Introduction: Abdominal aortic aneurysms (AAA) are among the most lethal non-cancerous diseases. A comprehensive analysis of the AAA-related disease model has yet to be conducted.Methods: Weighted correlation network analysis (WGCNA) was performed for the AAA-related genes. Machine learning random forest and LASSO regression analysis were performed to develop the AAA-related score. Immune characteristics and epigenetic characteristics of the AAA-related score were explored.Results: Our study developed a reliable AAA-related disease model for predicting immunity and m1A/m5C/m6A/m7G epigenetic regulation.Discussion: The pathogenic roles of four model genes, UBE2K, TMEM230, VAMP7, and PUM2, in AAA, need further validation by in vitro and in vivo experiments.
基金：
National Natural Science Foundation of China [82070485]
第一作者单位：[1]Dalian Med Univ, Hosp 2, Dept Vasc Surg, Dalian, Peoples R China
通讯作者：
推荐引用方式(GB/T 7714)：
Tian Yu,Fu Shengjie,Zhang Nan,et al.The abdominal aortic aneurysm-related disease model based on machine learning predicts immunity and m1A/m5C/m6A/m7G epigenetic regulation[J].FRONTIERS IN GENETICS.2023,14:doi:10.3389/fgene.2023.1131957.
APA：
Tian, Yu,Fu, Shengjie,Zhang, Nan,Zhang, Hao&Li, Lei.(2023).The abdominal aortic aneurysm-related disease model based on machine learning predicts immunity and m1A/m5C/m6A/m7G epigenetic regulation.FRONTIERS IN GENETICS,14,
MLA：
Tian, Yu,et al."The abdominal aortic aneurysm-related disease model based on machine learning predicts immunity and m1A/m5C/m6A/m7G epigenetic regulation".FRONTIERS IN GENETICS 14.(2023)
