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A role of inflammaging in aortic aneurysm: new insights from bioinformatics analysis

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 ar...

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Autores principales: Wang, Shilin, Liu, Hao, Yang, Peiwen, Wang, Zhiwen, Ye, Ping, Xia, Jiahong, Chen, Shu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10511768/
https://www.ncbi.nlm.nih.gov/pubmed/37744379
http://dx.doi.org/10.3389/fimmu.2023.1260688
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author Wang, Shilin
Liu, Hao
Yang, Peiwen
Wang, Zhiwen
Ye, Ping
Xia, Jiahong
Chen, Shu
author_facet Wang, Shilin
Liu, Hao
Yang, Peiwen
Wang, Zhiwen
Ye, Ping
Xia, Jiahong
Chen, Shu
author_sort Wang, Shilin
collection PubMed
description 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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spelling pubmed-105117682023-09-22 A role of inflammaging in aortic aneurysm: new insights from bioinformatics analysis Wang, Shilin Liu, Hao Yang, Peiwen Wang, Zhiwen Ye, Ping Xia, Jiahong Chen, Shu Front Immunol Immunology 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. Frontiers Media S.A. 2023-09-06 /pmc/articles/PMC10511768/ /pubmed/37744379 http://dx.doi.org/10.3389/fimmu.2023.1260688 Text en Copyright © 2023 Wang, Liu, Yang, Wang, Ye, Xia and Chen https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Wang, Shilin
Liu, Hao
Yang, Peiwen
Wang, Zhiwen
Ye, Ping
Xia, Jiahong
Chen, Shu
A role of inflammaging in aortic aneurysm: new insights from bioinformatics analysis
title A role of inflammaging in aortic aneurysm: new insights from bioinformatics analysis
title_full A role of inflammaging in aortic aneurysm: new insights from bioinformatics analysis
title_fullStr A role of inflammaging in aortic aneurysm: new insights from bioinformatics analysis
title_full_unstemmed A role of inflammaging in aortic aneurysm: new insights from bioinformatics analysis
title_short A role of inflammaging in aortic aneurysm: new insights from bioinformatics analysis
title_sort role of inflammaging in aortic aneurysm: new insights from bioinformatics analysis
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10511768/
https://www.ncbi.nlm.nih.gov/pubmed/37744379
http://dx.doi.org/10.3389/fimmu.2023.1260688
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