Novel insights from comprehensive analysis: The role of cuproptosis and peripheral immune infiltration in Alzheimer's disease

Abstract

Background: Cuproptosis is increasingly recognized as an essential factor in the pathological process of Alzheimer's disease (AD). However, the specific role of cuproptosis-related genes in AD remains poorly understood.

Methods: Our first step was to obtain gene expression data from the GEO database and identify differentially expressed cuproptosis-associated genes (DECAGs) in AD. GO, KEGG, and GSEA analyses were then conducted on these genes. Subsequently, we attempted to classify AD patients by unsupervised clustering. Then, four machine-learning models were used to screen hub-genes from the DECAGs. We also explored the immune features of these genes and predicted target drugs. Molecular docking analysis was then performed on the predicted drugs and their corresponding hub-gene related proteins. Candidate markers were then validated by single-cell analysis and intracellular communication was investigated in a GEO scRNA-seq dataset. Lastly, we examined the expression levels of the hub-genes in peripheral blood cells using real-time quantitative PCR.

Results: 19 DECAGs were found in AD and the key biological processes and molecular functions associated with AD were further determined. Two subtypes of peripheral blood cells showed significant alternations in AD: Cluster1 and Cluster2. Five hub-genes including FDX1, GLS, PDK1, MAP2K1, and SOD1 were then screened out from the machine-learning study. All of the five hub-genes were significantly correlated with various immunocytes. We discovered compounds targeting hub-gene related proteins and forecasted multiple strong hydrogen bonding interactions between the picked predicted drugs and the target proteins by molecular docking analysis. Subsequently, in the single-cell analysis of AD peripheral blood, all hub-genes except SOD1 were found to be up-regulated in B cells, NK cells, and CD4+ T cells, possibly acting on the MIF pathway. Finally, we discovered that the levels of PDK1 expression in AD patients were remarkably upregulated, while FDX1 and GLS were significantly decreased using qPCR.

Conclusion: This study examined changes in intercellular communication between immune cells in the peripheral blood and identified five novel feature genes associated with cuproptosis in AD patients. These results facilitated a deeper understanding of the molecular mechanisms of AD and suggested novel therapeutic targets.

Fig 1. The basic flowchart of analysis in this study.

Fig 2. The expression profiles of 19 DECAGs between AD patients and normal controls.

(A) The boxplot showed the expression characteristics of 46 CAGs in AD. (B) The heatmap of DECAGs between AD and normal samples. (C) Ring diagram of the gene relationship network of 19 DECAGs, with red and green representing positive and negative correlations, respectively. (D) Correlation coefficient analysis of 16DECRG, expressed as the area of pie charts.

Fig 3. Functions and pathways enrichment analysis of the 19 DECAGs in AD.

(A) Networks of GO and KEGG enriched terms using the Metascape database. (B) P-value-based bar charts of biological processes. (C) The circle plot of GO analysis results. (D) Circle diagram of the KEGG pathway results. (E) The constructed PPI network using the 19 DECAGs with interaction score set at >0.900. (F) The significant enriched disease terms of the 19 DECAGs.

Fig 4. The immune-infiltrating features associated with cuproptosis in AD.

(A) Relative abundance of 22 immune-infiltrating cells in AD and normal samples. (B) The boxplot showing variations in immune microenvironment between AD and normal samples. (C) Correlation analysis between the 22 immune-infiltrating cells.

Fig 5. Consensus clustering analysis based on the DECAGs.

(A) The clustering matrix is most stable when k = 2. (B) Cumulative distribution function (CDF) curves when k = 2-9. (C) Delta area curves when k = 2-9. (D) Principal component analysis of the samples in two clusters. (E) The expression heatmap of 19 DECAGs between the two clusters. (F) The boxplot showing differences in immune-infiltrating cells between the two clusters.

Fig 6. Gene set variation analysis between the two clusters.

(A) Variations of GO analysis between Cluster1 and Cluster2. (B) Differences in KEGG pathways of the two clusters. (C) Differences in immune-related pathways between the two clusters.

Fig 7. Establishment of the cuproptosis risk model by integrating multiple analyses.

(A) Area under the ROC curves of each machine learning model. (B) The boxplot of residuals in each machine learning model. (C) Reverse cumulative distribution of residuals for each machine learning model. (D) The significant feature genes created for the GLM, RF, SVM, and XGB models. (E) A nomogram created for the five hub genes in GLM model. (F) Decision curve analysis displaying the predictive ability of the nomogram. (G) Calibration curves showing biases between ideal probability and actual probability. The AUC values were 0.805 and 0.605 for GSE33000 and GSE122063, respectively.

Fig 8. Gene set enrichment analysis for the five genes in AD patients.

Gene ontology analysis revealed biological functions in the high and low expressions of FDX1 (A), MAP2K1 (B), PDK1 (C), GLS (D), and SOD1 (E).

Fig 9. Characterizations of hub-genes related immune infiltration, drugs, and genes.

(A) Analysis of the relationship between hub-genes and immunocytes, as well as among different immunocytes. (B) The interacting network of the hub-genes and targeted drugs. (C) The predicting targeted genes using the GeneMANIA database.

Fig 10. Molecular docking in each hub-gene related protein with the picked predicted drugs.

(A) SOD1 and Tetracycline; (B) MAP2K1 and AZD8330; (C) FDX1 and LANRAPLENIB; (D) PDK1 and Dichloroacetic Acid.

Fig 11. Cell type classification and analysis of the hub DECAGs at the single-cell level.

(A) A t-SNE plot illustrating cell clustering. (B) t-SNE plots demonstrating typical cell surface markers defining CD4+  T cells (CD4, IL7R), NK cells (NKG7), B cells (CD79A), CD8+  T cells (CD8B), and monocytes (CD68). (C) A t-SNE plot visualizing grouped cell annotation. (D) t-SNE plots demonstrating the distribution of the picked hub genes and their co-expression in cells. (E) A bubble plot demonstrating the selected hub DECAGs for each cell type in AD.

Fig 12. Intercellular communication in the peripheral immune cells of AD patients.

(A) Circle plots assessing the cell-cell network communications. The thickness of the edges represents the number (left) or the weight (right) of interactions among peripheral immune cell types. (B) Circle plots demonstrating the cell-cell interaction weights among each peripheral immune cell separately. (C) Probability of communication between ligands and receptors in peripheral immune cell populations. Signal enhancement indicates an increase in the communication potential of these signals. (D) A heatmap illustrating the roles of each type of peripheral immune cells in the MIF signaling pathway. (E) Hierarchical analysis of the intercellular communication network of the MIF signaling pathway. The same colors represent the same cells. (F) Circle plots demonstrating cellular communication at the level of the MIF pathway and its corresponding ligand-receptor pairs level. (G) Chord plots demonstrating cellular communication in the MIF signaling pathway at the level of ligand-receptor pairs.

Fig 13. (A) Topographical plots of amyloid (18F-florbetapir) PET SUVr levels for the 6 AD patients.

(each column represents one participant, named after their baseline MMSE score). (B) The relative gene expression of five hub genes between AD patients and normal controls using GAPDH as internal references.