Incorporating machine learning and PPI networks to identify mitochondrial fission-related immune markers in abdominal aortic aneurysms
© 2024 The Authors. Published by Elsevier Ltd..
Purpose: The aim of this study is to investigate abdominal aortic aneurysm (AAA), a disease characterised by inflammation and progressive vasodilatation, for novel gene-targeted therapeutic loci.
Methods: To do this, we used weighted co-expression network analysis (WGCNA) and differential gene analysis on samples from the GEO database. Additionally, we carried out enrichment analysis and determined that the blue module was of interest. Additionally, we performed an investigation of immune infiltration and discovered genes linked to immune evasion and mitochondrial fission. In order to screen for feature genes, we used two PPI network gene selection methods and five machine learning methods. This allowed us to identify the most featrue genes (MFGs). The expression of the MFGs in various cell subgroups was then evaluated by analysis of single cell samples from AAA. Additionally, we looked at the expression levels of the MFGs as well as the levels of inflammatory immune-related markers in cellular and animal models of AAA. Finally, we predicted potential drugs that could be targeted for the treatment of AAA.
Results: Our research identified 1249 up-regulated differential genes and 3653 down-regulated differential genes. Through WGCNA, we also discovered 44 genes in the blue module. By taking the point where several strategies for gene selection overlap, the MFG (ITGAL and SELL) was produced. We discovered through single cell research that the MFG were specifically expressed in T regulatory cells, NK cells, B lineage, and lymphocytes. In both animal and cellular models of AAA, the MFGs' mRNA levels rose.
Conclusion: We searched for the AAA novel targeted gene (ITGAL and SELL), which most likely function through lymphocytes of the B lineage, NK cells, T regulatory cells, and B lineage. This analysis gave AAA a brand-new goal to treat or prevent the disease.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
Heliyon - 10(2024), 7 vom: 15. Apr., Seite e27989 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Liu, Yi-Jiang [VerfasserIn] |
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| Links: |
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| Themen: |
Abdominal aortic aneurysm1 |
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| Anmerkungen: |
Date Revised 10.04.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.heliyon.2024.e27989 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM37080225X |
|---|
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| 245 | 1 | 0 | |a Incorporating machine learning and PPI networks to identify mitochondrial fission-related immune markers in abdominal aortic aneurysms |
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| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2024 The Authors. Published by Elsevier Ltd. | ||
| 520 | |a Purpose: The aim of this study is to investigate abdominal aortic aneurysm (AAA), a disease characterised by inflammation and progressive vasodilatation, for novel gene-targeted therapeutic loci | ||
| 520 | |a Methods: To do this, we used weighted co-expression network analysis (WGCNA) and differential gene analysis on samples from the GEO database. Additionally, we carried out enrichment analysis and determined that the blue module was of interest. Additionally, we performed an investigation of immune infiltration and discovered genes linked to immune evasion and mitochondrial fission. In order to screen for feature genes, we used two PPI network gene selection methods and five machine learning methods. This allowed us to identify the most featrue genes (MFGs). The expression of the MFGs in various cell subgroups was then evaluated by analysis of single cell samples from AAA. Additionally, we looked at the expression levels of the MFGs as well as the levels of inflammatory immune-related markers in cellular and animal models of AAA. Finally, we predicted potential drugs that could be targeted for the treatment of AAA | ||
| 520 | |a Results: Our research identified 1249 up-regulated differential genes and 3653 down-regulated differential genes. Through WGCNA, we also discovered 44 genes in the blue module. By taking the point where several strategies for gene selection overlap, the MFG (ITGAL and SELL) was produced. We discovered through single cell research that the MFG were specifically expressed in T regulatory cells, NK cells, B lineage, and lymphocytes. In both animal and cellular models of AAA, the MFGs' mRNA levels rose | ||
| 520 | |a Conclusion: We searched for the AAA novel targeted gene (ITGAL and SELL), which most likely function through lymphocytes of the B lineage, NK cells, T regulatory cells, and B lineage. This analysis gave AAA a brand-new goal to treat or prevent the disease | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Abdominal aortic aneurysm1 | |
| 650 | 4 | |a Bioinformatics6 | |
| 650 | 4 | |a Immune microenvironment2 | |
| 650 | 4 | |a Machine learning3 | |
| 650 | 4 | |a Mitochondrial fission4 | |
| 650 | 4 | |a Single cell analysis5 | |
| 700 | 1 | |a Li, Rui |e verfasserin |4 aut | |
| 700 | 1 | |a Xiao, Di |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Cui |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Yan-Lin |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Jia-Lin |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Zhan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Xin-Guo |e verfasserin |4 aut | |
| 700 | 1 | |a Shan, Zhong-Gui |e verfasserin |4 aut | |
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