The abdominal aortic aneurysm-related disease model based on machine learning predicts immunity and m1A/m5C/m6A/m7G epigenetic regulation

Yu Tian; Shengjie Fu; Nan Zhang; Hao Zhang; Lei Li

doi:10.3389/fgene.2023.1131957

The abdominal aortic aneurysm-related disease model based on machine learning predicts immunity and m1A/m5C/m6A/m7G epigenetic regulation

Front Genet. 2023 Feb 23:14:1131957. doi: 10.3389/fgene.2023.1131957. eCollection 2023.

Authors

Yu Tian¹, Shengjie Fu¹, Nan Zhang², Hao Zhang³, Lei Li¹

Affiliations

¹ Department of Vascular Surgery, The Second Hospital of Dalian Medical University, Dalian, China.
² Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China.
³ Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China.

Abstract

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.

Keywords: aortic aneurysm; disease model; immunity; m1A/m5C/m6A/m7G; machine learning.

Grants and funding

National Natural Science Foundation of China (82070485).