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Consensus strategy in genes prioritization and combined bioinformatics analysis for preeclampsia pathogenesis

BACKGROUND: Preeclampsia 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 reco...

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Autores principales: Tejera, Eduardo, Cruz-Monteagudo, Maykel, Burgos, Germán, Sánchez, María-Eugenia, Sánchez-Rodríguez, Aminael, Pérez-Castillo, Yunierkis, Borges, Fernanda, Cordeiro, Maria Natália Dias Soeiro, Paz-y-Miño, César, Rebelo, Irene
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5549357/
https://www.ncbi.nlm.nih.gov/pubmed/28789679
http://dx.doi.org/10.1186/s12920-017-0286-x
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author Tejera, Eduardo
Cruz-Monteagudo, Maykel
Burgos, Germán
Sánchez, María-Eugenia
Sánchez-Rodríguez, Aminael
Pérez-Castillo, Yunierkis
Borges, Fernanda
Cordeiro, Maria Natália Dias Soeiro
Paz-y-Miño, César
Rebelo, Irene
author_facet Tejera, Eduardo
Cruz-Monteagudo, Maykel
Burgos, Germán
Sánchez, María-Eugenia
Sánchez-Rodríguez, Aminael
Pérez-Castillo, Yunierkis
Borges, Fernanda
Cordeiro, Maria Natália Dias Soeiro
Paz-y-Miño, César
Rebelo, Irene
author_sort Tejera, Eduardo
collection PubMed
description BACKGROUND: Preeclampsia 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. METHODS: We 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. RESULTS: The 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. CONCLUSION: Our 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12920-017-0286-x) contains supplementary material, which is available to authorized users.
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spelling pubmed-55493572017-08-11 Consensus strategy in genes prioritization and combined bioinformatics analysis for preeclampsia pathogenesis Tejera, Eduardo Cruz-Monteagudo, Maykel Burgos, Germán Sánchez, María-Eugenia Sánchez-Rodríguez, Aminael Pérez-Castillo, Yunierkis Borges, Fernanda Cordeiro, Maria Natália Dias Soeiro Paz-y-Miño, César Rebelo, Irene BMC Med Genomics Research Article BACKGROUND: Preeclampsia 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. METHODS: We 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. RESULTS: The 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. CONCLUSION: Our 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12920-017-0286-x) contains supplementary material, which is available to authorized users. BioMed Central 2017-08-08 /pmc/articles/PMC5549357/ /pubmed/28789679 http://dx.doi.org/10.1186/s12920-017-0286-x Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Tejera, Eduardo
Cruz-Monteagudo, Maykel
Burgos, Germán
Sánchez, María-Eugenia
Sánchez-Rodríguez, Aminael
Pérez-Castillo, Yunierkis
Borges, Fernanda
Cordeiro, Maria Natália Dias Soeiro
Paz-y-Miño, César
Rebelo, Irene
Consensus strategy in genes prioritization and combined bioinformatics analysis for preeclampsia pathogenesis
title Consensus strategy in genes prioritization and combined bioinformatics analysis for preeclampsia pathogenesis
title_full Consensus strategy in genes prioritization and combined bioinformatics analysis for preeclampsia pathogenesis
title_fullStr Consensus strategy in genes prioritization and combined bioinformatics analysis for preeclampsia pathogenesis
title_full_unstemmed Consensus strategy in genes prioritization and combined bioinformatics analysis for preeclampsia pathogenesis
title_short Consensus strategy in genes prioritization and combined bioinformatics analysis for preeclampsia pathogenesis
title_sort consensus strategy in genes prioritization and combined bioinformatics analysis for preeclampsia pathogenesis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5549357/
https://www.ncbi.nlm.nih.gov/pubmed/28789679
http://dx.doi.org/10.1186/s12920-017-0286-x
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