基于机器学习的腹主动脉瘤相关疾病模型预测免疫及m1A/m5C/m6A/m7G表观遗传调控。
The abdominal aortic aneurysm-related disease model based on machine learning predicts immunity and m1A/m5C/m6A/m7G epigenetic regulation.
作者信息
Tian Yu, Fu Shengjie, Zhang Nan, Zhang Hao, Li Lei
机构信息
Department of Vascular Surgery, The Second Hospital of Dalian Medical University, Dalian, China.
Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China.
出版信息
Front Genet. 2023 Feb 23;14:1131957. doi: 10.3389/fgene.2023.1131957. eCollection 2023.
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. 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. Our study developed a reliable AAA-related disease model for predicting immunity and m1A/m5C/m6A/m7G epigenetic regulation. The pathogenic roles of four model genes, UBE2K, TMEM230, VAMP7, and PUM2, in AAA, need further validation by and experiments.
腹主动脉瘤（AAA）是最致命的非癌性疾病之一。尚未对与AAA相关的疾病模型进行全面分析。对与AAA相关的基因进行了加权基因共表达网络分析（WGCNA）。进行了机器学习随机森林和LASSO回归分析以建立与AAA相关的评分。探索了与AAA相关评分的免疫特征和表观遗传特征。我们的研究开发了一种可靠的与AAA相关的疾病模型，用于预测免疫和m1A/m5C/m6A/m7G表观遗传调控。四个模型基因UBE2K、TMEM230、VAMP7和PUM2在AAA中的致病作用，需要通过[具体实验名称1]和[具体实验名称2]实验进一步验证。
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