Identification of network-based differential gene expression signatures and their transcriptional factors to develop progressive blood biomarkers for Alzheimer's disease

Abstract

BackgroundAlzheimer's disease (AD) can go years without diagnosis due to a lack of biomarker identification with a growing incidence rate in the geriatric population. Identifying genes and their transcriptional factors and kinases that regulate the phosphorylation and pathogenesis of AD is a state-of-art approach to identifying novel diagnostic biomarkers. MethodologyMicroarray dataset GSE140829 was retrieved from the GEO database to identify differentially expressed genes (DEGs) between AD and control samples. Furthermore, a protein interaction network was built using the String database, and DEGs were examined using Cytoscape based on high betweenness centrality (BC) and degree values. Additionally, the hub genes were identified via Cytohubba, and eight modules were then identified using molecular complex detection (MCODE). ResultsUsing a Venn diagram, we mined 1674 common DEGs from AD and control samples. The primary interaction data from the STRING consists of 1198 nodes and 1992 edges, which serve an extenuated network. Further, a core network was extracted from an extended network that consists of 676 nodes connected via 1955 edges and was analyzed based on high BC and Degree values. Based on the network topological analysis and clustering, the hub genes were identified and further validated by comparing them with the backbone network. Compelling results from both the core and backbone network. HSP90AA1 identified as a major blood biomarker, followed by HSPA5, CREBBP, UBC, GRB2, MAPK3, and TRAF6 are selected as the major biomarkers. ConclusionThis study shows the potential for predicting AD risk factors and identifies promising blood biomarkers for early AD diagnosis. Additionally, developing inhibitors for the identified transcriptional factors and kinases might improve future therapeutic applications.