Identification of key biomarkers and immune infiltration in the thoracic acute aortic dissection by bioinformatics analysis
Abstract Background Thoracic acute aortic dissection (TAAD), one of the most fatal cardiovascular diseases, leads to sudden death, however, its mechanism remains unclear. Methods Three Gene Expression Omnibus datasets were employed to detect differentially expressed genes (DEGs). A similar function...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-02-01
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| Series: | BMC Cardiovascular Disorders |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12872-023-03110-4 |
| _version_ | 1811166060287623168 |
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| author | Jun Luo Haoming Shi Haoyu Ran Cheng Zhang Qingchen Wu Yue Shao |
| author_facet | Jun Luo Haoming Shi Haoyu Ran Cheng Zhang Qingchen Wu Yue Shao |
| author_sort | Jun Luo |
| collection | DOAJ |
| description | Abstract Background Thoracic acute aortic dissection (TAAD), one of the most fatal cardiovascular diseases, leads to sudden death, however, its mechanism remains unclear. Methods Three Gene Expression Omnibus datasets were employed to detect differentially expressed genes (DEGs). A similar function and co-expression network was identified by weighted gene co-expression network analysis. The least absolute shrinkage and selection operator, random forest, and support vector machines-recursive feature elimination were utilized to filter diagnostic TAAD markers, and then screened markers were validated by quantitative real-time PCR and another independent dataset. CIBERSORT was deployed to analyze and evaluate immune cell infiltration in TAAD tissues. Results Twenty-five DEGs were identified and narrowed down to three after screening. Finally, two genes, SLC11A1 and FGL2, were verified by another dataset and qRT-PCR. Function analysis revealed that SLC11A1 and FGL2 play significant roles in immune-inflammatory responses. Conclusion SLC11A1 and FGL2 are differently expressed in aortic dissection and may be involved in immune-inflammatory responses. |
| first_indexed | 2024-04-10T15:46:21Z |
| format | Article |
| id | doaj.art-5a336a69b8084b94833bb37f0f98cf6d |
| institution | Directory Open Access Journal |
| issn | 1471-2261 |
| language | English |
| last_indexed | 2024-04-10T15:46:21Z |
| publishDate | 2023-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cardiovascular Disorders |
| spelling | doaj.art-5a336a69b8084b94833bb37f0f98cf6d2023-02-12T12:03:59ZengBMCBMC Cardiovascular Disorders1471-22612023-02-0123111010.1186/s12872-023-03110-4Identification of key biomarkers and immune infiltration in the thoracic acute aortic dissection by bioinformatics analysisJun Luo0Haoming Shi1Haoyu Ran2Cheng Zhang3Qingchen Wu4Yue Shao5Department of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical UniversityChongqing Medical UniversityChongqing Medical UniversityDepartment of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical UniversityDepartment of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical UniversityDepartment of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical UniversityAbstract Background Thoracic acute aortic dissection (TAAD), one of the most fatal cardiovascular diseases, leads to sudden death, however, its mechanism remains unclear. Methods Three Gene Expression Omnibus datasets were employed to detect differentially expressed genes (DEGs). A similar function and co-expression network was identified by weighted gene co-expression network analysis. The least absolute shrinkage and selection operator, random forest, and support vector machines-recursive feature elimination were utilized to filter diagnostic TAAD markers, and then screened markers were validated by quantitative real-time PCR and another independent dataset. CIBERSORT was deployed to analyze and evaluate immune cell infiltration in TAAD tissues. Results Twenty-five DEGs were identified and narrowed down to three after screening. Finally, two genes, SLC11A1 and FGL2, were verified by another dataset and qRT-PCR. Function analysis revealed that SLC11A1 and FGL2 play significant roles in immune-inflammatory responses. Conclusion SLC11A1 and FGL2 are differently expressed in aortic dissection and may be involved in immune-inflammatory responses.https://doi.org/10.1186/s12872-023-03110-4Bioinformatics analysisThoracic acute aortic dissectionDifferentially expressed genesImmune-inflammatory responses |
| spellingShingle | Jun Luo Haoming Shi Haoyu Ran Cheng Zhang Qingchen Wu Yue Shao Identification of key biomarkers and immune infiltration in the thoracic acute aortic dissection by bioinformatics analysis BMC Cardiovascular Disorders Bioinformatics analysis Thoracic acute aortic dissection Differentially expressed genes Immune-inflammatory responses |
| title | Identification of key biomarkers and immune infiltration in the thoracic acute aortic dissection by bioinformatics analysis |
| title_full | Identification of key biomarkers and immune infiltration in the thoracic acute aortic dissection by bioinformatics analysis |
| title_fullStr | Identification of key biomarkers and immune infiltration in the thoracic acute aortic dissection by bioinformatics analysis |
| title_full_unstemmed | Identification of key biomarkers and immune infiltration in the thoracic acute aortic dissection by bioinformatics analysis |
| title_short | Identification of key biomarkers and immune infiltration in the thoracic acute aortic dissection by bioinformatics analysis |
| title_sort | identification of key biomarkers and immune infiltration in the thoracic acute aortic dissection by bioinformatics analysis |
| topic | Bioinformatics analysis Thoracic acute aortic dissection Differentially expressed genes Immune-inflammatory responses |
| url | https://doi.org/10.1186/s12872-023-03110-4 |
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