Identification and Experimental Validation of NETosis-Mediated Abdominal Aortic Aneurysm Gene Signature Using Multi-omics, Machine Learning, and Mendelian Randomization
Abdominal aortic aneurysm (AAA) is a life-threatening disorder with limited therapeutic options. Neutrophil extracellular traps (NETs) are formed by a process known as "NETosis" that has been implicated in AAA pathogenesis, yet the roles and prognostic significance of NET-related genes in AAA remain poorly understood. This study aimed to identify key AAA- and NET-related genes (AAA-NETs-RGs), elucidate their potential mechanisms in contributing to AAA, and explore potential therapeutic compounds for AAA therapy. Through bioinformatics analysis of multiomics and machine learning, we identified six AAA-NETs-RGs: DUSP26, FCN1, MTHFD2, GPRC5C, SEMA4A, and CCR7, which exhibited strong diagnostic potential for predicting AAA progression, were significantly enriched in pathways related to cytokine-cytokine receptor interaction and chemokine signaling. Immune infiltration analysis revealed a causal association between AAA-NETs-RGs and immune cell infiltration. Cell-cell communication analysis indicated that AAA-NETs-RGs predominantly function in smooth muscle cells, B cells, T cells, and NK cells, primarily through cytokine and chemokine signaling. Gene profiling revealed that CCR7 and MTHFD2 exhibited the most significant upregulation in AAA patients compared to non-AAA controls, as well as in in vitro AAA models. Notably, genetic depletion of CCR7 and MTHFD2 strongly inhibited Ang II-induced phenotypic switching, functional impairment, and senescence in vascular smooth muscle cells (VSMCs). Based on AAA-NETs-RGs, molecular docking analysis combined with the Connectivity Map (CMap) database identified mirdametinib as a potential therapeutic agent for AAA. Mirdametinib effectively alleviated Ang II-induced phenotypic switching, biological dysfunction, and senescence. These findings provide valuable insights into understanding the pathophysiology of AAA and highlight promising therapeutic strategies targeting AAA-NETs-RGs.
基金:
Social Development Special Fund of Yunnan Province Science and Technology Department [202403AC100017]; Key Research and Development Project of the Tibet Autonomous Region [XZ202501ZY0044]; Natural Science Foundation of Shaanxi Province [23-JC-YB-705]; Graduate Student Research Innovation Project of Northwestern University 2024 [185]
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外文
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大类|2 区化学
小类|2 区化学:综合3 区药物化学3 区计算机:信息系统3 区计算机:跨学科应用
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Q1CHEMISTRY, MEDICINALQ1COMPUTER SCIENCE, INFORMATION SYSTEMSQ1COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONSQ2CHEMISTRY, MULTIDISCIPLINARY
第一作者机构:[1]Northwest Univ, Coll Life Sci, Minist Educ, Key Lab Resource Biol & Biotechnol Western China, Xian 710069, Shaanxi, Peoples R China
通讯作者:
通讯机构:[1]Northwest Univ, Coll Life Sci, Minist Educ, Key Lab Resource Biol & Biotechnol Western China, Xian 710069, Shaanxi, Peoples R China[3]Northwest Univ, Affiliated Hosp, Xian Hosp 3, Xian Key Lab Cardiovasc & Cerebrovasc Dis, Xian 710018, Peoples R China
推荐引用方式(GB/T 7714):
Wu Chengsong,Ren Yuanyuan,Li Yang,et al.Identification and Experimental Validation of NETosis-Mediated Abdominal Aortic Aneurysm Gene Signature Using Multi-omics, Machine Learning, and Mendelian Randomization[J].JOURNAL OF CHEMICAL INFORMATION AND MODELING.2025,doi:10.1021/acs.jcim.4c02318.
APA:
Wu, Chengsong,Ren, Yuanyuan,Li, Yang,Cui, Yue,Zhang, Liyao...&Xiong, Yuyan.(2025).Identification and Experimental Validation of NETosis-Mediated Abdominal Aortic Aneurysm Gene Signature Using Multi-omics, Machine Learning, and Mendelian Randomization.JOURNAL OF CHEMICAL INFORMATION AND MODELING,,
MLA:
Wu, Chengsong,et al."Identification and Experimental Validation of NETosis-Mediated Abdominal Aortic Aneurysm Gene Signature Using Multi-omics, Machine Learning, and Mendelian Randomization".JOURNAL OF CHEMICAL INFORMATION AND MODELING .(2025)