Alzheimer’s disease (AD) is a progressive neurodegenerative disease that ranks as the fourth most common cause of death in developed countries. In our study, genes differentially expressed between AD and healthy individuals were identified and used to construct protein-protein interaction (PPI) networks. The AD-related PPI network was used to identify functional modules, and enrichment analysis showed that they were significantly involved in “Alzheimer’s disease”, “apoptosis”, and related pathways. We predicted non-coding RNAs and transcription factors that may regulate the functional modules. The expression of hub genes and transcription factors was validated in an independent data set. The results in this study provide several candidates for further research on mechanisms of AD pathogenesis.

Purpose The purpose of this study was to investigate the potential pathogenic mechanisms of post-intracerebral hemorrhage depression. Methods Profiles of gene expression in brain tissue of patients with intracerebral hemorrhage (ICH) or depression were downloaded from the Gene Expression Omnibus (GEO) database. We analyzed differentially expressed genes (DEGs) for the two diseases separately. With these DEGs, we conducted an enrichment analysis based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) as well as cross-talk analysis, then we identified hub bridge genes using integrated bridge landscape analysis. Results We found 131 DEGs for interaction between ICH and depression. In the enrichment analysis, we found 55 GO terms and KEGG pathways involving interacting genes of ICH and depression, and 10 GO terms and 10 KEGG pathways most significantly related to cross-talk between ICH and depression. In the integrated bridge landscape analysis, we identified 20 hub bridge genes. In further analysis, we found that hub bridge genes HLA-A, HMOX1 , and JUN related to endocytosis, cell adhesion, and phagosomes may exert their effects through the dopamine (DA) system and the serotonergic pathway post-ICH depression. HLA-A may play a role in the occurrence and development of ICH and depression through immune mediation and cell adhesion. HMOX1 and JUN may participate in the mechanism by interacting with HLA-A . Conclusion Through bioinformatics analysis, we identified potential hub bridge genes and pathways related to post-ICH depression. Our study provides references for further research on mechanisms on the pathogenesis of post-ICH depression.

BackgroundDespite tremendous progress in diagnosis and prediction of Alzheimer’s disease (AD), the absence of treatments implies the need for further research. In this study, we screened AD biomarkers by comparing expression profiles of AD and control tissue samples and used various models to identify potential biomarkers. We further explored immune cells associated with these biomarkers that are involved in the brain microenvironment.MethodsBy differential expression analysis, we identified differentially expressed genes (DEGs) of four datasets (GSE125583, GSE118553, GSE5281, GSE122063), and common expression direction of genes of four datasets were considered as intersecting DEGs, which were used to perform enrichment analysis. We then screened the intersecting pathways between the pathways identified by enrichment analysis. DEGs in intersecting pathways that had an area under the curve (AUC) > 0.7 constructed random forest, least absolute shrinkage and selection operator (LASSO), logistic regression, and gradient boosting machine models. Subsequently, using receiver operating characteristic curve (ROC) and decision curve analysis (DCA) to select an optimal diagnostic model, we obtained the feature genes. Feature genes that were regulated by differentially expressed miRNAs (AUC > 0.85) were explored further. Furthermore, using single-sample GSEA to calculate infiltration of immune cells in AD patients.ResultsScreened 1855 intersecting DEGs that were involved in RAS and AMPK signaling. The LASSO model performed best among the four models. Thus, it was used as the optimal diagnostic model for ROC and DCA analyses. This obtained eight feature genes, including ATP2B3, BDNF, DVL2, ITGA10, SLC6A12, SMAD4, SST, and TPI1. SLC6A12 is regulated by miR-3176. Finally, the results of ssGSEA indicated dendritic cells and plasmacytoid dendritic cells were highly infiltrated in AD patients.ConclusionThe LASSO model is the optimal diagnostic model for identifying feature genes as potential AD biomarkers, which can supply new strategies for the treatment of patients with AD.

ObjectiveTo explore the role and mechanism of Sirt1 in protecting neural stem cells (NSCs) from apoptosis.Materials and methodsTransfection was used to overexpress Sirt1 in rat NSCs. The effect of Sirt1 overexpression on camptothecin-induced apoptosis of NSCs was evaluated. Western blotting was used to examine the expression of Sirt1, cleaved caspase-3, and acetylated histone 3K9.ResultsOverexpression of Sirt1 in NSCs decreased the cleavage of caspase-3 and acetylation of histone 3K9.ConclusionSirt1 may protect NSCs from apoptosis by decreasing the acetylation of histone 3 on K9.