depression
Depressive Disorder, Major*
Humans
Molecular Docking Simulation
Quality of Life

Substances

ERAP2 protein, human
Aminopeptidases

Grants and funding

This study was financially supported by the Startup Fund from the Three-Year Action Plan for Shanghai (project number: ZY (2021-2023)-0211), Shanghai Collaborative Innovation Center for Chronic Disease Prevention and Health Services (2021 Science and Technology 02-37) Chinese Medicine Research Project Plan of Shanghai Municipal Health Commission (grant no. 2020JP002), and Shanghai Natural Science Fund (grant no. 19ZR1452000).