Abstract
Ferroptosis, an iron-dependent form of regulated cell death, has been linked to the occurrence and progression of ischemic stroke (IS). This study aims to uncover the key ferroptosis-related genes in IS and their correlations with immunoinflammatory responses. Key differentially expressed ferroptosis-related genes were screened by integrating differential analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction analysis. Machine learning algorithms, LASSO regression, Random Forest, RGF, and LightGBM were employed to identify potential diagnostic biomarkers, and diagnostic model was then established. Oxygen-glucose deprivation/reoxygenation (OGD/R)-stimulated HT-22 cells were established to validate the expression of biomarkers by RT-qPCR and western blot. Fifteen key ferroptosis-related genes were identified by integrated analyses, and ATM, DUSP1, SRC, and STAT3 were further screened as biomarkers by four algorithms. The diagnostic model established based on these four biomarkers exhibited well predictive power for IS, with AUC over 0.8 in both training and validation sets. Expression of DUSP and STATS positively correlated with neuroinflammation pathway, and positively correlated with abundance of neutrophils and macrophages. SRC positively correlated with abundance of monocytes, whereas ATM positively correlated with CD8 T cells and resting memory CD4 T cells. Both mRNA and protein levels of DUSP1, SRC, and STATS3 were significantly enhanced, while the level of ATM was reduced in OGD/R-stimulated HT-22 cells than control cells. In conclusion, dysregulation of key ferroptosis-related genes, ATM, DUSP1, SRC, and STAT3 might be implicated in the progression of IS, which could be biomarkers or targets for the diagnosis and therapy of IS.
Keywords:
Ferroptosis; Immune response; Ischemic stroke; Machine learning; Neuroinflammation.