The purpose of this study was to recognise predictive biomarkers and explore the promising therapeutic targets of AD with depression. We confirmed a positive correlation between AD and depression through MR Analysis. Through WGCNA analysis, we identified 1569 genes containing two modules, which were most related to AD. In addition, 1629 depressive DEGs were also identified. In these genes, 84 genes were shared by both AD and depression, which were screened by the Degree algorithm, MCC algorithm, and four machine learning algorithms. Two genes (ITGB5 and SPCS1) were confirmed as predictive biomarkers with AUC > 0.7. Furthermore, the nomogram indicated that ITGB5 and SPCS1 are good biomarkers in diagnosing AD with depression. Four drugs targeted at ITGB5 were determined by the DGIdb website. In conclusion, we identified two predictive biomarkers for AD with depression, thus providing promising therapeutic targets for AD with depression.
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