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Identification of Molecular Subtypes and Key Genes of Atherosclerosis Through Gene Expression Profiles
Atherosclerotic cardiovascular disease (ASCVD) caused by atherosclerosis (AS) is one of the highest causes of mortality worldwide. Although there have been many studies on AS, its etiology remains unclear. In order to carry out molecular characterization of different types of AS, we retrieved two da...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8113832/ https://www.ncbi.nlm.nih.gov/pubmed/33996893 http://dx.doi.org/10.3389/fmolb.2021.628546 |
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author | Yang, Yujia Cai, Yue Zhang, Yuan Yi, Xu Xu, Zhiqiang |
author_facet | Yang, Yujia Cai, Yue Zhang, Yuan Yi, Xu Xu, Zhiqiang |
author_sort | Yang, Yujia |
collection | PubMed |
description | Atherosclerotic cardiovascular disease (ASCVD) caused by atherosclerosis (AS) is one of the highest causes of mortality worldwide. Although there have been many studies on AS, its etiology remains unclear. In order to carry out molecular characterization of different types of AS, we retrieved two datasets composed of 151 AS samples and 32 normal samples from the Gene Expression Omnibus database. Using the non-negative matrix factorization (NMF) algorithm, we successfully divided the 151 AS samples into two subgroups. We then compared the molecular characteristics between the two groups using weighted gene co-expression analysis (WGCNA) and identified six key modules associated with the two subgroups. Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analysis were used to identify the potential functions and pathways associated with the modules. In addition, we used the cytoscape software to construct and visualize protein–protein networks so as to identify key genes in the modules of interest. Three hub genes including PTGER3, GNAI1, and IGFBP5 were further screened using the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Since the modules were associated with immune pathways, we performed immune cell infiltration analysis. We discovered a significant difference in the level of immune cell infiltration by naïve B cells, CD8 T cells, T regulatory cells (Tregs), resting NK cells, Monocytes, Macrophages M0, Macrophages M1, and Macrophages M2 between the two subgroups. In addition, we observed the three hub genes were positively correlated with Tregs but negatively correlated with Macrophages M0. We also found that the three key genes are differentially expressed between normal and diseased tissue, as well as in the different subgroups. Receiver operating characteristic (ROC) results showed a good performance in the validation dataset. These results may provide novel insight into cellular and molecular characteristics of AS and potential markers for diagnosis and targeted therapy. |
format | Online Article Text |
id | pubmed-8113832 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81138322021-05-13 Identification of Molecular Subtypes and Key Genes of Atherosclerosis Through Gene Expression Profiles Yang, Yujia Cai, Yue Zhang, Yuan Yi, Xu Xu, Zhiqiang Front Mol Biosci Molecular Biosciences Atherosclerotic cardiovascular disease (ASCVD) caused by atherosclerosis (AS) is one of the highest causes of mortality worldwide. Although there have been many studies on AS, its etiology remains unclear. In order to carry out molecular characterization of different types of AS, we retrieved two datasets composed of 151 AS samples and 32 normal samples from the Gene Expression Omnibus database. Using the non-negative matrix factorization (NMF) algorithm, we successfully divided the 151 AS samples into two subgroups. We then compared the molecular characteristics between the two groups using weighted gene co-expression analysis (WGCNA) and identified six key modules associated with the two subgroups. Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analysis were used to identify the potential functions and pathways associated with the modules. In addition, we used the cytoscape software to construct and visualize protein–protein networks so as to identify key genes in the modules of interest. Three hub genes including PTGER3, GNAI1, and IGFBP5 were further screened using the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Since the modules were associated with immune pathways, we performed immune cell infiltration analysis. We discovered a significant difference in the level of immune cell infiltration by naïve B cells, CD8 T cells, T regulatory cells (Tregs), resting NK cells, Monocytes, Macrophages M0, Macrophages M1, and Macrophages M2 between the two subgroups. In addition, we observed the three hub genes were positively correlated with Tregs but negatively correlated with Macrophages M0. We also found that the three key genes are differentially expressed between normal and diseased tissue, as well as in the different subgroups. Receiver operating characteristic (ROC) results showed a good performance in the validation dataset. These results may provide novel insight into cellular and molecular characteristics of AS and potential markers for diagnosis and targeted therapy. Frontiers Media S.A. 2021-04-28 /pmc/articles/PMC8113832/ /pubmed/33996893 http://dx.doi.org/10.3389/fmolb.2021.628546 Text en Copyright © 2021 Yang, Cai, Zhang, Yi and Xu. 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 | Molecular Biosciences Yang, Yujia Cai, Yue Zhang, Yuan Yi, Xu Xu, Zhiqiang Identification of Molecular Subtypes and Key Genes of Atherosclerosis Through Gene Expression Profiles |
title | Identification of Molecular Subtypes and Key Genes of Atherosclerosis Through Gene Expression Profiles |
title_full | Identification of Molecular Subtypes and Key Genes of Atherosclerosis Through Gene Expression Profiles |
title_fullStr | Identification of Molecular Subtypes and Key Genes of Atherosclerosis Through Gene Expression Profiles |
title_full_unstemmed | Identification of Molecular Subtypes and Key Genes of Atherosclerosis Through Gene Expression Profiles |
title_short | Identification of Molecular Subtypes and Key Genes of Atherosclerosis Through Gene Expression Profiles |
title_sort | identification of molecular subtypes and key genes of atherosclerosis through gene expression profiles |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8113832/ https://www.ncbi.nlm.nih.gov/pubmed/33996893 http://dx.doi.org/10.3389/fmolb.2021.628546 |
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