BACKGROUND The Smad7 protein is negative regulator of the TGF-β signaling pathway, which is upregulated in patients with breast cancer. miRNAs regulate proteins expressions by arresting or degrading the mRNAs. The purpose of this work is to identify a miRNAs profile that regulates the expression of the mRNA coding for Smad7 in breast cancer using the data from patients with breast cancer obtained from the Cancer Genome Atlas Project. METHODS We develop an automatic search method based on genetic algorithms to find a predictive model based on deep neural networks (DNN) which fit the set of biological data and apply the Olden algorithm to identify the relative importance of each miRNAs. RESULTS A computational model of non-linear regression is shown, based on deep neural networks that predict the regulation given by the miRNA target transcripts mRNA coding for Smad7 protein in patients with breast cancer, with R2 of 0.99 is shown and MSE of 0.00001. In addition, the model is validated with the results in vivo and in vitro experiments reported in the literature. The set of miRNAs hsa-mir-146a, hsa-mir-93, hsa-mir-375, hsa-mir-205, hsa-mir-15a, hsa-mir-21, hsa-mir-20a, hsa-mir-503, hsa-mir-29c, hsa-mir-497, hsa-mir-107, hsa-mir-125a, hsa-mir-200c, hsa-mir-212, hsa-mir-429, hsa-mir-34a, hsa-let-7c, hsa-mir-92b, hsa-mir-33a, hsa-mir-15b, hsa-mir-224, hsa-mir-185 and hsa-mir-10b integrate a profile that critically regulates the expression of the mRNA coding for Smad7 in breast cancer. CONCLUSIONS We developed a genetic algorithm to select best features as DNN inputs (miRNAs). The genetic algorithm also builds the best DNN architecture by optimizing the parameters. Although the confirmation of the results by laboratory experiments has not occurred, the results allow suggesting that miRNAs profile could be used as biomarkers or targets in targeted therapies.

中文翻译：

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基于GA和DNN的模型可预测乳腺癌中受miRNA调节的mRNA-Smad7表达。

背景Smad7蛋白是TGF-β信号通路的负调节剂，在乳腺癌患者中被上调。miRNA通过阻止或降解mRNA来调节蛋白质表达。这项工作的目的是使用从癌症基因组图谱项目获得的乳腺癌患者数据，确定一种调节乳腺癌中编码Smad7的mRNA表达的miRNA谱。方法我们开发了一种基于遗传算法的自动搜索方法，以找到基于深度神经网络（DNN）的预测模型，该模型适合生物数据集，并应用Olden算法来识别每个miRNA的相对重要性。结果显示了非线性回归的计算模型，基于深层神经网络的研究，该网络预测了乳腺癌患者中编码Smad7蛋白的miRNA靶转录本的mRNA调控，R2为0.99，MSE为0.00001。另外，用文献中报道的体内和体外实验结果验证了该模型。miRNA的集合：hsa-mir-146a，hsa-mir-93，hsa-mir-375，hsa-mir-205，hsa-mir-15a，hsa-mir-21，hsa-mir-20a，hsa-mir- 503，hsa-mir-29c，hsa-mir-497，hsa-mir-107，hsa-mir-125a，hsa-mir-200c，hsa-mir-212，hsa-mir-429，hsa-mir-34a， hsa-let-7c，hsa-mir-92b，hsa-mir-33a，hsa-mir-15b，hsa-mir-224，hsa-mir-185和hsa-mir-10b整合了一个关键调控蛋白表达的谱在乳腺癌中编码Smad7的mRNA。结论我们开发了一种遗传算法来选择最佳特征作为DNN输入（miRNA）。遗传算法还通过优化参数来构建最佳的DNN架构。尽管尚未通过实验室实验确认结果，但这些结果表明，miRNA谱可作为靶向治疗中的生物标志物或靶标。