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. 2023 Jan 26:13:1064639.
doi: 10.3389/fneur.2022.1064639. eCollection 2022.

Diagnostic models and predictive drugs associated with cuproptosis hub genes in Alzheimer's disease

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Diagnostic models and predictive drugs associated with cuproptosis hub genes in Alzheimer's disease (VSports注册入口)

Erdong Zhang et al. Front Neurol. .

Abstract

Alzheimer's disease (AD) is a chronic neurodegenerative disease, and its underlying genes and treatments are unclear. Abnormalities in copper metabolism can prevent the clearance of β-amyloid peptides and promote the progression of AD pathogenesis. Therefore, the present study used a bioinformatics approach to perform an integrated analysis of the hub gene based on cuproptosis that can influence the diagnosis and treatment of AD. The gene expression profiles were obtained from the Gene Expression Omnibus database, including non-demented (ND) and AD samples. A total of 2,977 cuproptosis genes were retrieved from published articles. The seven hub genes associated with cuproptosis and AD were obtained from the differentially expressed genes and WGCNA in brain tissue from GSE33000 VSports手机版. The GO analysis demonstrated that these genes were involved in phosphoribosyl pyrophosphate, lipid, and glucose metabolism. By stepwise regression and logistic regression analysis, we screened four of the seven cuproptosis genes to construct a diagnostic model for AD, which was validated by GES15222, GS48350, and GSE5281. In addition, immune cell infiltration of samples was investigated for correlation with these hub genes. We identified six drugs targeting these seven cuproptosis genes in DrugBank. Hence, these cuproptosis gene signatures may be an important prognostic indicator for AD and may offer new insights into treatment options. .

Keywords: Alzheimer's disease; cuproptosis; diagnostic; drug; immune V体育安卓版. .

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The flowchart of the analyses.
Figure 2
Figure 2
Differentially expression gene between ND and AD samples. (A) Volcano plot showing the significant genes found by limma analysis. Red genes represent significantly high expression in AD, blue genes indicate significantly high expression in ND, and gray genes mean no changes. (B) The top 20 genes significantly expressed in AD or ND samples, showing by the heatmap.
Figure 3
Figure 3
The WGCNA results. (A) Analysis of the scale-free fit index for various soft-thresholding powers (β). (B) The mean connectivity for the soft-thresholding powers. (C) Clustering dendrograms of genes, with dissimilarity based on the topological overlap, together with assigned module colors. (D) Correlations between different modules and clinical traits. (E) Correlation of module membership and gene significance in the midnight blue module.
Figure 4
Figure 4
Hub genes and GO analysis. (A) The intersections of the DE, cuproptosis genes, and midnight blue genes. (B) Biological processes in which the seven hub genes were involved.
Figure 5
Figure 5
The hub genes expression in the ND and AD samples of GSE33000. (A) IFI30, (B) CLIC1, (C) LYZ, (D) PYGL, (E) PLA1A, (F) ALOX5AP, and (G) A4GALT.
Figure 6
Figure 6
GSEA revealed the enriched pathways of the hub genes. (A) A4GALT, (B) ALOX5AP, (C) CLIC1, (D) IFI30, (E) LYZ, (F) PLA1A, and (G) PYGL.
Figure 7
Figure 7
Receiver operating characteristic (ROC) curves and corresponding AUC values for the four expression cohorts. (A) GSE33000, (B) GSE48350, (C) GSE15222, and (D) GSE5281.
Figure 8
Figure 8
Immune cell infiltration between ND and AD samples. (A) The percentage of 22 immune cells in each sample. (B) Different levels of immune infiltrating cells between ND and AD samples.
Figure 9
Figure 9
Correlation between the hub genes and immune infiltrating cells. **p < 0.01, ***p < 0.001.

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