Background: Atherosclerosis (AS) is a major cause of cardiovascular diseases and neutrophil extracellular traps (NETs) may be actively involved in the development of atherosclerosis. Identifying key biomarkers in this process is essential for developing targeted treatments for AS.
Methods: We performed bioinformatics analysis using a NETosis-related gene (NRGs) set and three AS datasets (GSE100927, GSE21545, and GSE159677). Differential expression analysis and machine learning techniques (random forest and SVM-RFE) were used to screen for key NRGs. Functional enrichment analysis was conducted using GO and KEGG pathways. The expression and role of PTAFR and NETs in the mouse AS model were validated through histology, immunofluorescence, flow cytometry, and Western blot analysis. The regulatory relationship between PTAFR and NETs was confirmed by siRNA and antagonist intervention targeting PTAFR.
Results: We identified 24 differentially expressed NRGs in AS. Random Forest and SVM-RFE analyses highlighted PTAFR as a key gene. Prognostic analysis revealed PTAFR significantly impacts ischemic events in AS patients. WB and immunofluorescence confirmed increased levels of NETs and PTAFR in the mouse AS model. Single-cell analysis, flow cytometry, and immunofluorescence revealed that PTAFR is primarily distributed in macrophages and neutrophils. Cellular experiments further confirmed that PTAFR regulates NETs formation.
Conclusion: PTAFR is an important regulatory factor for NET formation in AS, influencing the progression and prognosis of atherosclerosis. Targeting PTAFR may provide new therapeutic strategies for AS.
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http://dx.doi.org/10.1186/s40246-024-00708-3 | DOI Listing |
Hum Genomics
December 2024
Precision Medicine Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Background: Atherosclerosis (AS) is a major cause of cardiovascular diseases and neutrophil extracellular traps (NETs) may be actively involved in the development of atherosclerosis. Identifying key biomarkers in this process is essential for developing targeted treatments for AS.
Methods: We performed bioinformatics analysis using a NETosis-related gene (NRGs) set and three AS datasets (GSE100927, GSE21545, and GSE159677).
J Clin Med Res
December 2024
Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
In Vivo
October 2024
Department of Medicine, Pharmacogenetics Laboratory, Drug Research and Development Center (NPDM), Federal University of Ceará, Fortaleza, CE, Brazil;
J Neurochem
October 2024
Bernal Institute, University of Limerick, Limerick, Ireland.
Lipids Health Dis
April 2024
Department of Biological Sciences, University of Limerick, Limerick, V94PH61, Ireland.
Background: Pro-inflammatory processes triggered by the accumulation of extracellular amyloid beta (Aβ) peptides are a well-described pathology in Alzheimer's disease (AD). Activated astrocytes surrounding Aβ plaques contribute to inflammation by secreting proinflammatory factors. While astrocytes may phagocytize Aβ and contribute to Aβ clearance, reactive astrocytes may also increase Aβ production.
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