Bioinformatics, interaction network analysis, and neural networks to characterize gene expression of radicular cyst and periapical granuloma.

Autor:	Poswar Fde O; Department of Dentistry, Universidade Estadual de Montes Claros, Minas Gerais, Brazil., Farias LC; Department of Physiopathology, Universidade Estadual de Montes Claros, Minas Gerais, Brazil., Fraga CA; Department of Dentistry, Universidade Estadual de Montes Claros, Minas Gerais, Brazil., Bambirra W Jr; Department of Restorative Dentistry, Faculty of Dentistry, Universidade Federal de Minas Gerais, Minas Gerais, Brazil., Brito-Júnior M; Department of Dentistry, Universidade Estadual de Montes Claros, Minas Gerais, Brazil., Sousa-Neto MD; Department of Restorative Dentistry, Faculty of Dentistry, Universidade de São Paulo, Ribeirão Preto, São Paulo, Brazil., Santos SH; Department of Physiopathology, Universidade Estadual de Montes Claros, Minas Gerais, Brazil; Department of Computer Science, Universidade Estadual de Montes Claros, Minas Gerais, Brazil;, de Paula AM; Department of Dentistry, Universidade Estadual de Montes Claros, Minas Gerais, Brazil., D'Angelo MF; Department of Computer Science, Universidade Estadual de Montes Claros, Minas Gerais, Brazil;, Guimarães AL; Department of Dentistry, Universidade Estadual de Montes Claros, Minas Gerais, Brazil. Electronic address: andreluizguimaraes@gmail.com.
Jazyk:	angličtina
Zdroj:	Journal of endodontics [J Endod] 2015 Jun; Vol. 41 (6), pp. 877-83. Date of Electronic Publication: 2015 Apr 11.
DOI:	10.1016/j.joen.2015.02.004
Abstrakt:	Introduction: Bioinformatics has emerged as an important tool to analyze the large amount of data generated by research in different diseases. In this study, gene expression for radicular cysts (RCs) and periapical granulomas (PGs) was characterized based on a leader gene approach. Methods: A validated bioinformatics algorithm was applied to identify leader genes for RCs and PGs. Genes related to RCs and PGs were first identified in PubMed, GenBank, GeneAtlas, and GeneCards databases. The Web-available STRING software (The European Molecular Biology Laboratory [EMBL], Heidelberg, Baden-Württemberg, Germany) was used in order to build the interaction map among the identified genes by a significance score named weighted number of links. Based on the weighted number of links, genes were clustered using k-means. The genes in the highest cluster were considered leader genes. Multilayer perceptron neural network analysis was used as a complementary supplement for gene classification. Results: For RCs, the suggested leader genes were TP53 and EP300, whereas PGs were associated with IL2RG, CCL2, CCL4, CCL5, CCR1, CCR3, and CCR5 genes. Conclusions: Our data revealed different gene expression for RCs and PGs, suggesting that not only the inflammatory nature but also other biological processes might differentiate RCs and PGs. (Copyright © 2015 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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