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Construction of an immune-related signature for predicting the ischemic events in patients undergoing carotid endarterectomy

Background: Inflammatory responses have drawn more attention to atherosclerosis; however, the immune-related genes (IRGs) as a prognostic factor in atherosclerotic plaque remain to be fully elucidated. Here, the purpose of this study was to investigate whether the IRGs could be identified as a relia...

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Autores principales: Li, Shifu, Zhang, Qian, Weng, Ling, Li, Jian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9592116/
https://www.ncbi.nlm.nih.gov/pubmed/36299596
http://dx.doi.org/10.3389/fgene.2022.1014264
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author Li, Shifu
Zhang, Qian
Weng, Ling
Li, Jian
author_facet Li, Shifu
Zhang, Qian
Weng, Ling
Li, Jian
author_sort Li, Shifu
collection PubMed
description Background: Inflammatory responses have drawn more attention to atherosclerosis; however, the immune-related genes (IRGs) as a prognostic factor in atherosclerotic plaque remain to be fully elucidated. Here, the purpose of this study was to investigate whether the IRGs could be identified as a reliable biomarker for predicting ischemic events in patients undergoing carotid endarterectomy (CEA). Methods: Two datasets GSE97210 and GSE21545 were downloaded from the Gene Expression Omnibus (GEO) database. The dataset GSE97210 was used to explore the significant pathways and differentially expressed IRGs (DEIRGs) between plaques and controls, which were further screened to identify the prognostic DEIRGs in the GSE21545 dataset. The identification of molecular subgroups with the prognostic gene expression patterns was achieved through nonnegative matrix factorization (NMF) clustering. Functional analyses including GO, KEGG, GSVA, and GSEA analyses, and immune analyses including xCell and ssGSEA algorithms were conducted to elucidate the underlying mechanisms. The prognostic risk model was constructed using the LASSO algorithm and multivariate Cox regression analysis. Results: A total of 796 DEIRGs (including 588 upregulated and 208 downregulated) were identified. Nine prognostic DEIRGs were further screened with univariate Cox regression analysis. Two clusters with different prognosis were grouped based on the prognostic DEIRGs. Immune infiltration analysis shows that cluster 2 with a better prognosis presented with a higher immune response than cluster 1. A prognostic model based on seven IRGs (IL2RA, NR4A2, DES, ERAP2, SLPI, RASGRP1, and AGTR2) was developed and verified. Consistent with the immune analysis of the cluster, the immune infiltration in the low-risk group with a better prognosis was also more active than that in the high-risk group. Finally, a nomogram based on the seven genes was constructed, which might have future implications in clinical care. Conclusion: The expression of immune-related genes is correlated with the immune microenvironment of atherosclerotic patients and could be applied to predict the ischemic events in patients undergoing CEA accurately.
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spelling pubmed-95921162022-10-25 Construction of an immune-related signature for predicting the ischemic events in patients undergoing carotid endarterectomy Li, Shifu Zhang, Qian Weng, Ling Li, Jian Front Genet Genetics Background: Inflammatory responses have drawn more attention to atherosclerosis; however, the immune-related genes (IRGs) as a prognostic factor in atherosclerotic plaque remain to be fully elucidated. Here, the purpose of this study was to investigate whether the IRGs could be identified as a reliable biomarker for predicting ischemic events in patients undergoing carotid endarterectomy (CEA). Methods: Two datasets GSE97210 and GSE21545 were downloaded from the Gene Expression Omnibus (GEO) database. The dataset GSE97210 was used to explore the significant pathways and differentially expressed IRGs (DEIRGs) between plaques and controls, which were further screened to identify the prognostic DEIRGs in the GSE21545 dataset. The identification of molecular subgroups with the prognostic gene expression patterns was achieved through nonnegative matrix factorization (NMF) clustering. Functional analyses including GO, KEGG, GSVA, and GSEA analyses, and immune analyses including xCell and ssGSEA algorithms were conducted to elucidate the underlying mechanisms. The prognostic risk model was constructed using the LASSO algorithm and multivariate Cox regression analysis. Results: A total of 796 DEIRGs (including 588 upregulated and 208 downregulated) were identified. Nine prognostic DEIRGs were further screened with univariate Cox regression analysis. Two clusters with different prognosis were grouped based on the prognostic DEIRGs. Immune infiltration analysis shows that cluster 2 with a better prognosis presented with a higher immune response than cluster 1. A prognostic model based on seven IRGs (IL2RA, NR4A2, DES, ERAP2, SLPI, RASGRP1, and AGTR2) was developed and verified. Consistent with the immune analysis of the cluster, the immune infiltration in the low-risk group with a better prognosis was also more active than that in the high-risk group. Finally, a nomogram based on the seven genes was constructed, which might have future implications in clinical care. Conclusion: The expression of immune-related genes is correlated with the immune microenvironment of atherosclerotic patients and could be applied to predict the ischemic events in patients undergoing CEA accurately. Frontiers Media S.A. 2022-10-10 /pmc/articles/PMC9592116/ /pubmed/36299596 http://dx.doi.org/10.3389/fgene.2022.1014264 Text en Copyright © 2022 Li, Zhang, Weng 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 Genetics
Li, Shifu
Zhang, Qian
Weng, Ling
Li, Jian
Construction of an immune-related signature for predicting the ischemic events in patients undergoing carotid endarterectomy
title Construction of an immune-related signature for predicting the ischemic events in patients undergoing carotid endarterectomy
title_full Construction of an immune-related signature for predicting the ischemic events in patients undergoing carotid endarterectomy
title_fullStr Construction of an immune-related signature for predicting the ischemic events in patients undergoing carotid endarterectomy
title_full_unstemmed Construction of an immune-related signature for predicting the ischemic events in patients undergoing carotid endarterectomy
title_short Construction of an immune-related signature for predicting the ischemic events in patients undergoing carotid endarterectomy
title_sort construction of an immune-related signature for predicting the ischemic events in patients undergoing carotid endarterectomy
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9592116/
https://www.ncbi.nlm.nih.gov/pubmed/36299596
http://dx.doi.org/10.3389/fgene.2022.1014264
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