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Identification of potential biomarkers of inflammation-related genes for ischemic cardiomyopathy
OBJECTIVE: Inflammation plays an important role in the pathophysiology of ischemic cardiomyopathy (ICM). We aimed to identify potential biomarkers of inflammation-related genes for ICM and build a model based on the potential biomarkers for the diagnosis of ICM. MATERIALS AND METHODS: The microarray...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9445158/ https://www.ncbi.nlm.nih.gov/pubmed/36082132 http://dx.doi.org/10.3389/fcvm.2022.972274 |
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author | Wang, Jianru Xie, Shiyang Cheng, Yanling Li, Xiaohui Chen, Jian Zhu, Mingjun |
author_facet | Wang, Jianru Xie, Shiyang Cheng, Yanling Li, Xiaohui Chen, Jian Zhu, Mingjun |
author_sort | Wang, Jianru |
collection | PubMed |
description | OBJECTIVE: Inflammation plays an important role in the pathophysiology of ischemic cardiomyopathy (ICM). We aimed to identify potential biomarkers of inflammation-related genes for ICM and build a model based on the potential biomarkers for the diagnosis of ICM. MATERIALS AND METHODS: The microarray datasets and RNA-Sequencing datasets of human ICM were downloaded from the Gene Expression Omnibus database. We integrated 8 microarray datasets via the SVA package to screen the differentially expressed genes (DEGs) between ICM and non-failing control samples, then the differentially expressed inflammation-related genes (DEIRGs) were identified. The least absolute shrinkage and selection operator, support vector machine recursive feature elimination, and random forest were utilized to screen the potential diagnostic biomarkers from the DEIRGs. The potential biomarkers were validated in the RNA-Sequencing datasets and the functional experiment of the ICM rat, respectively. A nomogram was established based on the potential biomarkers and evaluated via the area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), and Clinical impact curve (CIC). RESULTS: 64 DEGs and 19 DEIRGs were identified, respectively. 5 potential biomarkers (SERPINA3, FCN3, PTN, CD163, and SCUBE2) were ultimately selected. The validation results showed that each of these five potential biomarkers showed good discriminant power for ICM, and their expression trends were consistent with the bioinformatics results. The results of AUC, calibration curve, DCA, and CIC showed that the nomogram demonstrated good performance, calibration, and clinical utility. CONCLUSION: SERPINA3, FCN3, PTN, CD163, and SCUBE2 were identified as potential biomarkers associated with the inflammatory response to ICM. The proposed nomogram could potentially provide clinicians with a helpful tool to the diagnosis and treatment of ICM from an inflammatory perspective. |
format | Online Article Text |
id | pubmed-9445158 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94451582022-09-07 Identification of potential biomarkers of inflammation-related genes for ischemic cardiomyopathy Wang, Jianru Xie, Shiyang Cheng, Yanling Li, Xiaohui Chen, Jian Zhu, Mingjun Front Cardiovasc Med Cardiovascular Medicine OBJECTIVE: Inflammation plays an important role in the pathophysiology of ischemic cardiomyopathy (ICM). We aimed to identify potential biomarkers of inflammation-related genes for ICM and build a model based on the potential biomarkers for the diagnosis of ICM. MATERIALS AND METHODS: The microarray datasets and RNA-Sequencing datasets of human ICM were downloaded from the Gene Expression Omnibus database. We integrated 8 microarray datasets via the SVA package to screen the differentially expressed genes (DEGs) between ICM and non-failing control samples, then the differentially expressed inflammation-related genes (DEIRGs) were identified. The least absolute shrinkage and selection operator, support vector machine recursive feature elimination, and random forest were utilized to screen the potential diagnostic biomarkers from the DEIRGs. The potential biomarkers were validated in the RNA-Sequencing datasets and the functional experiment of the ICM rat, respectively. A nomogram was established based on the potential biomarkers and evaluated via the area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), and Clinical impact curve (CIC). RESULTS: 64 DEGs and 19 DEIRGs were identified, respectively. 5 potential biomarkers (SERPINA3, FCN3, PTN, CD163, and SCUBE2) were ultimately selected. The validation results showed that each of these five potential biomarkers showed good discriminant power for ICM, and their expression trends were consistent with the bioinformatics results. The results of AUC, calibration curve, DCA, and CIC showed that the nomogram demonstrated good performance, calibration, and clinical utility. CONCLUSION: SERPINA3, FCN3, PTN, CD163, and SCUBE2 were identified as potential biomarkers associated with the inflammatory response to ICM. The proposed nomogram could potentially provide clinicians with a helpful tool to the diagnosis and treatment of ICM from an inflammatory perspective. Frontiers Media S.A. 2022-08-23 /pmc/articles/PMC9445158/ /pubmed/36082132 http://dx.doi.org/10.3389/fcvm.2022.972274 Text en Copyright © 2022 Wang, Xie, Cheng, Li, Chen and Zhu. 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 Wang, Jianru Xie, Shiyang Cheng, Yanling Li, Xiaohui Chen, Jian Zhu, Mingjun Identification of potential biomarkers of inflammation-related genes for ischemic cardiomyopathy |
title | Identification of potential biomarkers of inflammation-related genes for ischemic cardiomyopathy |
title_full | Identification of potential biomarkers of inflammation-related genes for ischemic cardiomyopathy |
title_fullStr | Identification of potential biomarkers of inflammation-related genes for ischemic cardiomyopathy |
title_full_unstemmed | Identification of potential biomarkers of inflammation-related genes for ischemic cardiomyopathy |
title_short | Identification of potential biomarkers of inflammation-related genes for ischemic cardiomyopathy |
title_sort | identification of potential biomarkers of inflammation-related genes for ischemic cardiomyopathy |
topic | Cardiovascular Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9445158/ https://www.ncbi.nlm.nih.gov/pubmed/36082132 http://dx.doi.org/10.3389/fcvm.2022.972274 |
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