Moreover, one bioinformatic analysis of WGCNA and three machine-learning strategies of LASSO, SVM-RFE, and RF analyses commonly identified that TXNIP, EGR1, and IGFBP5 could serve as biomarkers of AD, combining them as a tool gave rise to high AUCs of 0.954 and 0.938 in the two verification datasets (Figure 5C). This evidence concerns the gene TXNIP and Alzheimer disease.