Bioinformatics analysis and prediction of Alzheimer's disease and alcohol dependence based on Ferroptosis-related genes

Abstract

Background: Alzheimer's disease (AD) is a neurodegenerative disease whose origins have not been universally accepted. Numerous studies have demonstrated the relationship between AD and alcohol dependence; however, few studies have combined the origins of AD, alcohol dependence, and programmed cell death (PCD) to analyze the mechanistic relationship between the development of this pair of diseases. We demonstrated in previous studies the relationship between psychiatric disorders and PCD, and in the same concerning neurodegeneration-related AD, we found an interesting link with the Ferroptosis pathway. In the present study, we explored the bioinformatic interactions between AD, alcohol dependence, and Ferroptosis and tried to elucidate and predict the development of AD from this aspect.

Methods: We selected the Alzheimer's disease dataset GSE118553 and alcohol dependence dataset GSE44456 from the Gene Expression Omnibus (GEO) database. Ferroptosis-related genes were gathered through Gene Set Enrichment Analysis (GSEA), Kyoto Encyclopedia of Genes and Genomes (KEGG), and relevant literature, resulting in a total of 88 related genes. For the AD and alcohol dependence datasets, we conducted Limma analysis to identify differentially expressed genes (DEGs) and performed functional enrichment analysis on the intersection set. Furthermore, we used ferroptosis-related genes and the DEGs to perform machine learning crossover analysis, employing Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify candidate immune-related central genes. This analysis was also used to construct protein-protein interaction networks (PPI) and artificial neural networks (ANN), as well as to plot receiver operating characteristic (ROC) curves for diagnosing AD and alcohol dependence. We analyzed immune cell infiltration to explore the role of immune cell dysregulation in AD. Subsequently, we conducted consensus clustering analysis of AD using three relevant candidate gene models and examined the immune microenvironment and functional pathways between different subgroups. Finally, we generated a network of gene-gene interactions and miRNA-gene interactions using Networkanalyst.

Results: The crossover of AD and alcohol dependence DEG contains 278 genes, and functional enrichment analysis showed that both AD and alcohol dependence were strongly correlated with Ferroptosis, and then crossed them with Ferroptosis-related genes to obtain seven genes. Three candidate genes were finally identified by machine learning to build a diagnostic prediction model. After validation by ANN and PPI analysis, ROC curves were plotted to assess the diagnostic value of AD and alcohol dependence. The results showed a high diagnostic value of the predictive model. In the immune infiltration analysis, functional metabolism and immune microenvironment of AD patients were significantly associated with Ferroptosis. Finally, analysis of target genes and miRNA-gene interaction networks showed that hsa-mir-34a-5p and has-mir-106b-5p could simultaneously regulate the expression of both CYBB and ACSL4.

Conclusion: We obtained a diagnostic prediction model with good effect by comprehensive analysis, and validation of ROC in AD and alcohol dependence data sets showed good diagnostic, predictive value for both AD (AUC 0. 75, CI 0.91-0.60), and alcohol dependence (AUC 0.81, CI 0.95-0.68). In the consensus clustering grouping, we identified variability in the metabolic and immune microenvironment between subgroups as a likely cause of the different prognosis, which was all related to Ferroptosis function. Finally, we discovered that hsa-mir-34a-5p and has-mir-106b-5p could simultaneously regulate the expression of both CYBB and ACSL4.

Keywords: ACSL4; Alzheimer’s disease; Ferroptosis; STEAP3; alcohol dependence; cybB.

FIGURE 1

Flow chart.

FIGURE 2

(A,B) Volcano maps and heat maps of DEGs in Alzheimer’s disease; (C,D) volcano and heat maps of differentially expressed genes in alcohol-dependent.

FIGURE 3

(A) Venn diagram of candidate genes linked with Alzheimer’s disease and alcohol dependence; (B) Candidate gene KEGG analysis; (C) Candidate genes GO analysis for cellular composition; (D) Candidate genes GO analysis for biological processes; (E) Candidate genes GO analysis for molecular function.

FIGURE 4

(A) Crossover Venn diagram of Alzheimer’s disease with alcohol dependence-related candidate genes and Ferroptosis-related genes; (B,C) LASSO regression candidate gene identification for Alzheimer’s disease dataset; (D,E) LASSO regression candidate gene identification for alcohol dependence dataset; (F) RF regression candidate gene identification for Alzheimer’s disease dataset; (G) RF regression candidate gene identification for alcohol dependence dataset; (H) machine learning Venn diagram cross-identification; (I) Candidate genes PPI network construction.

FIGURE 5

(A) ROC curves in the Alzheimer’s disease dataset; (B) ROC curves in the alcohol dependence dataset; (C) candidate gene expression profile analysis in the Alzheimer’s disease dataset; (D) candidate gene expression profile analysis in the alcohol dependence dataset.

FIGURE 6

(A) Correlation between 22 immune cells; (B) relative proportion of 22 immune cells in each sample; (C) difference in immune infiltration between the Alzheimer’s sample group and the control sample.

FIGURE 7

(A,B) Relevant candidate genes for consensus clustering analysis; (C) violin plot showing differential expression of relevant candidate genes between subgroups; (D) difference in immune infiltration between two subgroups.

FIGURE 8

(A) Volcano map of subgroup DEG; (B) DEGs’ KEGG analysis; (C) DEGs’ GO analysis for cell composition; (D) DEGs’ GO analysis for biological processes; (E) DEGs’ GO analysis for molecular functions.

FIGURE 9

Interaction between candidate genes and miRNAs.