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Identification of diagnostic mRNA biomarkers in whole blood for ankylosing spondylitis using WGCNA and machine learning feature selection
Ankylosing spondylitis (AS) is a common inflammatory spondyloarthritis affecting the spine and sacroiliac joint that finally results in sclerosis of the axial skeleton. Aside from human leukocyte antigen B27, transcriptomic biomarkers in blood for AS diagnosis still remain unknown. Hence, this study...
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/PMC9510835/ https://www.ncbi.nlm.nih.gov/pubmed/36172367 http://dx.doi.org/10.3389/fimmu.2022.956027 |
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author | Han, Yaguang Zhou, Yiqin Li, Haobo Gong, Zhenyu Liu, Ziye Wang, Huan Wang, Bo Ye, Xiaojian Liu, Yi |
author_facet | Han, Yaguang Zhou, Yiqin Li, Haobo Gong, Zhenyu Liu, Ziye Wang, Huan Wang, Bo Ye, Xiaojian Liu, Yi |
author_sort | Han, Yaguang |
collection | PubMed |
description | Ankylosing spondylitis (AS) is a common inflammatory spondyloarthritis affecting the spine and sacroiliac joint that finally results in sclerosis of the axial skeleton. Aside from human leukocyte antigen B27, transcriptomic biomarkers in blood for AS diagnosis still remain unknown. Hence, this study aimed to investigate credible AS-specific mRNA biomarkers from the whole blood of AS patients by analyzing an mRNA expression profile (GSE73754) downloaded Gene Expression Omnibus, which includes AS and healthy control blood samples. Weighted gene co-expression network analysis was performed and revealed three mRNA modules associated with AS. By performing gene set enrichment analysis, the functional annotations of these modules revealed immune biological processes that occur in AS. Several feature mRNAs were identified by analyzing the hubs of the protein-protein interaction network, which was based on the intersection between differentially expressed mRNAs and mRNA modules. A machine learning-based feature selection method, SVM-RFE, was used to further screen out 13 key feature mRNAs. After verifying by qPCR, IL17RA, Sqstm1, Picalm, Eif4e, Srrt, Lrrfip1, Synj1 and Cxcr6 were found to be significant for AS diagnosis. Among them, Cxcr6, IL17RA and Lrrfip1 were correlated with severity of AS symptoms. In conclusion, our findings provide a framework for identifying the key mRNAs in whole blood of AS that is conducive for the development of novel diagnostic markers for AS. |
format | Online Article Text |
id | pubmed-9510835 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95108352022-09-27 Identification of diagnostic mRNA biomarkers in whole blood for ankylosing spondylitis using WGCNA and machine learning feature selection Han, Yaguang Zhou, Yiqin Li, Haobo Gong, Zhenyu Liu, Ziye Wang, Huan Wang, Bo Ye, Xiaojian Liu, Yi Front Immunol Immunology Ankylosing spondylitis (AS) is a common inflammatory spondyloarthritis affecting the spine and sacroiliac joint that finally results in sclerosis of the axial skeleton. Aside from human leukocyte antigen B27, transcriptomic biomarkers in blood for AS diagnosis still remain unknown. Hence, this study aimed to investigate credible AS-specific mRNA biomarkers from the whole blood of AS patients by analyzing an mRNA expression profile (GSE73754) downloaded Gene Expression Omnibus, which includes AS and healthy control blood samples. Weighted gene co-expression network analysis was performed and revealed three mRNA modules associated with AS. By performing gene set enrichment analysis, the functional annotations of these modules revealed immune biological processes that occur in AS. Several feature mRNAs were identified by analyzing the hubs of the protein-protein interaction network, which was based on the intersection between differentially expressed mRNAs and mRNA modules. A machine learning-based feature selection method, SVM-RFE, was used to further screen out 13 key feature mRNAs. After verifying by qPCR, IL17RA, Sqstm1, Picalm, Eif4e, Srrt, Lrrfip1, Synj1 and Cxcr6 were found to be significant for AS diagnosis. Among them, Cxcr6, IL17RA and Lrrfip1 were correlated with severity of AS symptoms. In conclusion, our findings provide a framework for identifying the key mRNAs in whole blood of AS that is conducive for the development of novel diagnostic markers for AS. Frontiers Media S.A. 2022-09-12 /pmc/articles/PMC9510835/ /pubmed/36172367 http://dx.doi.org/10.3389/fimmu.2022.956027 Text en Copyright © 2022 Han, Zhou, Li, Gong, Liu, Wang, Wang, Ye and Liu 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 Han, Yaguang Zhou, Yiqin Li, Haobo Gong, Zhenyu Liu, Ziye Wang, Huan Wang, Bo Ye, Xiaojian Liu, Yi Identification of diagnostic mRNA biomarkers in whole blood for ankylosing spondylitis using WGCNA and machine learning feature selection |
title | Identification of diagnostic mRNA biomarkers in whole blood for ankylosing spondylitis using WGCNA and machine learning feature selection |
title_full | Identification of diagnostic mRNA biomarkers in whole blood for ankylosing spondylitis using WGCNA and machine learning feature selection |
title_fullStr | Identification of diagnostic mRNA biomarkers in whole blood for ankylosing spondylitis using WGCNA and machine learning feature selection |
title_full_unstemmed | Identification of diagnostic mRNA biomarkers in whole blood for ankylosing spondylitis using WGCNA and machine learning feature selection |
title_short | Identification of diagnostic mRNA biomarkers in whole blood for ankylosing spondylitis using WGCNA and machine learning feature selection |
title_sort | identification of diagnostic mrna biomarkers in whole blood for ankylosing spondylitis using wgcna and machine learning feature selection |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9510835/ https://www.ncbi.nlm.nih.gov/pubmed/36172367 http://dx.doi.org/10.3389/fimmu.2022.956027 |
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