Probing molecular pathways: Illuminating the connection between COVID-19 and Alzheimer's disease through the endocannabinoid system dynamics.

Our study investigates the molecular link between COVID-19 and Alzheimer's disease (AD). We aim to elucidate the mechanisms by which COVID-19 may influence the onset or progression of AD. Using bioinformatic tools, we analyzed gene expression datasets from the Gene Expression Omnibus (GEO) database, including GSE147507, GSE12685, and GSE26927. Intersection analysis was utilized to identify common differentially expressed genes (CDEGs) and their shared biological pathways. Consensus clustering was conducted to group AD patients based on gene expression, followed by an analysis of the immune microenvironment and variations in shared pathway activities between clusters. Additionally, we identified transcription factor-binding sites shared by CDEGs and genes in the common pathway. The activity of the pathway and the expression levels of the CDEGs were validated using GSE164805 and GSE48350 datasets. Six CDEGs (MAL2, NECAB1, SH3GL2, EPB41L3, MEF2C, and NRGN) were identified, along with a downregulated pathway, the endocannabinoid (ECS) signaling pathway, common to both AD and COVID-19. These CDEGs showed a significant correlation with ECS activity (p < 0.05) and immune functions. The ECS pathway was enriched in healthy individuals' brains and downregulated in AD patients. Validation using GSE164805 and GSE48350 datasets confirmed the differential expression of these genes in COVID-19 and AD tissues. Our findings reveal a potential pathogenetic link between COVID-19 and AD, mediated by CDEGs and the ECS pathway. However, further research and multicenter evidence are needed to translate these findings into clinical applications.

The role of CXCL8 and CCNB1 in predicting hepatocellular carcinoma in the context of cirrhosis: implications for early detection and immune-based therapies.

BACKGROUND: Cirrhosis is a serious condition characterized by the replacement of healthy liver tissue with scar tissue, which can progress to liver failure if left untreated. Hepatocellular carcinoma (HCC) is a concerning complication of cirrhosis. It can be challenge to identify individuals with cirrhosis who are at high risk of developing HCC, particularly in the absence of known risk factors. METHODS: In this study, statistical and bioinformatics methods were utilized to construct a protein-protein interaction network and identify disease-related hub genes. We analyzed two hub genes, CXCL8 and CCNB1, and developed a mathematical model to predict the likelihood of developing HCC in individuals with cirrhosis. We also investigated immune cell infiltration, functional analysis under ontology terms, pathway analysis, distinct clusters of cells, and protein-drug interactions. RESULTS: The results indicated that CXCL8 and CCNB1 were associated with the development of cirrhosis-induced HCC. A prognostic model based on these two genes was able to predict the occurrence and survival time of HCC. In addition, the candidate drugs were also discovered based on our model. CONCLUSION: The findings offer the potential for earlier detection of cirrhosis-induced HCC and provide a new instrument for clinical diagnosis, prognostication, and the development of immunological medications. This study also identified distinct clusters of cells in HCC patients using UMAP plot analysis and analyzed the expression of CXCL8 and CCNB1 within these cells, indicating potential therapeutic opportunities for targeted drug therapies to benefit HCC patients.