Exploration of Potential miRNA Biomarkers and Prediction for Ovarian Cancer Using Artificial Intelligence.
Exploration of Potential miRNA Biomarkers and Prediction for Ovarian Cancer Using Artificial Intelligence.
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DOI：
10.3389/fgene.2021.724785
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发表时间：
2021
影响因子：
3.7
通讯作者：
Santaguida P
中科院分区：
文献类型：
--
作者：
Hamidi F;Gilani N;Belaghi RA;Sarbakhsh P;Edgünlü T;Santaguida P
卵巢癌是第二大妇科恶性肿瘤，死亡率高。从高维、小样本的基因表达数据中进行基因表达数据的分类是一项具有挑战性的任务。miRNA是一种长度为18-25个核苷酸的小型非编码RNA，可调节基因表达，它的发现揭示了一种新的基因调节阵列的存在，并据报道在癌症中发挥着重要作用。通过使用LASSO和弹性网络作为特征选择技术的嵌入式算法，本研究在公开可用的数据集GSE 106817中鉴定了10种在卵巢血清癌样本中与非癌症样本相比受到调控的miRNA：hsa-miR-5100、hsa-miR-6800-5p、hsa-miR-1233-5p、hsa-miR-4532、hsa-miR-4783-3p、hsa-miR-4787-3p、hsa-miR-1228-5p、hsa-miR-1290、hsa-miR-3184-5p和hsa-miR-320b。此外，我们还实现了最先进的机器学习分类器，如逻辑回归、随机森林、人工神经网络、XGBoost和决策树，以构建临床预测模型。接下来，通过ROC分析在内部（GSE 106817）和外部验证数据集（GSE 113486）中评估具有鉴定的miRNA的这些模型的诊断性能。结果表明，前四个预测模型一致地产生100%的AUC。我们的研究结果提供了重要的证据表明，血清miRNA谱代表了一个有前途的诊断卵巢癌的生物标志物。
Ovarian cancer is the second most dangerous gynecologic cancer with a high mortality rate. The classification of gene expression data from high-dimensional and small-sample gene expression data is a challenging task. The discovery of miRNAs, a small non-coding RNA with 18–25 nucleotides in length that regulates gene expression, has revealed the existence of a new array for regulation of genes and has been reported as playing a serious role in cancer. By using LASSO and Elastic Net as embedded algorithms of feature selection techniques, the present study identified 10 miRNAs that were regulated in ovarian serum cancer samples compared to non-cancer samples in public available dataset GSE106817: hsa-miR-5100, hsa-miR-6800-5p, hsa-miR-1233-5p, hsa-miR-4532, hsa-miR-4783-3p, hsa-miR-4787-3p, hsa-miR-1228-5p, hsa-miR-1290, hsa-miR-3184-5p, and hsa-miR-320b. Further, we implemented state-of-the-art machine learning classifiers, such as logistic regression, random forest, artificial neural network, XGBoost, and decision trees to build clinical prediction models. Next, the diagnostic performance of these models with identified miRNAs was evaluated in the internal (GSE106817) and external validation dataset (GSE113486) by ROC analysis. The results showed that first four prediction models consistently yielded an AUC of 100%. Our findings provide significant evidence that the serum miRNA profile represents a promising diagnostic biomarker for ovarian cancer.
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