Identification of dysregulated genes in Parkinson’s Disease based on Bioinformatics Analysis

Abstract

Parkinson’s disease is increasing among elderly people around the world. This disorder has symptoms like akinesia, rigidity, and/or tremor. Parkinson's disease is characterized by dopaminergic neuron degeneration. The purpose of this study is to identify dysregulated pathways and genes included and affected in the human body by extracting the microarray expression dataset from dataset NCBI Gene Expression Omnibus (GEO) will be retrieved then data analysis, functional annotation, and protein interaction network will be done to develop blood-based biomarker by bioinformatics analysis. An entire of 180 Differentially Expressed Genes (DEGs) were detected in the gene expression profile of Parkinson’s disease containing 5 control and 5 test samples were studied using t-test (the cut-off is taken P<0.05) of accession number GSE54536 which included whole transcriptome analysis, in which 23 genes consider as up-regulated and 157 genes considered as down-regulated. The outcome of this study provided deeper insights into mechanisms of Parkinson’s disease on the molecular level, also cellular and biological levels by pathway enrichment analysis. Recently findings of this study have shown the involvement of DEGs in the Cancer pathway with a p-value of 4.50E-02, which is selected as it is the most significantly enriched pathway having the highest gene count as compared to other principles pathways. The STRING tool was applied to create a protein-protein interaction network, which was then visualized in Cytoscape. A total of 13 hub proteins were identified with minimum of 6 and a maximum of 9 degrees. Finally, a Connectivity Map (CMap) analysis was carried out to identify 56 small compounds capable of altering the expression of genes in Parkinson's disease, which could be used as a prognostic biomarker to assist individuals be diagnosed earlier and undergoing more effective treatment.