Doença de Parkinson: análise exploratória de dados disponíveis no Gene Expression Omnibus (GEO)

Abstract

Parkinson's Disease (PD) is the second most prevalent neurodegenerative disease in the human population, with the highest incidence in males. Despite diagnostic and therapeutic advances, there are still divergences in the literature on the clinical classification and differentiation of the disease, in addition to the multifactorial nature of the factors that contribute to this disease, whose classification as "idiopathic" has been challenged in recent studies, which also contribute to the numerous challenges involved in the development of potential therapies. With the aging of the world population, diseases like this one have been the focus of countless types of research. Here, we did an exploratory analysis of the public database integrated with GEO (Gene Expression Omnibus), called BioProject, which contains “omic” data regarding Parkinson's Disease, from animal models to human patients. Graphical analysis with diagram generation in Python language (Version 3.7.4) and with google sheets tools was used to visualize the main omic data, as well as the description of other relevant information for researches related to PD. Thus, the main species, tissues, and molecules used in 365 projects were found. A higher frequency of projects with the human species was found, with nervous tissues and with RNA molecules (the most common extraction being total RNA). In addition, information was also collected on the sex, age, and ethnicity of the organisms, and their possible impacts will also be discussed in this work. Finally, it was concluded that the prevalence of data from human and rodent specimens did not vary much from what was found in previous works, as well as the main technologies and tissues in general, as well as a lack of information on gender, age, and ethnicity that, added to the low amount of samples per condition in some projects and the lack of standardization in nomenclatures, has an impact on the quality and correct reproducibility of the data, as well as on the perspective of contribution to future analyses.