Identification of Shared Gene Signatures Associated with Alzheimer's Disease and COVID-19 through Bioinformatics Analysis.

Background: Some studies have shown a link between Alzheimer's disease (AD) and COVID-19. This includes a Mendelian randomization study, which suggests that Alzheimer's disease and COVID-19 may be causally linked in terms of pathogenic mechanisms. However, there are fewer studies related to the two in terms of common pathogenic genes and immune infiltration. We conducted this study to identify key genes in COVID-19 linked to Alzheimer's disease, assess their relevance to immune cell profiles, and explore potential novel biomarkers.

Methods: The RNA datasets GSE157103 and GSE125583 for COVID-19 and Alzheimer's disease, respectively, were acquired via the GEO database and subsequently processed. Through the utilization of differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA), genes associated with Alzheimer's disease and COVID-19 were identified. The immune cell signatures were estimated using the xCell algorithm, and correlation analysis identified links between key genes and significantly different immune cell signatures. Finally, we conducted transcription factor (TF) analysis, mRNA analysis, and sensitivity drug analysis.

Results: Differential analysis identified 3560 (2099 up-regulated and 1461 down-regulated) and 1456 (640 up-regulated and 816 down-regulated) differential genes for COVID-19 and AD compared to normal controls, respectively. WGCNA analysis revealed 254 key module genes for COVID-19 and 791 for AD. We combined the differential genes and WGCNA key module genes for each disease to obtain two gene sets. The intersection of these two gene sets was examined to obtain intersecting genes. Subsequently, PPI network analysis was conducted, leading to the identification of 12 hub genes. Then, 12 immune-related hub genes were further identified. Immune infiltration patterns and the correlation between 12 hub genes and 64 immune cell types were analyzed. The analysis revealed a significant positive correlation between the two diseases under study. The relationship network between Transcription Factors and mRNA, as well as the predictions of drugs, further illustrate the strong association between the two diseases. This provides valuable information for further target exploration and drug screening.

Conclusion: Our study suggests potential shared genes, signalling pathways, and common drug candidates that may be associated with COVID-19 and AD. This may provide insights for future studies of AD patients infected with SARS-CoV-2 and help improve diagnostic and therapeutic approaches.