Discovery of potential Alzheimer’s disease key biomarkers based on bioinformatics analysis

Abstract

AbstractBackgroundAlzheimer’s disease (AD) is a neurodegenerative disorder and the most common cause of dementia in the elderly. AD is characterized by short‐term to long‐term memory loss, confusion, mood changes, and language difficulties. Numerous studies have focused on the dysregulated genes in AD, but the pathogenesis is still unknown. The underlying risk factors remain largely unclear. The discovery of more accurate AD biomarkers will allow early detection and development of new treatments. Recently bioinformatics has become a relevant research tool for biomedicine. In this study, we explored differential expressed genes (DEGs) potentially involved in the pathogenesis of AD.MethodOpen access databases of RNA microarrays of tissue from brain and peripheral blood of AD patient, were analyzed after background correction and data normalization; Limma package was used for Differential expression Analysis (DEA) throw statistical R programming language. Data were corrected with the Benjamini and Hochberg approach, and the genes with p values equal to or less than 0.05 were considered significant. The direction of change in gene expression was determined by its variation in the “log2‐fold change” between controls and patients. We performed a functional enrichment analysis of GO using goana and topGO‐Limma.ResultWith the Brain tissue database, we obtained 17 down regulated genes (DR) and 81 up regulated genes (UR). Functional enrichment analysis of DEGs showed UR pathways: behavior, nervous systems process, post synapses, enzyme binding, while DR were: cellular component organization, RNA metabolic process, protein‐containing complex, nucleoside triphosphatase activity and RNA binding. The blood tissue database showed 850 genes DR and 693 genes UR. The functional enrichment analysis of these DEGs showed UR pathways: regulation of transcription by RNA polymerase‐II and RNA processing, while DR were: cell development and synapses. Finally, the intersection of the DEGs in the two databases showed 3 genes shared between the brain and blood.ConclusionOur in‐silico analysis of brain and blood databases identified several UR and DR genes; the detection of these genes could provide new insight into potential therapies for AD. However, more research is needed to validate these potential new biomarkers and correlate them with the clinical data at different AD stages.