Abdominal aortic aneurysm (AAA) is typically asymptomatic but a devastating cardiovascular disorder, with overall mortality exceeding 80 % once it ruptures. Some patients with AAA may also have comorbid metabolic syndrome (MS), suggesting a potential common underlying pathogenesis. Mitochondrial dysfunction has been reported as a key factor contributing to the deterioration of both AAA and MS. However, the intricate interplay between metabolism and mitochondrial function, both contributing to the development of AAA, has not been thoroughly explored. In this study, we identified candidate genes related to mitochondrial function in AAA and MS. Subsequently, we developed a nomoscore model comprising hub genes (PINK1, ACSL1, CYP27A1, and SLC25A11), identified through the application of two machine learning algorithms, to predict AAA. We observed a marked disparity in immune infiltration profiles between high- and low-nomoscore groups. Furthermore, we confirmed a significant upregulation of the expression of the four hub genes in AAA tissues. Among these, ACSL1 showed relatively higher expression in LPS-treated RAW264.7 cell lines, while CYP27A1 exhibited a notable decrease. Moreover, SLC25A11 displayed a significant upregulation in AngII-treated VSMCs. Conversely, the expression level of PINK1 declined in LPS-stimulated RAW264.7 cell lines but significantly increased in AngII-treated VSMCs. In vivo experiments revealed that the activation of PINK1-mediated mitophagy inhibited the development of AAA in mice. In this current study, we have innovatively identified four mitochondrial function-related genes through integrated bioinformatic analysis. This discovery sheds light on the regulatory mechanisms and unveils promising therapeutic targets for the comorbidity of AAA and MS.
Keywords: Abdominal aortic aneurysm; Machine learning; Metabolic syndrome; Mitochondria; Nomoscore.
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