ArticleFrontiers in immunology2023
A role of inflammaging in aortic aneurysm: new insights from bioinformatics analysis.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Unlocking the power of empagliflozin: Rescuing inflammation in hyperglycaemia-exposed human cardiomyocytes through comprehensive multi-level analysis.European journal of heart failure · 2025Pooled it
- Expression and regulation network of BLNK in CP/CPPS in animal, cell model and clinical samples.Inflammation · 2025Article
- Differential Expression Analyses on Human Aortic Tissue Reveal Novel Genes and Pathways Associated With Abdominal Aortic Aneurysm Onset and Progression.Journal of the American Heart Association · 2024Article
- The Predictive Role of Indices Indirectly Indicating Hyperlipidemia and Inflammation in Ascending Aortic Aneurysm.Cureus · 2024Article
- Immunophenotypes in psychosis: is it a premature inflamm-aging disorder?Molecular psychiatry · 2024Review
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7 authors.
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Abstract
Introduction: Aortic aneurysms (AA) are prevalent worldwide with a notable absence of drug therapies. Thus, identifying potential drug targets is of utmost importance. AA often presents in the elderly, coupled with consistently raised serum inflammatory markers. Given that ageing and inflammation are pivotal processes linked to the evolution of AA, we have identified key genes involved in the inflammaging process of AA development through various bioinformatics methods, thereby providing potential molecular targets for further investigation. Methods: The transcriptome data of AA was procured from the datasets GSE140947, GSE7084, and GSE47472, sourced from the NCBI GEO database, whilst gene data of ageing and inflammation were obtained from the GeneCards Database. To identify key genes, differentially expressed analysis using the "Limma" package and WGCNA were implemented. Protein-protein intersection (PPI) analysis and machine learning (ML) algorithms were employed for the screening of potential biomarkers, followed by an assessment of the diagnostic value. Following the acquisition of the hub inflammaging and AA-related differentially expressed genes (IADEGs), the TFs-mRNAs-miRNAs regulatory network was established. The CIBERSORT algorithm was utilized to investigate immune cell infiltration in AA. The correlation of hub IADEGs with infiltrating immunocytes was also evaluated. Lastly, wet laboratory experiments were carried out to confirm the expression of hub IADEGs. Results: 342 and 715 AA-related DEGs (ADEGs) recognized from GSE140947 and GSE7084 datasets were procured by intersecting the results of "Limma" and WGCNA analyses. After 83 IADEGs were obtained, PPI analysis and ML algorithms pinpointed 7 and 5 hub IADEGs candidates respectively, and 6 of them demonstrated a high diagnostic value. Immune cell infiltration outcomes unveiled immune dysregulation in AA. In the wet laboratory experiments, 3 hub IADEGs, including BLNK, HLA-DRA, and HLA-DQB1, finally exhibited an expression trend in line with the bioinformatics analysis result. Discussion: Our research identified three genes - BLNK, HLA-DRA, and HLA-DQB1- that play a significant role in promoting the development of AA through inflammaging, providing novel insights into the future understanding and therapeutic intervention of AA.
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