Signed weighted Gene Co-Expression Network Analysis Revealed Systems Level Characteristics of Parkinson’s Disease

Background: Parkinson’s disease as a progressive neurodegenerativedisease involves central nervous system with consequentprogressive neurological symptoms including behavioraland cognitive disabilities. This disease causes many yearlynew cases worldwide and result in mortality in some years aftersymptoms appearance. Here, we used weighted gene co-expressionnetwork analysis (WGCNA) to detect highly important(hub) genes involved in the disease.Materials and Methods: We used GSE۶۸۷۱۹ dataset contains۲۹ disease and ۴۴ healthy control post-mortem samples fromprefrontal cortex of human brain. Matrix was refined for propergenes using removing duplicate genes and analyzing coefficientof variation. To analyze WGCNA, we used R programmingpackage with the same name. DAVID database was used toanalyze gene ontology. Clustering analysis were done by heatmap.۳ package of R program. Cytoscape ۳.۴.۰ and Gephi ۰.۹programs were used to final analyze and visualize the networks.Results: Our results showed ۶ distinct modules that were highlyrelated to the disease. Gene ontology revealed that these modulesare related to chemical synaptic transmission, regulationof transcription, DNA-templated, and inflammatory response.Clustering of hub genes almost divided controls and diseasesamples into distinct groups. Centrality analysis of the modulesdetected several genes including RPL۱۳, TPRA۱, SLC۲۵A۳۹,and DMPK as hub genes.Conclusion: Our study revealed Parkinson’s disease characteristicsin the systems level and several hub genes which could beused for further studies as markers or therapeutic targets.

کلیدواژه ها:

Clustering ، Gene Ontology ، Hub Genes ، Parkinson’s Disease ، WGCNA