Identification of ARHGAP9 as a Key Diagnostic Marker for Abdominal Aortic Aneurysm by Multiomics and Experimental Validation

Abstract

Abdominal aortic aneurysm (AAA) is a serious vascular condition that significantly endangers the lives of patients. Although there have been improvements in early detection and treatment methods, considerable challenges persist regarding the timely identification and evaluation of risk associated with this disease. Therefore, there is an immediate requirement for novel biomarkers that can enhance the early diagnosis and risk evaluation of AAA, thus allowing for more accurate and individualized medical interventions. In this study, we identified key diagnostic markers for AAA using various machine learning algorithms, and we explored the functions of these genes in AAA through gene enrichment analysis. A diagnostic model for AAA was constructed based on multiple machine learning algorithms, with the random forest algorithm highlighting the central role of ARHGAP9. In vitro experiments confirmed the influence of ARHGAP9 on vascular smooth muscle cells (VSMCs). Our findings indicate that the key genes identified are associated with the immune microenvironment and metabolism in AAA samples. The validated diagnostic model exhibited excellent predictive performance. Knockdown of ARHGAP9 significantly inhibited the proliferative capacity of VSMCs. In conclusion, our results suggest that ARHGAP9 may serve as a diagnostic and therapeutic marker for AAA.