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Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy
BACKGROUND: Atherosclerosis is the main pathological change in atherosclerotic cardiovascular disease, and its underlying mechanisms are not well understood. The aim of this study was to explore the hub genes involved in atherosclerosis and their potential mechanisms through bioinformatics analysis....
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10176947/ https://www.ncbi.nlm.nih.gov/pubmed/37173673 http://dx.doi.org/10.1186/s12920-023-01531-w |
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author | Zheng, Zhipeng Yuan, Dong Shen, Cheng Zhang, Zhiyuan Ye, Jun Zhu, Li |
author_facet | Zheng, Zhipeng Yuan, Dong Shen, Cheng Zhang, Zhiyuan Ye, Jun Zhu, Li |
author_sort | Zheng, Zhipeng |
collection | PubMed |
description | BACKGROUND: Atherosclerosis is the main pathological change in atherosclerotic cardiovascular disease, and its underlying mechanisms are not well understood. The aim of this study was to explore the hub genes involved in atherosclerosis and their potential mechanisms through bioinformatics analysis. METHODS: Three microarray datasets from Gene Expression Omnibus (GEO) identified robust differentially expressed genes (DEGs) by robust rank aggregation (RRA). We performed connectivity map (CMap) analysis and functional enrichment analysis on robust DEGs and constructed a protein‒protein interaction (PPI) network using the STRING database to identify the hub gene using 12 algorithms of cytoHubba in Cytoscape. Receiver operating characteristic (ROC) analysis was used to assess the diagnostic potency of the hub genes.The CIBERSORT algorithm was used to perform immunocyte infiltration analysis and explore the association between the identified biomarkers and infiltrating immunocytes using Spearman’s rank correlation analysis in R software. Finally, we evaluated the expression of the hub gene in foam cells. RESULTS: A total of 155 robust DEGs were screened by RRA and were revealed to be mainly associated with cytokines and chemokines by functional enrichment analysis. CD52 and IL1RN were identified as hub genes and were validated in the GSE40231 dataset. Immunocyte infiltration analysis showed that CD52 was positively correlated with gamma delta T cells, M1 macrophages and CD4 memory resting T cells, while IL1RN was positively correlated with monocytes and activated mast cells. RT-qPCR results indicate that CD52 and IL1RN were highly expressed in foam cells, in agreement with bioinformatics analysis. CONCLUSIONS: This study has established that CD52 and IL1RN may play a key role in the occurrence and development of atherosclerosis, which opens new lines of thought for further research on the pathogenesis of atherosclerosis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01531-w. |
format | Online Article Text |
id | pubmed-10176947 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-101769472023-05-13 Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy Zheng, Zhipeng Yuan, Dong Shen, Cheng Zhang, Zhiyuan Ye, Jun Zhu, Li BMC Med Genomics Research BACKGROUND: Atherosclerosis is the main pathological change in atherosclerotic cardiovascular disease, and its underlying mechanisms are not well understood. The aim of this study was to explore the hub genes involved in atherosclerosis and their potential mechanisms through bioinformatics analysis. METHODS: Three microarray datasets from Gene Expression Omnibus (GEO) identified robust differentially expressed genes (DEGs) by robust rank aggregation (RRA). We performed connectivity map (CMap) analysis and functional enrichment analysis on robust DEGs and constructed a protein‒protein interaction (PPI) network using the STRING database to identify the hub gene using 12 algorithms of cytoHubba in Cytoscape. Receiver operating characteristic (ROC) analysis was used to assess the diagnostic potency of the hub genes.The CIBERSORT algorithm was used to perform immunocyte infiltration analysis and explore the association between the identified biomarkers and infiltrating immunocytes using Spearman’s rank correlation analysis in R software. Finally, we evaluated the expression of the hub gene in foam cells. RESULTS: A total of 155 robust DEGs were screened by RRA and were revealed to be mainly associated with cytokines and chemokines by functional enrichment analysis. CD52 and IL1RN were identified as hub genes and were validated in the GSE40231 dataset. Immunocyte infiltration analysis showed that CD52 was positively correlated with gamma delta T cells, M1 macrophages and CD4 memory resting T cells, while IL1RN was positively correlated with monocytes and activated mast cells. RT-qPCR results indicate that CD52 and IL1RN were highly expressed in foam cells, in agreement with bioinformatics analysis. CONCLUSIONS: This study has established that CD52 and IL1RN may play a key role in the occurrence and development of atherosclerosis, which opens new lines of thought for further research on the pathogenesis of atherosclerosis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01531-w. BioMed Central 2023-05-12 /pmc/articles/PMC10176947/ /pubmed/37173673 http://dx.doi.org/10.1186/s12920-023-01531-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 Zheng, Zhipeng Yuan, Dong Shen, Cheng Zhang, Zhiyuan Ye, Jun Zhu, Li Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy |
title | Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy |
title_full | Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy |
title_fullStr | Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy |
title_full_unstemmed | Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy |
title_short | Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy |
title_sort | identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10176947/ https://www.ncbi.nlm.nih.gov/pubmed/37173673 http://dx.doi.org/10.1186/s12920-023-01531-w |
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