Epithelial–mesenchymal transition related genes in unruptured aneurysms identified through weighted gene coexpression network analysis
Abstract Intracranial aneurysm (IA) can cause fatal subarachnoid hemorrhage (SAH) after rupture, and identifying patients with unruptured IAs is essential for reducing SAH fatalities. The epithelial–mesenchymal transition (EMT) may be vital to IA progression. Here, identified key EMT-related genes i...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-04390-6 |
| _version_ | 1798034733646479360 |
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| author | Yong’an Jiang JingXing Leng Qianxia Lin Fang Zhou |
| author_facet | Yong’an Jiang JingXing Leng Qianxia Lin Fang Zhou |
| author_sort | Yong’an Jiang |
| collection | DOAJ |
| description | Abstract Intracranial aneurysm (IA) can cause fatal subarachnoid hemorrhage (SAH) after rupture, and identifying patients with unruptured IAs is essential for reducing SAH fatalities. The epithelial–mesenchymal transition (EMT) may be vital to IA progression. Here, identified key EMT-related genes in aneurysms and their pathogenic mechanisms via bioinformatic analysis. The GSE13353, GSE75436, and GSE54083 datasets from Gene Expression Omnibus were analyzed with limma to identify differentially expressed genes (DEGs) among unruptured aneurysms, ruptured aneurysms, and healthy samples. The results revealed that three EMT-related DEGs (ADIPOQ, WNT11, and CCL21) were shared among all groups. Coexpression modules and hub genes were identified via weighted gene co-expression network analysis, revealing two significant modules (red and green) and 14 EMT-related genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses suggested that cytokine interactions were closely related. Gene set enrichment analysis revealed that unruptured aneurysms were enriched for the terms “inflammatory response” and “vascular endothelial growth”. Protein–protein interaction analysis identified seven key genes, which were evaluated with the GSE54083 dataset to determine their sensitivity and specificity. In the external validation set, we verified the differential expression of seven genes in unruptured aneurysms and normal samples. Together, these findings indicate that FN1, and SPARC may help distinguish normal patients from patients with asymptomatic IAs. |
| first_indexed | 2024-04-11T20:48:21Z |
| format | Article |
| id | doaj.art-7f9f32fd7cdb4046849609d1f628fb70 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T20:48:21Z |
| publishDate | 2022-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-7f9f32fd7cdb4046849609d1f628fb702022-12-22T04:03:57ZengNature PortfolioScientific Reports2045-23222022-01-0112111210.1038/s41598-021-04390-6Epithelial–mesenchymal transition related genes in unruptured aneurysms identified through weighted gene coexpression network analysisYong’an Jiang0JingXing Leng1Qianxia Lin2Fang Zhou3Department of Neurosurgery, Jiangxi Provincial People’s HospitalDepartment of Neurosurgery, Jiangxi Provincial People’s HospitalJiangxi University of Traditional Chinese MedicineDepartment of Vascular Breast Surgery, Jiangxi Provincial People’s HospitalAbstract Intracranial aneurysm (IA) can cause fatal subarachnoid hemorrhage (SAH) after rupture, and identifying patients with unruptured IAs is essential for reducing SAH fatalities. The epithelial–mesenchymal transition (EMT) may be vital to IA progression. Here, identified key EMT-related genes in aneurysms and their pathogenic mechanisms via bioinformatic analysis. The GSE13353, GSE75436, and GSE54083 datasets from Gene Expression Omnibus were analyzed with limma to identify differentially expressed genes (DEGs) among unruptured aneurysms, ruptured aneurysms, and healthy samples. The results revealed that three EMT-related DEGs (ADIPOQ, WNT11, and CCL21) were shared among all groups. Coexpression modules and hub genes were identified via weighted gene co-expression network analysis, revealing two significant modules (red and green) and 14 EMT-related genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses suggested that cytokine interactions were closely related. Gene set enrichment analysis revealed that unruptured aneurysms were enriched for the terms “inflammatory response” and “vascular endothelial growth”. Protein–protein interaction analysis identified seven key genes, which were evaluated with the GSE54083 dataset to determine their sensitivity and specificity. In the external validation set, we verified the differential expression of seven genes in unruptured aneurysms and normal samples. Together, these findings indicate that FN1, and SPARC may help distinguish normal patients from patients with asymptomatic IAs.https://doi.org/10.1038/s41598-021-04390-6 |
| spellingShingle | Yong’an Jiang JingXing Leng Qianxia Lin Fang Zhou Epithelial–mesenchymal transition related genes in unruptured aneurysms identified through weighted gene coexpression network analysis Scientific Reports |
| title | Epithelial–mesenchymal transition related genes in unruptured aneurysms identified through weighted gene coexpression network analysis |
| title_full | Epithelial–mesenchymal transition related genes in unruptured aneurysms identified through weighted gene coexpression network analysis |
| title_fullStr | Epithelial–mesenchymal transition related genes in unruptured aneurysms identified through weighted gene coexpression network analysis |
| title_full_unstemmed | Epithelial–mesenchymal transition related genes in unruptured aneurysms identified through weighted gene coexpression network analysis |
| title_short | Epithelial–mesenchymal transition related genes in unruptured aneurysms identified through weighted gene coexpression network analysis |
| title_sort | epithelial mesenchymal transition related genes in unruptured aneurysms identified through weighted gene coexpression network analysis |
| url | https://doi.org/10.1038/s41598-021-04390-6 |
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