AUTHOR=Tian Yu , Fu Shengjie , Zhang Nan , Zhang Hao , Li Lei 

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

JOURNAL=Frontiers in Genetics

VOLUME=14

YEAR=2023

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2023.1131957

DOI=10.3389/fgene.2023.1131957

ISSN=1664-8021

ABSTRACT=<p><bold>Introduction:</bold> 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.</p><p><bold>Methods:</bold> 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.</p><p><bold>Results:</bold> Our study developed a reliable AAA-related disease model for predicting immunity and m1A/m5C/m6A/m7G epigenetic regulation.</p><p><bold>Discussion:</bold> The pathogenic roles of four model genes, UBE2K, TMEM230, VAMP7, and PUM2, in AAA, need further validation by <italic>in vitro</italic> and <italic>in vivo</italic> experiments.</p>