The underlying molecular mechanisms and biomarkers of plaque vulnerability based on bioinformatics analysis
Abstract Aim The study aimed to identify the underlying differentially expressed genes (DEGs) and mechanism of unstable atherosclerotic plaque using bioinformatics methods. Methods GSE120521, which includes four unstable samples and four stable atherosclerotic samples, was downloaded from the GEO da...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-10-01
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| Series: | European Journal of Medical Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40001-022-00840-7 |
| _version_ | 1811335233226670080 |
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| author | Rui Cheng Xiaojiang Xu Shurong Yang Zhongqian mi Yong Zhao Jinhua gao Feiyan Yu Xiuyun Ren |
| author_facet | Rui Cheng Xiaojiang Xu Shurong Yang Zhongqian mi Yong Zhao Jinhua gao Feiyan Yu Xiuyun Ren |
| author_sort | Rui Cheng |
| collection | DOAJ |
| description | Abstract Aim The study aimed to identify the underlying differentially expressed genes (DEGs) and mechanism of unstable atherosclerotic plaque using bioinformatics methods. Methods GSE120521, which includes four unstable samples and four stable atherosclerotic samples, was downloaded from the GEO database. DEGs were identified using LIMMA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were performed using the Database for metascape Visualization online tool. Based on the STRING database, protein–protein interactions (PPIs) network among DEGs were constructed. Regulatory networks were visualized using Cytoscape. We use the xCell to analyze the different immune cell subtypes. Results A total of 1626 DEGs (1034 up-regulated and 592 down-regulated DEGs) were identified between unstable and stable samples. I pulled 62 transcription factors (34 up-regulated TFs and 28 down-regulated TFs) from the Trust database. The up-regulated TFs were mainly enrichment in positive regulation of myeloid leukocyte differentiation, and the down-regulated TFs were mainly enrichment in connective tissue development. In the PPI network, RB1, CEBPA, PPARG, BATF was the most significantly up-regulated gene in ruptured atherosclerotic samples. The immune cell composition enriched in CD cells and macrophages in the unstable carotid plaque. Conclusions Upregulated RB1, CEBPA, PPARG, BATF and down-regulated SRF, MYOCD, HEY2, GATA6 might perform critical promotional roles in atherosclerotic plaque rupture, furthermore, number and polarization of macrophages may play an important role in vulnerable plaques. |
| first_indexed | 2024-04-13T17:21:04Z |
| format | Article |
| id | doaj.art-16c6d00c5a1440418636204275882dff |
| institution | Directory Open Access Journal |
| issn | 2047-783X |
| language | English |
| last_indexed | 2024-04-13T17:21:04Z |
| publishDate | 2022-10-01 |
| publisher | BMC |
| record_format | Article |
| series | European Journal of Medical Research |
| spelling | doaj.art-16c6d00c5a1440418636204275882dff2022-12-22T02:37:57ZengBMCEuropean Journal of Medical Research2047-783X2022-10-0127111010.1186/s40001-022-00840-7The underlying molecular mechanisms and biomarkers of plaque vulnerability based on bioinformatics analysisRui Cheng0Xiaojiang Xu1Shurong Yang2Zhongqian mi3Yong Zhao4Jinhua gao5Feiyan Yu6Xiuyun Ren7Shanxi Medical UniversityShanxi Medical UniversityShanxi Medical UniversityShanxi Medical UniversityShanxi Medical University School and Hospital of StomatologyShanxi Medical University School and Hospital of StomatologyShanxi Medical UniversityShanxi Medical UniversityAbstract Aim The study aimed to identify the underlying differentially expressed genes (DEGs) and mechanism of unstable atherosclerotic plaque using bioinformatics methods. Methods GSE120521, which includes four unstable samples and four stable atherosclerotic samples, was downloaded from the GEO database. DEGs were identified using LIMMA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were performed using the Database for metascape Visualization online tool. Based on the STRING database, protein–protein interactions (PPIs) network among DEGs were constructed. Regulatory networks were visualized using Cytoscape. We use the xCell to analyze the different immune cell subtypes. Results A total of 1626 DEGs (1034 up-regulated and 592 down-regulated DEGs) were identified between unstable and stable samples. I pulled 62 transcription factors (34 up-regulated TFs and 28 down-regulated TFs) from the Trust database. The up-regulated TFs were mainly enrichment in positive regulation of myeloid leukocyte differentiation, and the down-regulated TFs were mainly enrichment in connective tissue development. In the PPI network, RB1, CEBPA, PPARG, BATF was the most significantly up-regulated gene in ruptured atherosclerotic samples. The immune cell composition enriched in CD cells and macrophages in the unstable carotid plaque. Conclusions Upregulated RB1, CEBPA, PPARG, BATF and down-regulated SRF, MYOCD, HEY2, GATA6 might perform critical promotional roles in atherosclerotic plaque rupture, furthermore, number and polarization of macrophages may play an important role in vulnerable plaques.https://doi.org/10.1186/s40001-022-00840-7AtherosclerosisDifferentially expressed genesMacrophagesUnstable atherosclerotic plaqueTranscription factors |
| spellingShingle | Rui Cheng Xiaojiang Xu Shurong Yang Zhongqian mi Yong Zhao Jinhua gao Feiyan Yu Xiuyun Ren The underlying molecular mechanisms and biomarkers of plaque vulnerability based on bioinformatics analysis European Journal of Medical Research Atherosclerosis Differentially expressed genes Macrophages Unstable atherosclerotic plaque Transcription factors |
| title | The underlying molecular mechanisms and biomarkers of plaque vulnerability based on bioinformatics analysis |
| title_full | The underlying molecular mechanisms and biomarkers of plaque vulnerability based on bioinformatics analysis |
| title_fullStr | The underlying molecular mechanisms and biomarkers of plaque vulnerability based on bioinformatics analysis |
| title_full_unstemmed | The underlying molecular mechanisms and biomarkers of plaque vulnerability based on bioinformatics analysis |
| title_short | The underlying molecular mechanisms and biomarkers of plaque vulnerability based on bioinformatics analysis |
| title_sort | underlying molecular mechanisms and biomarkers of plaque vulnerability based on bioinformatics analysis |
| topic | Atherosclerosis Differentially expressed genes Macrophages Unstable atherosclerotic plaque Transcription factors |
| url | https://doi.org/10.1186/s40001-022-00840-7 |
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