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Identification of key genes and pathways in atherosclerosis using integrated bioinformatics analysis

BACKGROUND: Atherosclerosis (AS) is a chronic inflammatory disease that might induce severe cardiovascular events, such as myocardial infarction and cerebral infarction. These risk factors in the pathogenesis of AS remain uncertain and further research is needed. This study aims to explore the poten...

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Autores principales: Li, Shihuan, Li, Suqin, Li, Qingjie, Zhou, Qiaofeng, Liao, Wenli, Yu, Liangzhu, Ouyang, Changhan, Xia, Hongli, Liu, Chao, Li, Mincai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10183119/
https://www.ncbi.nlm.nih.gov/pubmed/37179331
http://dx.doi.org/10.1186/s12920-023-01533-8
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author Li, Shihuan
Li, Suqin
Li, Qingjie
Zhou, Qiaofeng
Liao, Wenli
Yu, Liangzhu
Ouyang, Changhan
Xia, Hongli
Liu, Chao
Li, Mincai
author_facet Li, Shihuan
Li, Suqin
Li, Qingjie
Zhou, Qiaofeng
Liao, Wenli
Yu, Liangzhu
Ouyang, Changhan
Xia, Hongli
Liu, Chao
Li, Mincai
author_sort Li, Shihuan
collection PubMed
description BACKGROUND: Atherosclerosis (AS) is a chronic inflammatory disease that might induce severe cardiovascular events, such as myocardial infarction and cerebral infarction. These risk factors in the pathogenesis of AS remain uncertain and further research is needed. This study aims to explore the potential molecular mechanisms of AS by bioinformatics analyses. METHODS: GSE100927 gene expression profiles, including 69 AS samples and 35 healthy controls, were downloaded from Gene Expression Omnibus database and indenfied for key genes and pathways in AS. RESULTS: A total of 443 differentially expressed genes (DEGs) between control and AS were identified, including 323 down-regulated genes and 120 up-regulated genes. The Gene ontology terms enriched by the up-regulated DEGs were associated with the regulation of leukocyte activation, endocytic vesicle, and cytokine binding, while the down-regulated DEGs were associated with negative regulation of cell growth, extracellular matrix, and G protein-coupled receptor binding. KEGG pathway analysis showed that the up-regulated DEGs were enriched in Osteoclast differentiation and Phagosome, while the down-regulated DEGs were enriched in vascular smooth muscle contraction and cGMP-PKG signaling pathway. Using the modular analysis of Cytoscape, we identified 3 modules mainly involved in Leishmaniasis and Osteoclast differentiation. The GSEA analysis showed the up-regulated gene sets were enriched in the ribosome, ascorbated metabolism, and propanoate metabolism. The LASSO Cox regression analysis showed the top 3 genes were TNF, CX3CR1, and COL1R1. Finally, we found these immune cells were conferred significantly higher infiltrating density in the AS group. CONCLUSIONS: Our data showed the pathway of Osteoclast differentiation and Leishmaniasis was involved in the AS process and we developed a three-gene model base on the prognosis of AS. These findings clarified the gene regulatory network of AS and may provide a novel target for AS therapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01533-8.
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spelling pubmed-101831192023-05-15 Identification of key genes and pathways in atherosclerosis using integrated bioinformatics analysis Li, Shihuan Li, Suqin Li, Qingjie Zhou, Qiaofeng Liao, Wenli Yu, Liangzhu Ouyang, Changhan Xia, Hongli Liu, Chao Li, Mincai BMC Med Genomics Research BACKGROUND: Atherosclerosis (AS) is a chronic inflammatory disease that might induce severe cardiovascular events, such as myocardial infarction and cerebral infarction. These risk factors in the pathogenesis of AS remain uncertain and further research is needed. This study aims to explore the potential molecular mechanisms of AS by bioinformatics analyses. METHODS: GSE100927 gene expression profiles, including 69 AS samples and 35 healthy controls, were downloaded from Gene Expression Omnibus database and indenfied for key genes and pathways in AS. RESULTS: A total of 443 differentially expressed genes (DEGs) between control and AS were identified, including 323 down-regulated genes and 120 up-regulated genes. The Gene ontology terms enriched by the up-regulated DEGs were associated with the regulation of leukocyte activation, endocytic vesicle, and cytokine binding, while the down-regulated DEGs were associated with negative regulation of cell growth, extracellular matrix, and G protein-coupled receptor binding. KEGG pathway analysis showed that the up-regulated DEGs were enriched in Osteoclast differentiation and Phagosome, while the down-regulated DEGs were enriched in vascular smooth muscle contraction and cGMP-PKG signaling pathway. Using the modular analysis of Cytoscape, we identified 3 modules mainly involved in Leishmaniasis and Osteoclast differentiation. The GSEA analysis showed the up-regulated gene sets were enriched in the ribosome, ascorbated metabolism, and propanoate metabolism. The LASSO Cox regression analysis showed the top 3 genes were TNF, CX3CR1, and COL1R1. Finally, we found these immune cells were conferred significantly higher infiltrating density in the AS group. CONCLUSIONS: Our data showed the pathway of Osteoclast differentiation and Leishmaniasis was involved in the AS process and we developed a three-gene model base on the prognosis of AS. These findings clarified the gene regulatory network of AS and may provide a novel target for AS therapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01533-8. BioMed Central 2023-05-13 /pmc/articles/PMC10183119/ /pubmed/37179331 http://dx.doi.org/10.1186/s12920-023-01533-8 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
Li, Shihuan
Li, Suqin
Li, Qingjie
Zhou, Qiaofeng
Liao, Wenli
Yu, Liangzhu
Ouyang, Changhan
Xia, Hongli
Liu, Chao
Li, Mincai
Identification of key genes and pathways in atherosclerosis using integrated bioinformatics analysis
title Identification of key genes and pathways in atherosclerosis using integrated bioinformatics analysis
title_full Identification of key genes and pathways in atherosclerosis using integrated bioinformatics analysis
title_fullStr Identification of key genes and pathways in atherosclerosis using integrated bioinformatics analysis
title_full_unstemmed Identification of key genes and pathways in atherosclerosis using integrated bioinformatics analysis
title_short Identification of key genes and pathways in atherosclerosis using integrated bioinformatics analysis
title_sort identification of key genes and pathways in atherosclerosis using integrated bioinformatics analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10183119/
https://www.ncbi.nlm.nih.gov/pubmed/37179331
http://dx.doi.org/10.1186/s12920-023-01533-8
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