Identification and Interaction of Key Genes in Alzheimer's Disease via Bioinformatics Analysis
Clinical Research Center in Mental Health, Shanghai Yangpu District Mental Health Center, Shanghai University of Medicine and Health Sciences, Shanghai 200090, China
Yiming Wu, Clinical Research Center in Mental Health, Shanghai Yangpu District Mental Health Center, Shanghai University of Medicine and Health Sciences, Shanghai 200090, China, E-mail: firstname.lastname@example.org
Dementia is most frequently caused by Alzheimer’s disease. The objective of this study was to investigate the potential targets of Alzheimer’s disease and its associated biological processes. We obtained gene expression profiles of the GSE5281, GSE48350 and GSE11882 datasets from the gene expression Omnibus database. Among the total datasets, 263 were of Alzheimer’s disease tissues and 204 were of healthy tissues. We selected differentially expressed genes from Alzheimer’s disease tissues and healthy tissues using the gene expression Omnibus 2R tool and Venn diagram software. Next, we used database for annotation, visualization and integrated discovery database, gene ontology database and Kyoto encyclopedia of genes and genomes pathways for analysis. Then, by using Cytoscape’s search tool for the retrieval of interacting genes, it is possible to see how these differentially expressed genes and protein-protein interactions interact with one another. In total, there were 270 consistently expressed genes, containing 193 down-regulated genes and 77 up-regulated genes. Additionally, 270 consistently expressed genes were found by chip analysis as the test set and 242 acquisitions of causative differentially expressed genes were sought via gene list automatically derived for you as the training set. ToppGene was then utilized to optimize these genes. Of the protein-protein interaction network, analysed by the molecular complex detection plugin, 14 regulated genes were selected. In conclusion, using integrated bioinformatics techniques, we have attempted to discover the substantially altered expression genes associated with a poorer prognosis in Alzheimer’s disease and the investigations may benefit their predictive and prognostic value in Alzheimer’s disease.