Validation of miRNAs as Breast Cancer Biomarkers with a Machine Learning Approach
Validation of miRNAs as Breast Cancer Biomarkers with a Machine Learning Approach
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DOI：
10.3390/cancers11030431
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发表时间：
2019-03-01
期刊：
影响因子：
5.2
通讯作者：
Li, Zhongwei
中科院分区：
文献类型：
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作者：
Rehman, Oneeb;Zhuang, Hanqi;Li, Zhongwei
某些小型非编码 microRNA (miRNA) 在正常组织和癌症中存在差异表达，这使得它们成为癌症生物标志物的绝佳候选者。此前，选定的 miRNA 子集已被实验证实与乳腺癌有关。在本文中，我们使用机器学习方法对 miRNA 表达数据验证了这些 miRNA 的重要性。我们使用信息增益 (IG)、卡方 (CHI2) 和最小绝对收缩和选择操作 (LASSO) 对这些相关 miRNA 集进行特征选择，以按重要性对它们进行排名。然后，我们使用随机森林 (RF) 和支持向量机 (SVM) 分类器，使用这些 miRNA 作为特征进行癌症分类。我们的结果表明，我们的分析排名较高的 miRNA 具有较高的分类器性能。随着 miRNA 排名的降低，性能也随之降低，这证实了这些 miRNA 作为生物标志物具有不同程度的重要性。此外，我们发现使用至少 3 个 miRNA 作为乳腺癌生物标志物与使用整套 1800 个 miRNA 一样有效。这项工作表明，机器学习是用于癌症检测和诊断的 miRNA 功能研究的有用工具。
Certain small noncoding microRNAs (miRNAs) are differentially expressed in normal tissues and cancers, which makes them great candidates for biomarkers for cancer. Previously, a selected subset of miRNAs has been experimentally verified to be linked to breast cancer. In this paper, we validated the importance of these miRNAs using a machine learning approach on miRNA expression data. We performed feature selection, using Information Gain (IG), Chi-Squared (CHI2) and Least Absolute Shrinkage and Selection Operation (LASSO), on the set of these relevant miRNAs to rank them by importance. We then performed cancer classification using these miRNAs as features using Random Forest (RF) and Support Vector Machine (SVM) classifiers. Our results demonstrated that the miRNAs ranked higher by our analysis had higher classifier performance. Performance becomes lower as the rank of the miRNA decreases, confirming that these miRNAs had different degrees of importance as biomarkers. Furthermore, we discovered that using a minimum of three miRNAs as biomarkers for breast cancers can be as effective as using the entire set of 1800 miRNAs. This work suggests that machine learning is a useful tool for functional studies of miRNAs for cancer detection and diagnosis.