Expression characteristics of peripheral blood genes reveal potential biomarkers and candidate therapeutic targets for Parkinson's disease

Weifeng Chen, Dajin Zhou, Na Zhu, Xiumei Yan, Guizhen Jiang, Longyou Zhao


In neurodegenerative disease, Parkinson’s disease is the second most common one. Current demographic trends tell that by 2030, the risk of prevalence is close to 4% and the incidence is expected to double. Understanding the detailed process of Parkinson's disease can help us to figure out new biomarkers and candidate therapeutic targets for the diagnosis and progression of PD. This study is based on modularity for in-depth analysis and exploration of critical genes in the pathogenesis of Parkinson's disease, intended to identify the molecular processes of Parkinson's disease. According to the hypergeometric test, by performing differential analysis, enrichment analysis, co-expression module analysis, network connectivity analysis and finally, the ncRNA (non-coding RNA) and transcription factor that regulate the module were predicted. Based on the above methods, we obtained ten co-expression modules, including 2180 differential genes. Among them, RB1, IL7, and other genes were significantly differentially expressed in PD patients, and they had existing regulation in dysfunction modules, which was identified as Key genes in PD. The biological processes involved in the modular genes, for example, regulate lymphocyte activation, signal release, cellular calcium homeostasis, regulation of inflammatory responses, and regulation of exocytosis. This behavior significantly regulates signaling pathways such as cytokine-cytokine receptor interactions. Further, we identified ncRNA pivot including miR-25-3p. Also, transcription Factors pivot such as RELA, STAT1 significantly regulate dysfunction modules. This study can help to reveal all Parkinson's core dysfunction modules and potential regulatory factors as well as essential genes and the study assists to improve our understanding of its pathogenesis. The study can also be used to determine treatment goals and measure the effectiveness of interventions to provide predictive biomarkers and candidate therapeutic targets.


Parkinson's disease; Dysfunction module; Biomarkers; Therapeutic targets; Essential genes

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