Identification of immune cell infiltration and biomarkers in Alzheimer's disease and COVID-19 by integrated bioinformatics analysis and machine learning

Abstract

Background: Since its emergence in late 2019, COVID-19 has become a global epidemic, resulting in numerous infections, including a significant number of critically ill patients. Several studies have suggested a possible link between Alzheimer's disease (AD) and COVID-19. For instance, a Mendelian randomization study has proposed a causal relationship between Alzheimer's disease and COVID-19 in the pathogenic mechanism. However, there are limited studies exploring the common pathogenic genes and immune infiltration between the two. Therefore, we conducted this study to identify key genes in COVID-19 associated with Alzheimer's disease, evaluate their correlation with immune cell characteristics and metabolic pathways, and investigate potential novel biomarkers. Methods: Transcriptome analyses were used to identify common biomolecular markers of AD and COVID-19. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed on gene chip datasets (GSE213313, GSE5281, and GSE63060) from AD and COVID-19 patients to identify genes associated with both conditions. Common pathogenic molecular mechanisms were identified through Gene Ontology (GO) enrichment analyses. The core genes were then identified using machine learning methods. Subsequently, we evaluated the relationship between these core genes and common immune cells and metabolic pathways. Finally, our findings were validated through single-cell analysis. Results: The study identified 484 common differentially expressed genes (DEGs) by taking the intersection of genes between AD and COVID-19. The black module, containing 132 genes, showed the highest association between the two diseases according to WGCNA. GO enrichment analysis revealed that these genes mainly affect inflammation, cytokines, immune-related functions, and signaling pathways related to metal ions and cellular response to viruses. Additionally, a machine learning approach identified eight core genes. We identified links between these genes and immune cells and also found a strong association between EIF3H and oxidative phosphorylation. In addition, these results were further validated by single-cell analysis. Conclusion: This study identifies potential shared genes, signaling pathways, immune-related alterations, and changes in metabolic pathways that may collectively contribute to the pathogenesis of COVID-19 and Alzheimer's disease. These findings provide new targets for the diagnosis and treatment of both diseases.