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Identification of miRNAs Expression Profile in Gastric Cancer Using Self-Organizing Maps (SOM)

2014; Biomedical Informatics; Volume: 10; Issue: 5 Linguagem: Inglês

10.6026/97320630010246

0973-8894

Larissa Luz Gomes, Fabiano Cordeiro Moreira, Igor Hamoy, Sidney Emanuel Batista dos Santos, Paulo Pimentel de Assumpção, Ádamo Lima de Santana, Ândrea Ribeiro‐dos‐Santos,

Circular RNAs in diseases

In this paper, an unsupervised artificial neural network was implemented to identify the patters of specific signatures. The network was based on the differential expression of miRNAs (under or over expression) found in healthy or cancerous gastric tissues. Among the tissues analyzes, the neural network evaluated 514 miRNAs of gastric tissue that exhibited significant differential expression. The result suggested a specific expression signature nine miRNAs (hsa-mir-21, hsa-mir-29a, hsa-mir-29c, hsa-mir-148a, hsa-mir-141, hsa-let-7b, hsa-mir-31, hsa-mir-451, and hsa-mir-192), all with significant values (p-value < 0.01 and fold change > 5) that clustered the samples into two groups: healthy tissue and gastric cancer tissue. The results obtained "in silico" must be validated in a molecular biology laboratory; if confirmed, this method may be used in the future as a risk marker for gastric cancer development.