Novel anoikis-related diagnostic biomarkers for aortic dissection based on machine learning

Abstract

Abstract Aortic dissection (AD) is one of the most dangerous diseases of the cardiovascular system, which is characterized by acute onset and poor prognosis, while the pathogenesis of AD is still unclear and may affect or even delay the diagnosis of AD. Anchorage-dependent cell death (Anoikis) is a special mode of cell death, which is programmed cell death caused by normal cells after detachment from extracellular matrix (ECM) and has been widely studied in the field of oncology in recent years. In this study, we applied bioinformatics analysis, according to the results of research analysis and Gene Ontology (GO), as well as Kyoto Encyclopedia of Genes and Genomes (KEGG), finally found 3 anoikis-related genes (ARGs) based on machine learning. Among these, TP53 and TUBB3 were further verified by receiver operating characteristic (ROC), gene set enrichment analysis (GSEA), gene set variation analysis (GSVA)and other methods. We hypothesize ARGs may be involved in the pathogenesis of AD through pathways such as oxidative stress, inflammatory response, and ECM. Therefore, we conclude that these ARGs can be potential factors in determining the diagnosis of AD.