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The shared biomarkers and pathways of systemic lupus erythematosus and metabolic syndrome analyzed by bioinformatics combining machine learning algorithm and single-cell sequencing analysis
BACKGROUND: Systemic lupus erythematosus (SLE) is one of the most prevalent systemic autoimmune diseases, and metabolic syndrome (MetS) is the most common metabolic disorder that contains hypertension, dyslipidemia, and obesity. Despite clinical evidence suggested potential associations between SLE...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9627509/ https://www.ncbi.nlm.nih.gov/pubmed/36341378 http://dx.doi.org/10.3389/fimmu.2022.1015882 |
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author | Wang, Yingyu Huang, Zhongzhou Xiao, Yu Wan, Weiguo Yang, Xue |
author_facet | Wang, Yingyu Huang, Zhongzhou Xiao, Yu Wan, Weiguo Yang, Xue |
author_sort | Wang, Yingyu |
collection | PubMed |
description | BACKGROUND: Systemic lupus erythematosus (SLE) is one of the most prevalent systemic autoimmune diseases, and metabolic syndrome (MetS) is the most common metabolic disorder that contains hypertension, dyslipidemia, and obesity. Despite clinical evidence suggested potential associations between SLE and MetS, the underlying pathogenesis is yet unclear. METHODS: The microarray data sets of SLE and MetS were obtained from the Gene Expression Omnibus (GEO) database. To identify the shared genes between SLE and MetS, the Differentially Expressed Genes (DEGs) analysis and the weighted gene co-expression network analysis (WGCNA) were conducted. Then, the GO and KEGG analyses were performed, and the protein-protein interaction (PPI) network was constructed. Next, Random Forest and LASSO algorithms were used to screen shared hub genes, and a diagnostic model was built using the machine learning technique XG-Boost. Subsequently, CIBERSORT and GSVA were used to estimate the correlation between shared hub genes and immune infiltration as well as metabolic pathways. Finally, the significant hub genes were verified using single-cell RNA sequencing (scRNA-seq) data. RESULTS: Using limma and WGCNA, we identified 153 shared feature genes, which were enriched in immune- and metabolic-related pathways. Further, 20 shared hub genes were screened and successfully used to build a prognostic model. Those shared hub genes were associated with immunological and metabolic processes in peripheral blood. The scRNA-seq results verified that TNFSF13B and OAS1, possessing the highest diagnostic efficacy, were mainly expressed by monocytes. Additionally, they showed positive correlations with the pathways for the metabolism of xenobiotics and cholesterol, both of which were proven to be active in this comorbidity, and shown to be concentrated in monocytes. CONCLUSION: This study identified shared hub genes and constructed an effective diagnostic model in SLE and MetS. TNFSF13B and OAS1 had a positive correlation with cholesterol and xenobiotic metabolism. Both of these two biomarkers and metabolic pathways were potentially linked to monocytes, which provides novel insights into the pathogenesis and combined therapy of SLE comorbidity with MetS. |
format | Online Article Text |
id | pubmed-9627509 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96275092022-11-03 The shared biomarkers and pathways of systemic lupus erythematosus and metabolic syndrome analyzed by bioinformatics combining machine learning algorithm and single-cell sequencing analysis Wang, Yingyu Huang, Zhongzhou Xiao, Yu Wan, Weiguo Yang, Xue Front Immunol Immunology BACKGROUND: Systemic lupus erythematosus (SLE) is one of the most prevalent systemic autoimmune diseases, and metabolic syndrome (MetS) is the most common metabolic disorder that contains hypertension, dyslipidemia, and obesity. Despite clinical evidence suggested potential associations between SLE and MetS, the underlying pathogenesis is yet unclear. METHODS: The microarray data sets of SLE and MetS were obtained from the Gene Expression Omnibus (GEO) database. To identify the shared genes between SLE and MetS, the Differentially Expressed Genes (DEGs) analysis and the weighted gene co-expression network analysis (WGCNA) were conducted. Then, the GO and KEGG analyses were performed, and the protein-protein interaction (PPI) network was constructed. Next, Random Forest and LASSO algorithms were used to screen shared hub genes, and a diagnostic model was built using the machine learning technique XG-Boost. Subsequently, CIBERSORT and GSVA were used to estimate the correlation between shared hub genes and immune infiltration as well as metabolic pathways. Finally, the significant hub genes were verified using single-cell RNA sequencing (scRNA-seq) data. RESULTS: Using limma and WGCNA, we identified 153 shared feature genes, which were enriched in immune- and metabolic-related pathways. Further, 20 shared hub genes were screened and successfully used to build a prognostic model. Those shared hub genes were associated with immunological and metabolic processes in peripheral blood. The scRNA-seq results verified that TNFSF13B and OAS1, possessing the highest diagnostic efficacy, were mainly expressed by monocytes. Additionally, they showed positive correlations with the pathways for the metabolism of xenobiotics and cholesterol, both of which were proven to be active in this comorbidity, and shown to be concentrated in monocytes. CONCLUSION: This study identified shared hub genes and constructed an effective diagnostic model in SLE and MetS. TNFSF13B and OAS1 had a positive correlation with cholesterol and xenobiotic metabolism. Both of these two biomarkers and metabolic pathways were potentially linked to monocytes, which provides novel insights into the pathogenesis and combined therapy of SLE comorbidity with MetS. Frontiers Media S.A. 2022-10-19 /pmc/articles/PMC9627509/ /pubmed/36341378 http://dx.doi.org/10.3389/fimmu.2022.1015882 Text en Copyright © 2022 Wang, Huang, Xiao, Wan and Yang 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 Wang, Yingyu Huang, Zhongzhou Xiao, Yu Wan, Weiguo Yang, Xue The shared biomarkers and pathways of systemic lupus erythematosus and metabolic syndrome analyzed by bioinformatics combining machine learning algorithm and single-cell sequencing analysis |
title | The shared biomarkers and pathways of systemic lupus erythematosus and metabolic syndrome analyzed by bioinformatics combining machine learning algorithm and single-cell sequencing analysis |
title_full | The shared biomarkers and pathways of systemic lupus erythematosus and metabolic syndrome analyzed by bioinformatics combining machine learning algorithm and single-cell sequencing analysis |
title_fullStr | The shared biomarkers and pathways of systemic lupus erythematosus and metabolic syndrome analyzed by bioinformatics combining machine learning algorithm and single-cell sequencing analysis |
title_full_unstemmed | The shared biomarkers and pathways of systemic lupus erythematosus and metabolic syndrome analyzed by bioinformatics combining machine learning algorithm and single-cell sequencing analysis |
title_short | The shared biomarkers and pathways of systemic lupus erythematosus and metabolic syndrome analyzed by bioinformatics combining machine learning algorithm and single-cell sequencing analysis |
title_sort | shared biomarkers and pathways of systemic lupus erythematosus and metabolic syndrome analyzed by bioinformatics combining machine learning algorithm and single-cell sequencing analysis |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9627509/ https://www.ncbi.nlm.nih.gov/pubmed/36341378 http://dx.doi.org/10.3389/fimmu.2022.1015882 |
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