Integration of bulk/scRNA-seq and multiple machine learning algorithms identifies PIM1 as a biomarker associated with cuproptosis and ferroptosis in abdominal aortic aneurysm

Background Abdominal aortic aneurysm (AAA) is a serious life-threatening vascular disease, and its ferroptosis/cuproptosis markers have not yet been characterized. This study was aiming to identify markers associated with ferroptosis/cuproptosis in AAA by bioinformatics analysis combined with machine learning models and to perform experimental validation. Methods This study used three scRNA-seq datasets from different mouse models and a human PBMC bulk RNA-seq dataset. Candidate genes were identified by integrated analysis of scRNA-seq, cell communication analysis, monocle pseudo-time analysis, and hdWGCNA analysis. Four machine learning algorithms, LASSO, REF, RF and SVM, were used to construct a prediction model for the PBMC dataset, the above results were comprehensively analyzed, and the targets were confirmed by RT-qPCR. Results scRNA-seq analysis showed Mo/MF as the most sensitive cell type to AAA, and 34 cuproptosis associated ferroptosis genes were obtained. Pseudo-time series analysis, hdWGCNA and machine learning prediction model construction were performed on these genes. Subsequent comparison of the above results showed that only PIM1 appeared in all algorithms. RT-qPCR and western blot results were consistent with sequencing results, showing that PIM1 was significantly upregulated in AAA. Conclusion In a conclusion, PIM1 as a novel biomarker associated with cuproptosis/ferroptosis in AAA was highlighted.