Preeclampsia: a bioinformatics approach through protein-protein interaction networks analysis

• Tipo: Artigo em Revista Científica Internacional
• Data: 2012

Autores

Eduardo Tejera (Autor) Outra; Joao Bernardes (Autor) FMUP; Irene Rebelo (Autor) FFUP

Revista

Título: BMC Systems Biology

Vol. 6

Página Final: 97

ISSN: 1752-0509

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 20 Citações

Scopus - 22 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Matemática

Outras Informações

ID Authenticus: P-002-6ZN

DOI: 10.1186/1752-0509-6-97

Abstract (EN): Background: In this study we explored preeclampsia through a bioinformatics approach. We create a comprehensive genes/proteins dataset by the analysis of both public proteomic data and text mining of public scientific literature. From this dataset the associated protein-protein interaction network has been obtained. Several indexes of centrality have been explored for hubs detection as well as the enrichment statistical analysis of metabolic pathway and disease. Results: We confirmed the well known relationship between preeclampsia and cardiovascular diseases but also identified statistically significant relationships with respect to cancer and aging. Moreover, significant metabolic pathways such as apoptosis, cancer and cytokine-cytokine receptor interaction have also been identified by enrichment analysis. We obtained FLT1, VEGFA, FN1, F2 and PGF genes with the highest scores by hubs analysis; however, we also found other genes as PDIA3, LYN, SH2B2 and NDRG1 with high scores. Conclusions: The applied methodology not only led to the identification of well known genes related to preeclampsia but also to propose new candidates poorly explored or completely unknown in the pathogenesis of preeclampsia, which eventually need to be validated experimentally. Moreover, new possible connections were detected between preeclampsia and other diseases that could open new areas of research. More must be done in this area to resolve the identification of unknown interactions of proteins/genes and also for a better integration of metabolic pathways and diseases.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9