Identification and Experimental Validation of NETosis-Mediated Abdominal Aortic Aneurysm Gene Signature Using Multi-omics, Machine Learning, and Mendelian Randomization.
Chengsong Wu, Yuanyuan Ren, Yang Li, Yue Cui, Liyao Zhang, Pan Zhang, Xuejiao Zhang, Shangguang Kan, Chan Zhang, Yuyan Xiong
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引用次数: 0
Abstract
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.
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The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery.
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