Consensus strategy in genes prioritization and combined bioinformatics analysis for preeclampsia pathogenesis |
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Authors: | Eduardo Tejera Maykel Cruz-Monteagudo María-Eugenia Sánchez Aminael Sánchez-Rodríguez Yunierkis Pérez-Castillo Fernanda Borges Maria Natália Dias Soeiro Cordeiro César Paz-y-Miño Irene Rebelo |
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Institution: | 1.Facultad de Medicina,Universidad de Las Américas,Quito,Ecuador;2.Department of Molecular and Cellular Pharmacology,Miller School of Medicine and Center for Computational Science, University of Miami,Miami,USA;3.Department of General Education,West Coast University—Miami Campus,Doral,USA;4.Departamento de Ciencias Naturales,Universidad Técnica Particular de Loja,Loja,Ecuador;5.Escuela de Ciencias Físicas y Matemáticas,Universidad de Las Américas,Quito,Ecuador;6.CIQUP/Departamento de Quimica e Bioquimica, Faculdade de Ciências,Universidade do Porto,Porto,Portugal;7.REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences,University of Porto,Porto,Portugal;8.Centro de Investigaciones genética y genómica, Facultad de Ciencias de la Salud,Universidad Tecnológica Equinoccial,Quito,Ecuador;9.Faculty of Pharmacy,University of Porto,Porto,Portugal;10.UCIBIO@REQUIMTE,Caparica,Portugal |
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Abstract: | BackgroundPreeclampsia is a multifactorial disease with unknown pathogenesis. Even when recent studies explored this disease using several bioinformatics tools, the main objective was not directed to pathogenesis. Additionally, consensus prioritization was proved to be highly efficient in the recognition of genes-disease association. However, not information is available about the consensus ability to early recognize genes directly involved in pathogenesis. Therefore our aim in this study is to apply several theoretical approaches to explore preeclampsia; specifically those genes directly involved in the pathogenesis.MethodsWe firstly evaluated the consensus between 12 prioritization strategies to early recognize pathogenic genes related to preeclampsia. A communality analysis in the protein-protein interaction network of previously selected genes was done including further enrichment analysis. The enrichment analysis includes metabolic pathways as well as gene ontology. Microarray data was also collected and used in order to confirm our results or as a strategy to weight the previously enriched pathways.ResultsThe consensus prioritized gene list was rationally filtered to 476 genes using several criteria. The communality analysis showed an enrichment of communities connected with VEGF-signaling pathway. This pathway is also enriched considering the microarray data. Our result point to VEGF, FLT1 and KDR as relevant pathogenic genes, as well as those connected with NO metabolism.ConclusionOur results revealed that consensus strategy improve the detection and initial enrichment of pathogenic genes, at least in preeclampsia condition. Moreover the combination of the first percent of the prioritized genes with protein-protein interaction network followed by communality analysis reduces the gene space. This approach actually identifies well known genes related with pathogenesis. However, genes like HSP90, PAK2, CD247 and others included in the first 1% of the prioritized list need to be further explored in preeclampsia pathogenesis through experimental approaches. |
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