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Identification of common genetic characteristics of rheumatoid arthritis and major depressive disorder by bioinformatics analysis and machine learning
INTRODUCTION: Depression is the most common comorbidity of rheumatoid arthritis (RA). In particular, major depressive disorder (MDD) and rheumatoid arthritis share highly overlapping mental and physical manifestations, such as depressed mood, sleep disturbance, fatigue, pain, and worthlessness. This...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320004/ https://www.ncbi.nlm.nih.gov/pubmed/37415981 http://dx.doi.org/10.3389/fimmu.2023.1183115 |
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author | Jiang, Wen Wang, Xiaochuan Tao, Dongxia Zhao, Xin |
author_facet | Jiang, Wen Wang, Xiaochuan Tao, Dongxia Zhao, Xin |
author_sort | Jiang, Wen |
collection | PubMed |
description | INTRODUCTION: Depression is the most common comorbidity of rheumatoid arthritis (RA). In particular, major depressive disorder (MDD) and rheumatoid arthritis share highly overlapping mental and physical manifestations, such as depressed mood, sleep disturbance, fatigue, pain, and worthlessness. This overlap and indistinguishability often lead to the misattribution of physical and mental symptoms of RA patients to depression, and even, the depressive symptoms of MDD patients are ignored when receiving RA treatment. This has serious consequences, since the development of objective diagnostic tools to distinguish psychiatric symptoms from similar symptoms caused by physical diseases is urgent. METHODS: Bioinformatics analysis and machine learning. RESULTS: The common genetic characteristics of rheumatoid arthritis and major depressive disorder are EAF1, SDCBP and RNF19B. DISCUSSION: We discovered a connection between RA and MDD through immune infiltration studies: monocyte infiltration. Futhermore, we explored the correlation between the expression of the 3 marker genes and immune cell infiltration using the TIMER 2.0 database. This may help to explain the potential molecular mechanism by which RA and MDD increase the morbidity of each other. |
format | Online Article Text |
id | pubmed-10320004 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103200042023-07-06 Identification of common genetic characteristics of rheumatoid arthritis and major depressive disorder by bioinformatics analysis and machine learning Jiang, Wen Wang, Xiaochuan Tao, Dongxia Zhao, Xin Front Immunol Immunology INTRODUCTION: Depression is the most common comorbidity of rheumatoid arthritis (RA). In particular, major depressive disorder (MDD) and rheumatoid arthritis share highly overlapping mental and physical manifestations, such as depressed mood, sleep disturbance, fatigue, pain, and worthlessness. This overlap and indistinguishability often lead to the misattribution of physical and mental symptoms of RA patients to depression, and even, the depressive symptoms of MDD patients are ignored when receiving RA treatment. This has serious consequences, since the development of objective diagnostic tools to distinguish psychiatric symptoms from similar symptoms caused by physical diseases is urgent. METHODS: Bioinformatics analysis and machine learning. RESULTS: The common genetic characteristics of rheumatoid arthritis and major depressive disorder are EAF1, SDCBP and RNF19B. DISCUSSION: We discovered a connection between RA and MDD through immune infiltration studies: monocyte infiltration. Futhermore, we explored the correlation between the expression of the 3 marker genes and immune cell infiltration using the TIMER 2.0 database. This may help to explain the potential molecular mechanism by which RA and MDD increase the morbidity of each other. Frontiers Media S.A. 2023-06-21 /pmc/articles/PMC10320004/ /pubmed/37415981 http://dx.doi.org/10.3389/fimmu.2023.1183115 Text en Copyright © 2023 Jiang, Wang, Tao and Zhao 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 Jiang, Wen Wang, Xiaochuan Tao, Dongxia Zhao, Xin Identification of common genetic characteristics of rheumatoid arthritis and major depressive disorder by bioinformatics analysis and machine learning |
title | Identification of common genetic characteristics of rheumatoid arthritis and major depressive disorder by bioinformatics analysis and machine learning |
title_full | Identification of common genetic characteristics of rheumatoid arthritis and major depressive disorder by bioinformatics analysis and machine learning |
title_fullStr | Identification of common genetic characteristics of rheumatoid arthritis and major depressive disorder by bioinformatics analysis and machine learning |
title_full_unstemmed | Identification of common genetic characteristics of rheumatoid arthritis and major depressive disorder by bioinformatics analysis and machine learning |
title_short | Identification of common genetic characteristics of rheumatoid arthritis and major depressive disorder by bioinformatics analysis and machine learning |
title_sort | identification of common genetic characteristics of rheumatoid arthritis and major depressive disorder by bioinformatics analysis and machine learning |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320004/ https://www.ncbi.nlm.nih.gov/pubmed/37415981 http://dx.doi.org/10.3389/fimmu.2023.1183115 |
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