Fig. 1

Integrated study by statistical analysis using DESeq2 and machine learning methods to discover biomarker signatures that allow identification of MCI patients at high risk of AD. A During the 10-year longitudinal study, patients with MCI were classified as stable MCI (S-MCI, ≥ 4y) or progressive MCI (P-MCI, < 3y progressing to AD). Independently, 36 patients with AD were also selected. All subjects underwent whole transcriptome profiling of white blood cells via RNA-seq. B Schematic flow chart for identification of key genes derived from the RNA-seq data. Differential gene expression analysis was conducted by DESeq2 using the expected counts as inputs. For machine learning, count data are transformed into TPM values for feature selection and model construction. C, D Volcano plots showing significant upregulated and downregulated genes of P-MCI versus S-MCI based on DESeq2 analyses of males (C) or females (D). E Heatmap for 501 genes (204 upregulated and 297 downregulated) that differentially modulated in male P-MCI group. F Heatmap for 879 genes (440 upregulated and 439 downregulated) that differentially modulated in female P-MCI group. G A Venn diagram comparing significantly regulated genes found in male and female patients defined as P-MCI compared with S-MCI patients. H A Venn diagram comparing significantly dysregulated pathways found in male and female P-MCI patients compared with S-MCI patients. MCI, mild cognitive impairment; DEG, differentially expressed gene; IPA, ingenuity pathway analysis