Exploring the Efficacy and Target Genes of

objectiveTo unveil the efficacy and ferroptosis-related mechanisms of

methodsGene set variation analysis (GSVA) scores were used to investigate the relationship between ferroptosis and AD. Logistic regression with seven feature selections and a deep learning model were utilized to identify potential targets of AMK based on transcriptomic data from multiple tissues. A transcriptome-wide association study (TWAS), summary-data-based mendelian randomization (SMR), and mendelian randomization (MR) were utilized to validate the causal relationship between target genes and AD risk. A single-gene gene set enrichment analysis (GSEA) was employed to investigate the biological pathways associated with the target genes. Three molecular docking strategies and a molecular dynamics simulation were employed to verify the binding domains interacting with AMK. Furthermore, the anti-AD effects of AMK were validated in a zebrafish AD model by testing behavior responses, apoptosis, and the deposition of beta-amyloid (Aβ) in the brain. Ultimately, real-time qPCR was used to verify the ferroptosis-related targets, which was identified via multi-omics.

resultsFerroptosis is an important pathogenic mechanism of AD, as suggested by the GSVA scores. AMK may exert its anti-AD activity through targets genes identified in the brain (

conclusionsAMK significantly alleviated AD-like symptoms through the modulation of EGFR and HMOX1, which might reduce lipid peroxidation, thereby suppressing ferroptosis. This study provided evidence supporting the efficacy and therapeutic targets associated with ferroptosis in AMK-treated AD, which aid in the development of therapeutic interventions.