Poswar Fabiano de Oliveira, Farias Lucyana Conceição, Fraga Carlos Alberto de Carvalho, Bambirra Wilson, Brito-Júnior Manoel, Sousa-Neto Manoel Damião, Santos Sérgio Henrique Souza, de Paula Alfredo Maurício Batista, D'Angelo Marcos Flávio Silveira Vasconcelos, Guimarães André Luiz Sena
Department of Dentistry, Universidade Estadual de Montes Claros, Minas Gerais, Brazil.
Department of Physiopathology, Universidade Estadual de Montes Claros, Minas Gerais, Brazil.
J Endod. 2015 Jun;41(6):877-83. doi: 10.1016/j.joen.2015.02.004. Epub 2015 Apr 11.
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.
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.
For RCs, the suggested leader genes were TP53 and EP300, whereas PGs were associated with IL2RG, CCL2, CCL4, CCL5, CCR1, CCR3, and CCR5 genes.
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.
生物信息学已成为分析不同疾病研究产生的大量数据的重要工具。在本研究中，基于主导基因方法对根囊肿（RCs）和根尖肉芽肿（PGs）的基因表达进行了表征。
应用经过验证的生物信息学算法来识别RCs和PGs的主导基因。首先在PubMed、GenBank、GeneAtlas和GeneCards数据库中识别与RCs和PGs相关的基因。使用在线可用的STRING软件（欧洲分子生物学实验室[EMBL]，德国巴登-符腾堡州海德堡），通过名为加权链接数的显著性分数构建已识别基因之间的相互作用图谱。基于加权链接数，使用k均值对基因进行聚类。最高聚类中的基因被视为主导基因。多层感知器神经网络分析用作基因分类的补充。
对于RCs，建议的主导基因是TP53和EP300，而PGs与IL2RG、CCL2、CCL4、CCL5、CCR1、CCR3和CCR5基因相关。
我们的数据揭示了RCs和PGs不同的基因表达，表明不仅炎症性质，其他生物学过程也可能区分RCs和PGs。
