Long Term Outcomes and Potential Risk Factors for Endovascular Repair of Aortic Pseudoaneurysms in Vascular Behçet's Disease.

OBJECTIVE: This study aimed to analyse long term outcomes and risk factors for endovascular repair of aortic pseudoaneurysms in patients with vascular Behçet's disease (BD). METHODS: Medical records of 26 aortic vascular BD patients who underwent endovascular treatment at the vascular department of Peking Union Medical College Hospital between January 2002 and December 2021 were reviewed retrospectively. Erythrocyte sedimentation rate (ESR) and C reactive protein were used to assess BD activity. Computed tomography angiography (CTA) was obtained pre- and post-operatively for almost all patients. Univariable logistic regression analysis was used to analyse risk factors for endovascular repair, such as inflammatory indicators, drug usage, and stent graft parameters. RESULTS: The abdominal aorta (n = 17) was the most common site of 27 vascular BD pseudoaneurysms in this study. CTA also revealed one aortic arch pseudoaneurysm, seven descending thoracic aortic pseudoaneurysms, one thoraco-abdominal aortic pseudoaneurysm, and one pseudoaneurysm at the aortic bifurcation. Most of the pseudoaneurysms were treated with covered stent grafts. The technical success rate was 96% and no deaths occurred during hospital stay. The mean follow up was 5.8 ± 5.5 years and 31% (8/26) experienced post-operative complications. Overall one, three, and five year event free survival rates were 87%, 78%, and 74%, respectively. Univariable logistic regression analysis revealed that pre-operative ESR ≥ 16.0 mm/h (p = .040), pre-operative glucocorticoid (GC) use ≤ 11.5 days (p = .024), pre-operative immunosuppressant use ≤ 15.5 days (p = .028), and length of proximal landing zone ≤ 1.95 cm (p = .034) were associated with a worse prognoses following endovascular treatment. Proximal oversize ≥ 9.5% (p = .074) was also regarded as a risk factor, although not statistically significant. CONCLUSION: This study further confirmed the feasibility of endovascular repair for aortic vascular BD patients. Risk factors predicting poor prognoses included elevated pre-operative ESR, insufficient pre-operative GC use or immunosuppressant use, inadequate proximal landing zone, and larger proximal oversize percentage.

Identification of Novel Plasma Biomarkers for Abdominal Aortic Aneurysm by Protein Array Analysis.

Abdominal aortic aneurysm (AAA) is a potentially life-threatening disease that is common in the aging population. Currently, there are no approved diagnostic biomarkers or therapeutic drugs for AAA. We aimed to identify novel plasma biomarkers or potential therapeutic targets for AAA using a high-throughput protein array-based method. Proteomics expression profiles were investigated in plasma from AAA patients and healthy controls (HC) using 440-cytokine protein array analysis. Several promising biomarkers were further validated in independent cohorts using enzyme-linked immunosorbent assay (ELISA). Thirty-nine differentially expressed plasma proteins were identified between AAA and HC. Legumain (LGMN) was significantly higher in AAA patients and was validated in another large cohort. Additionally, "AAA without diabetes" (AAN) patients and "AAA complicated with type 2 diabetes mellitus" (AAM) patients had different cytokine expression patterns in their plasma, and nine plasma proteins were differentially expressed among the AAN, AAM, and HC subjects. Delta-like protein 1 (DLL1), receptor tyrosine-protein kinase erbB-3 (ERBB3), and dipeptidyl peptidase 4 (DPPIV) were significantly higher in AAM than in AAN. This study identified several promising plasma biomarkers of AAA. Their role as therapeutic targets for AAA warrants further investigation.

Key ferroptosis-related genes in abdominal aortic aneurysm formation and rupture as determined by combining bioinformatics techniques.

Objectives: Abdominal aortic aneurysm (AAA) is a cardiovascular disease with high mortality and pathogenesis closely related to various cell death types, e.g., autophagy, apoptosis and pyroptosis. However, the association between AAA and ferroptosis is unknown. Methods: GSE57691 and GSE98278 dataset were obtained from the Gene Expression Omnibus database, and a ferroptosis-related gene (FRG) set was downloaded from the FerrDb database. These data were normalized, and ferroptosis-related differentially expressed genes (FDEGs, AAA vs. normal samples) were identified using the limma package in R. FRGs expression was analyzed by Gene Set Expression Analysis (GSEA), and FDEGs were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) pathway enrichment analyses using the clusterProfiler package in R and ClueGO in Cytoscape. Protein-protein interaction networks were assembled using Cytoscape, and crucial FDEGs were identified using CytoHubba. Critical FDEG transcription factors (TFs) were predicted with iRegulon. FDEGs were verified in GSE98278 set, and key FDEGs in AAA (compared with normal samples) and ruptured AAA (RAAA; compared with AAA samples) were identified. Ferroptosis-related immune cell infiltration and correlations with key genes were analyzed by CIBERSORT. Key FEDGs were reverified in Ang II-induced AAA models of ApoE-/- and CD57B/6J mice by immunofluorescence assay. Results: In AAA and normal samples, 40 FDEGs were identified, and the expression of suppressive FRGs was significantly downregulated with GSEA. For FDEGs, the GO terms were response to oxidative stress and cellular response to external stimulus, and the KEGG pathways were the TNF and NOD-like receptor signaling pathways. IL6, ALB, CAV1, PTGS2, NOX4, PRDX6, GPX4, HSPA5, HSPB1, and NCF2 were the most enriched genes in the crucial gene cluster. CEBPG, NFAT5, SOX10, GTF2IRD1, STAT1, and RELA were potential TFs affecting these crucial genes. Ferroptosis-related immune cells involved in AAA formation were CD8+ T, naive CD4+ T, and regulatory T cells (Tregs); M0 and M2 macrophages; and eosinophils. Tregs were also involved in RAAA. GPX4, SLC2A1, and PEBP1 expression was downregulated in both the RAAA and AAA samples. GPX4 and PEBP1 were more important in AAA because they influenced ferroptosis-related immune cell infiltration, and SLC2A1 was more important in RAAA. Conclusions: This is the first study to show that ferroptosis is crucial to AAA/RAAA formation. The TNF and NOD-like signaling pathways and ferroptosis-related immune cell infiltration play key roles in AAA/RAAA. GPX4 is a key ferroptosis-related gene in AAA. Ferroptosis and related genes might be promising targets in the treatment of AAA/RAAA.

The molecular mechanism of Ang II induced-AAA models based on proteomics analysis in ApoE-/- and CD57BL/6J mice.

Apolipoprotein knockout (ApoE-/-) and CD57BL/6J mouse models of angiotensin II (Ang II)-induced abdominal aortic aneurysm (AAA) are commonly used in AAA research. However, the similarities and differences in the molecular mechanisms of AAA in these two genotypes have not been reported. In our study, we analyzed proteomics data from ApoE-/- and CD57BL/6J mouse models of Ang II-induced AAA and control mice by LC-MS/MS. Gene set enrichment analysis (GSEA) of differentially abundance proteins (DAPs) in the ApoE-/- or CD57BL/6J mouse groups was performed in R software, and infiltration of immune cells in groups was assessed. DAP that showed the same trend in abundance in ApoE-/- and CD57BL/6J mice (S-DAP) were identified and subjected to GO enrichment, KEGG pathway, and connectivity map (CMap) analyses. The protein-protein interaction (PPI) network of the S-DAP was drawn, the key S-DAP were identified by MCODE, and the transcription factors (TFs) of crucial S-DAP were predicted by iRegulon in Cytoscape. Male ApoE-/- and CD57BL/6J mouse models of Ang II-induced AAA are commonly used in AAA research, and extracellular matrix organization is associated with AAA in both of these models. However, there are some differences between the mechanisms underlying AAA in these two genotypes, and these differences need to be considered when studying AAA and selecting models. SIGNIFICANCE: Our research provided the first insight into the similarity and differential mechanisms of Ang II infused AAA models using ApoE-/- and CD57BL/6J mice. This study might provide the some advises for the selection of Ang II infused AAA models for further AAA researches.

Ageing- and AAA-associated differentially expressed proteins identified by proteomic analysis in mice.

Background: Abdominal aortic aneurysm (AAA) is a disease of high prevalence in old age, and its incidence gradually increases with increasing age. There were few studies about differences in the circulatory system in the incidence of AAA, mainly because younger patients with AAA are fewer and more comorbid nonatherosclerotic factors. Method: We induced AAA in ApoE-/- male mice of different ages (10 or 24 weeks) and obtained plasma samples. After the top 14 most abundant proteins were detected, the plasma was analyzed by a proteomic study using the data-dependent acquisition (DDA) technique. The proteomic results were compared between different groups to identify age-related differentially expressed proteins (DEPs) in the circulation that contribute to AAA formation. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI) network analyses were performed by R software. The top 10 proteins were determined with the MCC method of Cytoscape, and transcription factor (TF) prediction of the DEPs was performed with iRegulon (Cytoscape). Results: The aortic diameter fold increase was higher in the aged group than in the youth group (p < 0.01). Overall, 92 DEPs related to age and involved in AAA formation were identified. GO analysis of the DEPs showed enrichment of the terms wounding healing, response to oxidative stress, regulation of body fluid levels, ribose phosphate metabolic process, and blood coagulation. The KEGG pathway analysis showed enrichment of the terms platelet activation, complement and coagulation cascades, glycolysis/gluconeogenesis, carbon metabolism, biosynthesis of amino acids, and ECM-receptor interaction. The top 10 proteins were Tpi1, Eno1, Prdx1, Ppia, Prdx6, Vwf, Prdx2, Fga, Fgg, and Fgb, and the predicted TFs of these proteins were Nfe2, Srf, Epas1, Tbp, and Hoxc8. Conclusion: The identified proteins related to age and involved in AAA formation were associated with the response to oxidative stress, coagulation and platelet activation, and complement and inflammation pathways, and the TFs of these proteins might be potential targets for AAA treatments. Further experimental and biological studies are needed to elucidate the role of these age-associated and AAA-related proteins in the progression of AAA.