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Identification of copper death-associated molecular clusters and immunological profiles in rheumatoid arthritis

Zhou, Yu; Li, Xin; Ng, Liqi; Zhao, Qing; Guo, Wentao; Hu, Jinhua; Zhong, Jinghong; ... Su, Songchuan; + view all (2023) Identification of copper death-associated molecular clusters and immunological profiles in rheumatoid arthritis. Frontiers in Immunology , 14 , Article 1103509. 10.3389/fimmu.2023.1103509. Green open access

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Abstract

Objective: An analysis of the relationship between rheumatoid arthritis (RA) and copper death-related genes (CRG) was explored based on the GEO dataset. / Methods: Based on the differential gene expression profiles in the GSE93272 dataset, their relationship to CRG and immune signature were analysed. Using 232 RA samples, molecular clusters with CRG were delineated and analysed for expression and immune infiltration. Genes specific to the CRGcluster were identified by the WGCNA algorithm. Four machine learning models were then built and validated after selecting the optimal model to obtain the significant predicted genes, and validated by constructing RA rat models. / Results: The location of the 13 CRGs on the chromosome was determined and, except for GCSH. LIPT1, FDX1, DLD, DBT, LIAS and ATP7A were expressed at significantly higher levels in RA samples than in non-RA, and DLST was significantly lower. RA samples were significantly expressed in immune cells such as B cells memory and differentially expressed genes such as LIPT1 were also strongly associated with the presence of immune infiltration. Two copper death-related molecular clusters were identified in RA samples. A higher level of immune infiltration and expression of CRGcluster C2 was found in the RA population. There were 314 crossover genes between the 2 molecular clusters, which were further divided into two molecular clusters. A significant difference in immune infiltration and expression levels was found between the two. Based on the five genes obtained from the RF model (AUC = 0.843), the Nomogram model, calibration curve and DCA also demonstrated their accuracy in predicting RA subtypes. The expression levels of the five genes were significantly higher in RA samples than in non-RA, and the ROC curves demonstrated their better predictive effect. Identification of predictive genes by RA animal model experiments was also confirmed. / Conclusion: This study provides some insight into the correlation between rheumatoid arthritis and copper mortality, as well as a predictive model that is expected to support the development of targeted treatment options in the future.

Type: Article
Title: Identification of copper death-associated molecular clusters and immunological profiles in rheumatoid arthritis
Location: Switzerland
Open access status: An open access version is available from UCL Discovery
DOI: 10.3389/fimmu.2023.1103509
Publisher version: https://doi.org/10.3389/fimmu.2023.1103509
Language: English
Additional information: © 2023 Zhou, Li, Ng, Zhao, Guo, Hu, Zhong, Su, Liu and Su. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (http://creativecommons.org/licenses/by/4.0/). 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.
Keywords: rheumatoid arthritis, copper death, machine learning, immune infiltration, predictive models
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Ortho and MSK Science
URI: https://discovery.ucl.ac.uk/id/eprint/10167050
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