Identifying PTAFR as a hub gene in atherosclerosis: implications for NETosis and disease progression

• Published in: Human genomics Vol. 18; no. 1; pp. 139 - 14
• Main Authors: Ye, Chaowen, Zhao, Yunli, Yu, Wei, Huang, Rongzhong, Hu, Tianyang
• Format: Journal Article
• Language: English
• Published: England BioMed Central 21.12.2024; BMC
• Subjects: Animals; Arteriosclerosis; Atherosclerosis; Atherosclerosis - genetics; Atherosclerosis - pathology; Bioinformatics; Biomarkers - metabolism; Cardiovascular disease; Cardiovascular diseases; Computational Biology - methods; Coronary vessels; Datasets; Disease Models, Animal; Disease Progression; Extracellular Traps - genetics; Extracellular Traps - metabolism; Flow cytometry; Gene expression; Gene Expression Profiling; Gene set enrichment analysis; Genetic engineering; Heart attacks; Humans; Immunofluorescence; Immunohistochemistry; Ischemia; Leukocytes (neutrophilic); Machine Learning; Macrophages; Male; Mice; Mice, Inbred C57BL; NETosis; NETs; Neutrophils; Neutrophils - metabolism; Neutrophils - pathology; Patients; Prognosis; PTAFR; siRNA; Smooth muscle; Support vector machines; Vein & artery diseases; NETs; NETosis; Cardiovascular diseases; Atherosclerosis; PTAFR
• ISSN: 1479-7364; 1473-9542; 1479-7364
• DOI: 10.1186/s40246-024-00708-3

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. 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. 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. 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.