Identification of ferroptosis-associated biomarkers in Stanford type A aortic dissection based on machine learning

Background: Stanford type A aortic dissection (STAAD) is a serious cardiovascular disease with a high mortality rate. Ferroptosis is closely associated with various diseases, including cardiovascular disease. However, the role of ferroptosis in the progression of STAAD remains unclear. Methods: Gene expression profiles of GSE52093, GSE98770, and GSE153434 datasets were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and support vector machine-recursive feature elimination (SVM-RFE) were performed to determine the ferroptosis-associated characteristic genes in STAAD. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic efficacy. Furthermore, immune cell infiltrations were analyzed using the CIBERSORT algorithm. Drug sensitivity analysis was conducted based on the CellMiner database. Results: A total of 65 differentially expressed ferroptosis-associated genes were screened. DAZAP1 and GABARAPL2 were identified as valuable diagnostic biomarkers for STAAD. A nomogram with high accuracy and reliability was constructed as a diagnostic tool for STAAD. Furthermore, immune infiltration analysis suggested that monocytes were higher in the STAAD group compared with the control group. DAZAP1 was positively correlated with monocytes, whereas GABARAPL2 was negatively correlated with monocytes. Pan-cancer analysis showed that DAZAP1 and GABARAPL2 were closely associated with the prognosis of various cancers. In addition, some antitumor drugs might be useful for the treatment of STAAD. Conclusion: DAZAP1 and GABARAPL2 might serve as potential diagnostic biomarkers for STAAD. Meanwhile, DAZAP1 and GABARAPL2 might be related to cancer and STAAD in terms of ferroptosis, which provides insights into developing new therapeutic approaches for STAAD.