Immune Cell Infiltration and Key Gene Identification in Alzheimer's Disease and Sleep Deprivation

Introduction: Alzheimer's disease (AD) is a progressive neurodegenerative disorder marked by Amyloid-β plaques and neurofibrillary tangles. Disrupted circadian rhythms are common in AD and may worsen cognitive decline and psychological symptoms. The link between sleep deprivation and Alzheimer's risk remains unclear. This study aimed to identify potential diagnostic markers for Alzheimer's and sleep deprivation, focusing on the role of immune cell infiltration in disease progression. Materials and Methods: We examined AD expression data from the GEO database and sleep deprivation( SD)-related data from GeneCards. Using LIMMA on the GSE15222 dataset, we found 209 DEGs, analyzed them with four machine learning algorithms, and identified four Hub genes. We validated these findings with the GSE33000 dataset. CIBERSORT was employed to analyze 22 immune cell features, and Spearman correlation was used to assess the link between diagnostic markers and immune cells. results: It was determined that AD and SD are connected to changes in the immunological microenvironment. WGCNA revealed 1568 potential key genes. 209 overlapping expression regions were obtained by VENN mapping of differential and co-expressed genes and circadian rhythm. Four key genes (CIT, FASN, ELK1 and GFAP) were verified by machine learning methods, and ROC curve analysis, box attempt analysis, and Nomogram analysis were performed for key genes. GSE33000 was used to verify the key genes. These genes have high predictive accuracy. In AD samples, T cells CD4 naive, T cells follicular helper, T cells regulatory (Tregs), Monocytes, Macrophages M0 and Dendritic cells were found the expression ratio of activated, Mast cells resting and Neutrophils were generally high. B cells memory, T cells CD4 memory resting, Macrophages M2, Mast cells activated expression were relatively low (P < 0.05). According to the results of correlation analysis, CIT, FASN, ELK1 and GFAP showed a significant degree of association with a variety of immune cells. Results: AD and SD were linked to immune microenvironment changes. Initially, 1568 potential key genes were identified, and Venn analysis revealed 209 overlapping regions. Machine learning validated four key genes, confirming their high predictive accuracy. Significant differences in immune cell expression were found in AD samples, and correlation analysis showed CIT, FASN, ELK1, and GFAP were significantly associated with various immune cells. Conclusion: CIT, FASN, ELK1, and GFAP are key genes linked to pathology progression in AD and SD within the immune microenvironment. Identifying molecular subgroups may offer new perspectives for personalized Alzheimer's treatment.