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Identification of 5 hub genes for diagnosis of coronary artery disease

BACKGROUND: Coronary artery disease (CAD) is a main cause leading to increasing mortality of cardiovascular disease (CVD) worldwide. We aimed to discover marker genes and develop a diagnostic model for CAD. METHODS: CAD-related target genes were searched from DisGeNET. Count expression data and clin...

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Autores principales: Zhu, Pengyuan, Huang, Haitao, Xie, Tian, Liang, Huoqi, Li, Xing, Li, Xingyi, Dong, Hao, Yu, Xiaoqiang, Xia, Chunqiu, Zhong, Chongjun, Ming, Zhibing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10354867/
https://www.ncbi.nlm.nih.gov/pubmed/37476576
http://dx.doi.org/10.3389/fcvm.2023.1086127
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author Zhu, Pengyuan
Huang, Haitao
Xie, Tian
Liang, Huoqi
Li, Xing
Li, Xingyi
Dong, Hao
Yu, Xiaoqiang
Xia, Chunqiu
Zhong, Chongjun
Ming, Zhibing
author_facet Zhu, Pengyuan
Huang, Haitao
Xie, Tian
Liang, Huoqi
Li, Xing
Li, Xingyi
Dong, Hao
Yu, Xiaoqiang
Xia, Chunqiu
Zhong, Chongjun
Ming, Zhibing
author_sort Zhu, Pengyuan
collection PubMed
description BACKGROUND: Coronary artery disease (CAD) is a main cause leading to increasing mortality of cardiovascular disease (CVD) worldwide. We aimed to discover marker genes and develop a diagnostic model for CAD. METHODS: CAD-related target genes were searched from DisGeNET. Count expression data and clinical information were screened from the GSE202626 dataset. edgeR package identified differentially expressed genes (DEGs). Using online STRING tool and Cytoscape, protein-protein reactions (PPI) were predicted. WebGestaltR package was employed to functional enrichment analysis. We used Metascape to conduct module-based network analysis. VarElect algorithm provided genes-phenotype correlation analysis. Immune infiltration was assessed by ESTIMATE package and ssGSEA analysis. mRNAsi was determined by one class logistic regression (OCLR). A diagnostic model was constructed by SVM algorithm. RESULTS: 162 target genes were screened by intersection 1,714 DEGs and 1,708 CAD related target genes. 137 target genes of the 162 target genes were obtained using PPI analysis, in which those targets were enriched in inflammatory cytokine pathways, such as chemokine signaling pathway, and IL-17 signaling pathway. From the above 137 target genes, four functional modules (MCODE1-4) were extracted. From the 162 potential targets, CAD phenotype were directly and indirectly associated with 161 genes and 22 genes, respectively. Finally, 5 hub genes (CCL2, PTGS2, NLRP3, VEGFA, LTA) were screened by intersections with the top 20, directly and indirectly, and genes in MCODE1. PTGS2, NLRP3 and VEGFA were positively, while LTA was negatively correlated with immune cells scores. PTGS2, NLRP3 and VEGFA were negatively, while LTA was positively correlated with mRNAsi. A diagnostic model was successfully established, evidenced by 92.59% sensitivity and AUC was 0.9230 in the GSE202625 dataset and 94.11% sensitivity and AUC was 0.9706 in GSE120774 dataset. CONCLUSION: In this work, we identified 5 hub genes, which may be associated with CAD development.
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spelling pubmed-103548672023-07-20 Identification of 5 hub genes for diagnosis of coronary artery disease Zhu, Pengyuan Huang, Haitao Xie, Tian Liang, Huoqi Li, Xing Li, Xingyi Dong, Hao Yu, Xiaoqiang Xia, Chunqiu Zhong, Chongjun Ming, Zhibing Front Cardiovasc Med Cardiovascular Medicine BACKGROUND: Coronary artery disease (CAD) is a main cause leading to increasing mortality of cardiovascular disease (CVD) worldwide. We aimed to discover marker genes and develop a diagnostic model for CAD. METHODS: CAD-related target genes were searched from DisGeNET. Count expression data and clinical information were screened from the GSE202626 dataset. edgeR package identified differentially expressed genes (DEGs). Using online STRING tool and Cytoscape, protein-protein reactions (PPI) were predicted. WebGestaltR package was employed to functional enrichment analysis. We used Metascape to conduct module-based network analysis. VarElect algorithm provided genes-phenotype correlation analysis. Immune infiltration was assessed by ESTIMATE package and ssGSEA analysis. mRNAsi was determined by one class logistic regression (OCLR). A diagnostic model was constructed by SVM algorithm. RESULTS: 162 target genes were screened by intersection 1,714 DEGs and 1,708 CAD related target genes. 137 target genes of the 162 target genes were obtained using PPI analysis, in which those targets were enriched in inflammatory cytokine pathways, such as chemokine signaling pathway, and IL-17 signaling pathway. From the above 137 target genes, four functional modules (MCODE1-4) were extracted. From the 162 potential targets, CAD phenotype were directly and indirectly associated with 161 genes and 22 genes, respectively. Finally, 5 hub genes (CCL2, PTGS2, NLRP3, VEGFA, LTA) were screened by intersections with the top 20, directly and indirectly, and genes in MCODE1. PTGS2, NLRP3 and VEGFA were positively, while LTA was negatively correlated with immune cells scores. PTGS2, NLRP3 and VEGFA were negatively, while LTA was positively correlated with mRNAsi. A diagnostic model was successfully established, evidenced by 92.59% sensitivity and AUC was 0.9230 in the GSE202625 dataset and 94.11% sensitivity and AUC was 0.9706 in GSE120774 dataset. CONCLUSION: In this work, we identified 5 hub genes, which may be associated with CAD development. Frontiers Media S.A. 2023-07-05 /pmc/articles/PMC10354867/ /pubmed/37476576 http://dx.doi.org/10.3389/fcvm.2023.1086127 Text en © 2023 Zhu, Huang, Xie, Liang, Li, Li, Dong, Yu, Xia, Zhong and Ming. 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
Zhu, Pengyuan
Huang, Haitao
Xie, Tian
Liang, Huoqi
Li, Xing
Li, Xingyi
Dong, Hao
Yu, Xiaoqiang
Xia, Chunqiu
Zhong, Chongjun
Ming, Zhibing
Identification of 5 hub genes for diagnosis of coronary artery disease
title Identification of 5 hub genes for diagnosis of coronary artery disease
title_full Identification of 5 hub genes for diagnosis of coronary artery disease
title_fullStr Identification of 5 hub genes for diagnosis of coronary artery disease
title_full_unstemmed Identification of 5 hub genes for diagnosis of coronary artery disease
title_short Identification of 5 hub genes for diagnosis of coronary artery disease
title_sort identification of 5 hub genes for diagnosis of coronary artery disease
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10354867/
https://www.ncbi.nlm.nih.gov/pubmed/37476576
http://dx.doi.org/10.3389/fcvm.2023.1086127
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