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Identification of foam cell biomarkers by microarray analysis
BACKGROUND: Lipid infiltration and inflammatory response run through the occurrence of atherosclerosis. Differentiation into macrophages and foam cell formation are the key steps of AS. Aim of this study was that the differential gene expression between foam cells and macrophages was analyzed to sea...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7201525/ https://www.ncbi.nlm.nih.gov/pubmed/32375652 http://dx.doi.org/10.1186/s12872-020-01495-0 |
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author | Song, Zikai Lv, Shijie Wu, Haidi Qin, Ling Cao, Hongyan Zhang, Bo Ren, Shuping |
author_facet | Song, Zikai Lv, Shijie Wu, Haidi Qin, Ling Cao, Hongyan Zhang, Bo Ren, Shuping |
author_sort | Song, Zikai |
collection | PubMed |
description | BACKGROUND: Lipid infiltration and inflammatory response run through the occurrence of atherosclerosis. Differentiation into macrophages and foam cell formation are the key steps of AS. Aim of this study was that the differential gene expression between foam cells and macrophages was analyzed to search the key links of foam cell generation, so as to explore the pathogenesis of atherosclerosis and provide targets for the early screening and prevention of coronary artery disease (CAD). METHODS: The gene expression profiles of GSE9874 were downloaded from Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE9874) on GPL96 [HG-U133A] Affymetrix Human Genome U133. A total of 22,383 genes were analyzed for differentially expression genes (DEGs) by Bayes package. GO enrichment analysis and KEGG pathway analysis for DEGs were performed using KOBAS 3.0 software (Peking University, Beijing, China). STRING software (STRING 10.0; European Molecular Biology Laboratory, Heidelberg, Germany) was used to analyze the protein-protein interaction (PPI) of DEGs. RESULTS: A total of 167 DEGs between macrophages and foam cells were identified. Compared with macrophages, 102 genes were significantly upregulated and 65 genes were significantly downregulated (P < 0.01, fold-change > 1) in foam cells. DEGs were mainly enrich in ‘sterol biosynthetic and metabolic process’, ‘cholesterol metabolic and biosynthetic process’ by GO enrichment analysis. The results of KEGG pathway analysis showed all differential genes are involved in biological processes through 143 KEGG pathways. A PPI network of the DEGs was constructed and 10 outstanding genes of the PPI network was identified by using Cytoscape, which include HMGCR, SREBF2, LDLR, HMGCS1, FDFT1, LPL, DHCR24, SQLE, ABCA1 and FDPS. Conclusion: Lipid metabolism related genes and molecular pathways were the key to the transformation of macrophages into foam cells. Therefore, lipid metabolism disorder is the key to turn macrophages into foam cells, which plays a major role in CAD. |
format | Online Article Text |
id | pubmed-7201525 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-72015252020-05-08 Identification of foam cell biomarkers by microarray analysis Song, Zikai Lv, Shijie Wu, Haidi Qin, Ling Cao, Hongyan Zhang, Bo Ren, Shuping BMC Cardiovasc Disord Research Article BACKGROUND: Lipid infiltration and inflammatory response run through the occurrence of atherosclerosis. Differentiation into macrophages and foam cell formation are the key steps of AS. Aim of this study was that the differential gene expression between foam cells and macrophages was analyzed to search the key links of foam cell generation, so as to explore the pathogenesis of atherosclerosis and provide targets for the early screening and prevention of coronary artery disease (CAD). METHODS: The gene expression profiles of GSE9874 were downloaded from Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE9874) on GPL96 [HG-U133A] Affymetrix Human Genome U133. A total of 22,383 genes were analyzed for differentially expression genes (DEGs) by Bayes package. GO enrichment analysis and KEGG pathway analysis for DEGs were performed using KOBAS 3.0 software (Peking University, Beijing, China). STRING software (STRING 10.0; European Molecular Biology Laboratory, Heidelberg, Germany) was used to analyze the protein-protein interaction (PPI) of DEGs. RESULTS: A total of 167 DEGs between macrophages and foam cells were identified. Compared with macrophages, 102 genes were significantly upregulated and 65 genes were significantly downregulated (P < 0.01, fold-change > 1) in foam cells. DEGs were mainly enrich in ‘sterol biosynthetic and metabolic process’, ‘cholesterol metabolic and biosynthetic process’ by GO enrichment analysis. The results of KEGG pathway analysis showed all differential genes are involved in biological processes through 143 KEGG pathways. A PPI network of the DEGs was constructed and 10 outstanding genes of the PPI network was identified by using Cytoscape, which include HMGCR, SREBF2, LDLR, HMGCS1, FDFT1, LPL, DHCR24, SQLE, ABCA1 and FDPS. Conclusion: Lipid metabolism related genes and molecular pathways were the key to the transformation of macrophages into foam cells. Therefore, lipid metabolism disorder is the key to turn macrophages into foam cells, which plays a major role in CAD. BioMed Central 2020-05-06 /pmc/articles/PMC7201525/ /pubmed/32375652 http://dx.doi.org/10.1186/s12872-020-01495-0 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Song, Zikai Lv, Shijie Wu, Haidi Qin, Ling Cao, Hongyan Zhang, Bo Ren, Shuping Identification of foam cell biomarkers by microarray analysis |
title | Identification of foam cell biomarkers by microarray analysis |
title_full | Identification of foam cell biomarkers by microarray analysis |
title_fullStr | Identification of foam cell biomarkers by microarray analysis |
title_full_unstemmed | Identification of foam cell biomarkers by microarray analysis |
title_short | Identification of foam cell biomarkers by microarray analysis |
title_sort | identification of foam cell biomarkers by microarray analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7201525/ https://www.ncbi.nlm.nih.gov/pubmed/32375652 http://dx.doi.org/10.1186/s12872-020-01495-0 |
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