Integrative analysis of multi-omics and machine learning highlighted an m6A-related mRNA signature as a robust AAA progression predictor

Abstract

ObjectiveAbdominal aortic aneurysm (AAA) is a life-threatening disease in vascular surgery with significant morbidity and mortality rates upon rupture. Despite surgical interventions, effective targeted drugs for non-surgical candidates are lacking. M6A methylation, a dynamic RNA modification, has been implicated in various diseases, but its role in AAA remains poorly understood. In this study, we aimed to explore the participation of M6A in the progression of AAA progression through multi-omics and machine learning. Approach and Resultswe conducted methylated RNA immunoprecipitation with next-generation sequencing (MeRIP-seq) to profile the m6A methylome in AAA tissues, identifying differentially methylated genes (DMGs). Integrating multi-omics data from RNA-sequencing (RNA-seq) in GEO databases, we developed a machine learning-based AAA m6A-related mRNA signature (AMRMS) to predict AAA dilation risk. The AMRMS demonstrated robust predictive performance in distinguishing AAA patients with large AAA and small AAA. Notably, the AMRMS highlighted FKBP11 as a key gene with a significant impact on the predicted model. Subsequent single-cell RNA sequencing (ScRNA-seq) revealed the pivotal role of FKBP11-positive plasma cells in AAA progression. ConclusionsOur study provides novel insights into the regulatory role of m6A modification in AAA pathogenesis, and further develop a promising AMRMS for risk evaluation in AAA patients. Furthermore, the identification of FKBP11 positive plasma cells as significant contributors to AAA progression opens new avenues for targeted therapeutic interventions.