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Bioinformatics and Machine Learning Methods to Identify FN1 as a Novel Biomarker of Aortic Valve Calcification

AIM: The purpose of this study was to identify potential diagnostic markers for aortic valve calcification (AVC) and to investigate the function of immune cell infiltration in this disease. METHODS: The AVC data sets were obtained from the Gene Expression Omnibus. The identification of differentiall...

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Autores principales: Xiong, Tao, Han, Shen, Pu, Lei, Zhang, Tian-Chen, Zhan, Xu, Fu, Tao, Dai, Ying-Hai, Li, Ya-Xiong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8918776/
https://www.ncbi.nlm.nih.gov/pubmed/35295271
http://dx.doi.org/10.3389/fcvm.2022.832591
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author Xiong, Tao
Han, Shen
Pu, Lei
Zhang, Tian-Chen
Zhan, Xu
Fu, Tao
Dai, Ying-Hai
Li, Ya-Xiong
author_facet Xiong, Tao
Han, Shen
Pu, Lei
Zhang, Tian-Chen
Zhan, Xu
Fu, Tao
Dai, Ying-Hai
Li, Ya-Xiong
author_sort Xiong, Tao
collection PubMed
description AIM: The purpose of this study was to identify potential diagnostic markers for aortic valve calcification (AVC) and to investigate the function of immune cell infiltration in this disease. METHODS: The AVC data sets were obtained from the Gene Expression Omnibus. The identification of differentially expressed genes (DEGs) and the performance of functional correlation analysis were carried out using the R software. To explore hub genes related to AVC, a protein–protein interaction network was created. Diagnostic markers for AVC were then screened and verified using the least absolute shrinkage and selection operator, logistic regression, support vector machine-recursive feature elimination algorithms, and hub genes. The infiltration of immune cells into AVC tissues was evaluated using CIBERSORT, and the correlation between diagnostic markers and infiltrating immune cells was analyzed. Finally, the Connectivity Map database was used to forecast the candidate small molecule drugs that might be used as prospective medications to treat AVC. RESULTS: A total of 337 DEGs were screened. The DEGs that were discovered were mostly related with atherosclerosis and arteriosclerotic cardiovascular disease, according to the analyses. Gene sets involved in the chemokine signaling pathway and cytokine–cytokine receptor interaction were differently active in AVC compared with control. As the diagnostic marker for AVC, fibronectin 1 (FN1) (area the curve = 0.958) was discovered. Immune cell infiltration analysis revealed that the AVC process may be mediated by naïve B cells, memory B cells, plasma cells, activated natural killer cells, monocytes, and macrophages M0. Additionally, FN1 expression was associated with memory B cells, M0 macrophages, activated mast cells, resting mast cells, monocytes, and activated natural killer cells. AVC may be reversed with the use of yohimbic acid, the most promising small molecule discovered so far. CONCLUSION: FN1 can be used as a diagnostic marker for AVC. It has been shown that immune cell infiltration is important in the onset and progression of AVC, which may benefit in the improvement of AVC diagnosis and treatment.
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spelling pubmed-89187762022-03-15 Bioinformatics and Machine Learning Methods to Identify FN1 as a Novel Biomarker of Aortic Valve Calcification Xiong, Tao Han, Shen Pu, Lei Zhang, Tian-Chen Zhan, Xu Fu, Tao Dai, Ying-Hai Li, Ya-Xiong Front Cardiovasc Med Cardiovascular Medicine AIM: The purpose of this study was to identify potential diagnostic markers for aortic valve calcification (AVC) and to investigate the function of immune cell infiltration in this disease. METHODS: The AVC data sets were obtained from the Gene Expression Omnibus. The identification of differentially expressed genes (DEGs) and the performance of functional correlation analysis were carried out using the R software. To explore hub genes related to AVC, a protein–protein interaction network was created. Diagnostic markers for AVC were then screened and verified using the least absolute shrinkage and selection operator, logistic regression, support vector machine-recursive feature elimination algorithms, and hub genes. The infiltration of immune cells into AVC tissues was evaluated using CIBERSORT, and the correlation between diagnostic markers and infiltrating immune cells was analyzed. Finally, the Connectivity Map database was used to forecast the candidate small molecule drugs that might be used as prospective medications to treat AVC. RESULTS: A total of 337 DEGs were screened. The DEGs that were discovered were mostly related with atherosclerosis and arteriosclerotic cardiovascular disease, according to the analyses. Gene sets involved in the chemokine signaling pathway and cytokine–cytokine receptor interaction were differently active in AVC compared with control. As the diagnostic marker for AVC, fibronectin 1 (FN1) (area the curve = 0.958) was discovered. Immune cell infiltration analysis revealed that the AVC process may be mediated by naïve B cells, memory B cells, plasma cells, activated natural killer cells, monocytes, and macrophages M0. Additionally, FN1 expression was associated with memory B cells, M0 macrophages, activated mast cells, resting mast cells, monocytes, and activated natural killer cells. AVC may be reversed with the use of yohimbic acid, the most promising small molecule discovered so far. CONCLUSION: FN1 can be used as a diagnostic marker for AVC. It has been shown that immune cell infiltration is important in the onset and progression of AVC, which may benefit in the improvement of AVC diagnosis and treatment. Frontiers Media S.A. 2022-02-28 /pmc/articles/PMC8918776/ /pubmed/35295271 http://dx.doi.org/10.3389/fcvm.2022.832591 Text en Copyright © 2022 Xiong, Han, Pu, Zhang, Zhan, Fu, Dai and Li. 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 Cardiovascular Medicine
Xiong, Tao
Han, Shen
Pu, Lei
Zhang, Tian-Chen
Zhan, Xu
Fu, Tao
Dai, Ying-Hai
Li, Ya-Xiong
Bioinformatics and Machine Learning Methods to Identify FN1 as a Novel Biomarker of Aortic Valve Calcification
title Bioinformatics and Machine Learning Methods to Identify FN1 as a Novel Biomarker of Aortic Valve Calcification
title_full Bioinformatics and Machine Learning Methods to Identify FN1 as a Novel Biomarker of Aortic Valve Calcification
title_fullStr Bioinformatics and Machine Learning Methods to Identify FN1 as a Novel Biomarker of Aortic Valve Calcification
title_full_unstemmed Bioinformatics and Machine Learning Methods to Identify FN1 as a Novel Biomarker of Aortic Valve Calcification
title_short Bioinformatics and Machine Learning Methods to Identify FN1 as a Novel Biomarker of Aortic Valve Calcification
title_sort bioinformatics and machine learning methods to identify fn1 as a novel biomarker of aortic valve calcification
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8918776/
https://www.ncbi.nlm.nih.gov/pubmed/35295271
http://dx.doi.org/10.3389/fcvm.2022.832591
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