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Combining bioinformatics and machine learning to identify common mechanisms and biomarkers of chronic obstructive pulmonary disease and atrial fibrillation

BACKGROUND: Patients with chronic obstructive pulmonary disease (COPD) often present with atrial fibrillation (AF), but the common pathophysiological mechanisms between the two are unclear. This study aimed to investigate the common biological mechanisms of COPD and AF and to search for important bi...

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Autores principales: Sun, Ziyi, Lin, Jianguo, Zhang, Tianya, Sun, Xiaoning, Wang, Tianlin, Duan, Jinlong, Yao, Kuiwu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10086368/
https://www.ncbi.nlm.nih.gov/pubmed/37057099
http://dx.doi.org/10.3389/fcvm.2023.1121102
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author Sun, Ziyi
Lin, Jianguo
Zhang, Tianya
Sun, Xiaoning
Wang, Tianlin
Duan, Jinlong
Yao, Kuiwu
author_facet Sun, Ziyi
Lin, Jianguo
Zhang, Tianya
Sun, Xiaoning
Wang, Tianlin
Duan, Jinlong
Yao, Kuiwu
author_sort Sun, Ziyi
collection PubMed
description BACKGROUND: Patients with chronic obstructive pulmonary disease (COPD) often present with atrial fibrillation (AF), but the common pathophysiological mechanisms between the two are unclear. This study aimed to investigate the common biological mechanisms of COPD and AF and to search for important biomarkers through bioinformatic analysis of public RNA sequencing databases. METHODS: Four datasets of COPD and AF were downloaded from the Gene Expression Omnibus (GEO) database. The overlapping genes common to both diseases were screened by WGCNA analysis, followed by protein-protein interaction network construction and functional enrichment analysis to elucidate the common mechanisms of COPD and AF. Machine learning algorithms were also used to identify key biomarkers. Co-expression analysis, “transcription factor (TF)-mRNA-microRNA (miRNA)” regulatory networks and drug prediction were performed for key biomarkers. Finally, immune cell infiltration analysis was performed to evaluate further the immune cell changes in the COPD dataset and the correlation between key biomarkers and immune cells. RESULTS: A total of 133 overlapping genes for COPD and AF were obtained, and the enrichment was mainly focused on pathways associated with the inflammatory immune response. A key biomarker, cyclin dependent kinase 8 (CDK8), was identified through screening by machine learning algorithms and validated in the validation dataset. Twenty potential drugs capable of targeting CDK8 were obtained. Immune cell infiltration analysis revealed the presence of multiple immune cell dysregulation in COPD. Correlation analysis showed that CDK8 expression was significantly associated with CD8+ T cells, resting dendritic cell, macrophage M2, and monocytes. CONCLUSIONS: This study highlights the role of the inflammatory immune response in COPD combined with AF. The prominent link between CDK8 and the inflammatory immune response and its characteristic of not affecting the basal expression level of nuclear factor kappa B (NF-kB) make it a possible promising therapeutic target for COPD combined with AF.
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spelling pubmed-100863682023-04-12 Combining bioinformatics and machine learning to identify common mechanisms and biomarkers of chronic obstructive pulmonary disease and atrial fibrillation Sun, Ziyi Lin, Jianguo Zhang, Tianya Sun, Xiaoning Wang, Tianlin Duan, Jinlong Yao, Kuiwu Front Cardiovasc Med Cardiovascular Medicine BACKGROUND: Patients with chronic obstructive pulmonary disease (COPD) often present with atrial fibrillation (AF), but the common pathophysiological mechanisms between the two are unclear. This study aimed to investigate the common biological mechanisms of COPD and AF and to search for important biomarkers through bioinformatic analysis of public RNA sequencing databases. METHODS: Four datasets of COPD and AF were downloaded from the Gene Expression Omnibus (GEO) database. The overlapping genes common to both diseases were screened by WGCNA analysis, followed by protein-protein interaction network construction and functional enrichment analysis to elucidate the common mechanisms of COPD and AF. Machine learning algorithms were also used to identify key biomarkers. Co-expression analysis, “transcription factor (TF)-mRNA-microRNA (miRNA)” regulatory networks and drug prediction were performed for key biomarkers. Finally, immune cell infiltration analysis was performed to evaluate further the immune cell changes in the COPD dataset and the correlation between key biomarkers and immune cells. RESULTS: A total of 133 overlapping genes for COPD and AF were obtained, and the enrichment was mainly focused on pathways associated with the inflammatory immune response. A key biomarker, cyclin dependent kinase 8 (CDK8), was identified through screening by machine learning algorithms and validated in the validation dataset. Twenty potential drugs capable of targeting CDK8 were obtained. Immune cell infiltration analysis revealed the presence of multiple immune cell dysregulation in COPD. Correlation analysis showed that CDK8 expression was significantly associated with CD8+ T cells, resting dendritic cell, macrophage M2, and monocytes. CONCLUSIONS: This study highlights the role of the inflammatory immune response in COPD combined with AF. The prominent link between CDK8 and the inflammatory immune response and its characteristic of not affecting the basal expression level of nuclear factor kappa B (NF-kB) make it a possible promising therapeutic target for COPD combined with AF. Frontiers Media S.A. 2023-03-28 /pmc/articles/PMC10086368/ /pubmed/37057099 http://dx.doi.org/10.3389/fcvm.2023.1121102 Text en © 2023 Sun, Lin, Zhang, Sun, Wang, Duan and Yao. 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) (https://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.
spellingShingle Cardiovascular Medicine
Sun, Ziyi
Lin, Jianguo
Zhang, Tianya
Sun, Xiaoning
Wang, Tianlin
Duan, Jinlong
Yao, Kuiwu
Combining bioinformatics and machine learning to identify common mechanisms and biomarkers of chronic obstructive pulmonary disease and atrial fibrillation
title Combining bioinformatics and machine learning to identify common mechanisms and biomarkers of chronic obstructive pulmonary disease and atrial fibrillation
title_full Combining bioinformatics and machine learning to identify common mechanisms and biomarkers of chronic obstructive pulmonary disease and atrial fibrillation
title_fullStr Combining bioinformatics and machine learning to identify common mechanisms and biomarkers of chronic obstructive pulmonary disease and atrial fibrillation
title_full_unstemmed Combining bioinformatics and machine learning to identify common mechanisms and biomarkers of chronic obstructive pulmonary disease and atrial fibrillation
title_short Combining bioinformatics and machine learning to identify common mechanisms and biomarkers of chronic obstructive pulmonary disease and atrial fibrillation
title_sort combining bioinformatics and machine learning to identify common mechanisms and biomarkers of chronic obstructive pulmonary disease and atrial fibrillation
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10086368/
https://www.ncbi.nlm.nih.gov/pubmed/37057099
http://dx.doi.org/10.3389/fcvm.2023.1121102
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