Level D· Scientific groundwork from lab and animal studiesAnimal StudyEurope PMCOpen access

Identification of a biomarker and immune infiltration in perivascular adipose tissue of abdominal aortic aneurysm

Wang X., He B., Deng Y., Liu J., Zhang Z., Sun W.

Animal Study, published in Front Physiol (2022) — summary generated from the PubMed abstract.

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Level D· Scientific groundwork from lab and animal studiesEvidence level of this study

Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.

  • Level A · Stronger Clinical Evidence
  • Level B · Emerging clinical evidence with positive signals
  • Level C · Early human research exploring benefits
  • Level D · Scientific groundwork from lab and animal studies
  • Emerging · Emerging topic under active research
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This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.

Study type
Animal Study
Journal
Front Physiol (2022)
Reported sample size
—
Source database
Europe PMC
PMID
36187757
PMCID
PMC9523244
DOI
10.3389/fphys.2022.977910
Citations
8

Abstract (original English)

Objective: Abdominal aortic aneurysm (AAA) refers to unusual permanent dilation of the abdominal aorta, and gradual AAA expansion can lead to fatal rupture. However, we lack clear understanding of the pathogenesis of this disease. The effect of perivascular adipose tissue (PVAT) on vascular functional status has attracted increasing attention. Here, we try to identify the potential mechanisms linking AAA and PVAT. Methods: We downloaded dataset GSE119717, including 30 dilated AAA PVAT samples and 30 non-dilated aorta PVAT samples from AAA cases, from Gene Expression Omnibus to identify differentially expressed genes (DEGs). We performed pathway enrichment analysis by Metascape, ClueGo and DAVID to annotate PVAT functional status according to the DEGs. A protein-protein interaction network, the support vector machine (SVM)-recursive feature elimination and the least absolute shrinkage and selection operator regression model were constructed to identify feature genes. Immune infiltration analysis was explored by CIBERSORT. And the correlation between feature gene and immune cells was also calculated. Finally, we used the angiotensin II (Ang II)-ApoE-/- mouse model of AAA to verify the effect of feature gene expression by confirming protein expression using immunohistochemistry and western blot. Results: We identified 22 DEGs, including 21 upregulated genes and 1 downregulated gen

What this study does not prove

  • • This study does not prove SVF is an approved treatment or a replacement for standard care.
  • • This is preclinical work; animal or laboratory results cannot be applied to humans.

Evidence level

Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.

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