Oxidized low-density lipoprotein (ox-LDL) induces endothelial cell apoptosis and dysfunction. Statins are drugs that are clinically used to lower serum cholesterol levels, and they have been shown to exert vascular protective effects. In the present study, human umbilical vein endothelial cells were transfected with scramble control siRNA or siRNA specific for glutathione peroxidase (GPx)4 or cystine-glutamate antiporter (xCT). MTT, Matrigel and Transwell assays were used to evaluate cell proliferation, tube formation and migration, respectively. The levels of TNF-α, IL-α, 4-hydroxynonenal, GPx4 and xCT expression were detected by western blot analysis. It was demonstrated that ox-LDL promoted cytokine production and reduced the proliferation, migration and angiogenesis of endothelial cells. It was also observed that ox-LDL decreased GPx4 and xCT expression and induced ferroptosis. Furthermore, the inhibition of ferroptosis by deferoxamine mesylate attenuated ox-LDL-induced endothelial cell dysfunction and restored ox-LDL-decreased GPx4 and xCT expression. Consistent with these results, GPx4 and xCT knockdown by siRNA transfection aggravated ox-LDL-induced endothelial cell dysfunction and inhibition of proliferation. To the best of our knowledge, the present study was the first to discover that fluvastatin may protect endothelial cells from ox-LDL-induced ferroptosis and dysfunction. Furthermore, knockdown of GPx4 and xCT expression blunted the protective effects of fluvastatin on ox-LDL-treated endothelial cells. These data indicated a novel function of fluvastatin in the protection of endothelial cells from ox-LDL-induced ferroptosis, the mechanism of which involves the regulation of GPx4 and xCT.

Objectives. Abdominal aortic aneurysm (AAA), a disease with high mortality, is limited by the current diagnostic methods in the early screening. This study aimed to screen novel and significant biomarkers and construct a diagnostic model for AAA by using a novel machine learning method, i.e., an ensemble of the random forest (RF) algorithm and artificial neural network (ANN). Methods and Results. Through a search of the Gene Expression Omnibus (GEO) database, two large-sample gene expression datasets (GSE57691 and GSE47472) were downloaded and preprocessed. Differentially expressed genes (DEGs) in GSE57691 were identified by R software, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Essential metabolic pathways related to positive regulation of cell death and NAD binding were found. Then, RF was used to identify key genes from the DEGs, and an AAA diagnostic model was established by ANN. A transcription factor (TF) regulatory network of key genes related to angiogenesis and endothelial migration was constructed. Finally, a validation dataset was used to validate the model and the area under the receiver operating characteristic curve (AUC) value was high. Conclusion. Potential AAA-associated gene biomarkers were identified by RF, and a novel early diagnostic model of AAA was established by ANN. The AUC indicated that the diagnostic model had a highly satisfactory diagnostic performance. In conclusion, this study will provide a promising theoretical basis for further clinical and experimental studies.

Background Abdominal aortic aneurysm (AAA), a disease with high mortality, is limited by the current diagnostic methods in the early screening. This study aimed to construct a diagnostic model for AAA by using a novel machine learning method, i.e., an ensemble of the random forest (RF) algorithm and an artificial neural network (ANN) (RF-ANN), to identify potential AAA-associated genetic biomarkers. Methods Through a search of the Gene Expression Omnibus (GEO) database, two large-sample gene expression datasets (GSE57691 and GSE47472) were identified and downloaded. The differentially expressed genes (DEGs) between the AAA and normal control samples were identified, followed by Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Then, RF-ANN was used to identify the key genes from the DEGs, and an AAA diagnostic model was established. Finally, the diagnostic performance of the model was assessed using the area under the receiver operating characteristic curve (AUC) with GSE47472 as a test dataset. Results Using GSE57691, we obtained 2486 DEGs, 52 biological process annotations, 17 cellular component annotations, 17 molecular function annotations, and 13 significantly enriched KEGG pathways. Out of these DEGs, we further identified 74 key candidate feature genes by using the RF machine learning algorithm. The weight of each key gene was calculated by the ANN with GSE57691 as a training dataset to construct an AAA diagnostic model. A transcription factor (TF) regulatory network of key genes was constructed. Finally, GSE47472 was used to validate the model. The AUC value was 0.786, indicating that the model had a highly satisfactory diagnostic performance. Conclusion Potential AAA-associated gene biomarkers were identified, and a diagnostic model of AAA was established. This study may provide a valuable reference for early clinical diagnosis and the search for therapeutic targets of AAA.