Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection
Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection
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DOI：
10.1186/s12859-019-3050-8
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发表时间：
2019-09-18
影响因子：
3
通讯作者：
Tonda, Alberto
中科院分区：
文献类型：
--
作者：
Lopez-Rincon, Alejandro;Martinez-Archundia, Marlet;Tonda, Alberto
背景：微小RNA（microRNAs，miRNAs）是一种非编码RNA分子，与人类肿瘤密切相关，但很少在人体内循环。找到肿瘤相关的miRNA特征，即用于区分不同类型的癌症和正常组织的最小miRNA实体，是至关重要的。机器学习中的特征选择技术可以帮助，但他们往往提供幼稚或有偏见的results.Results：提出了一个集成的miRNA签名的特征选择策略。基于来自不同类型学的高精度分类器的特征相关性的共识来选择miRNA。该方法旨在识别在临床相关预测任务中使用时更加稳健和可靠的特征。使用所提出的方法，在从TCGA提取的8023个样本的数据集中识别出100-miRNA签名。当运行八个最先进的分类器沿着与100-miRNA签名对原始1046个特征时，可以检测到全局准确度仅相差1.4%。重要的是，这种100-miRNA特征足以区分肿瘤和正常组织。然后，该方法与其他特征选择方法，如UFS，RFE，EN，LASSO，遗传算法和EFS-CLA进行比较。该方法在不同分类器的10倍交叉验证上进行测试时具有更好的准确性，并将其应用于不同平台上的几个GEO数据集，其中一些分类器的分类准确率超过90%，证明了其跨平台适用性。100-miRNA签名足够稳定，可以提供与完整TCGA数据集几乎相同的分类准确性，并且在几个GEO数据集上进一步验证，不同类型的癌症和平台。此外，文献分析证实，签名中的100种miRNA中有77种以茎环或成熟序列形式出现在癌症研究中使用的循环miRNA列表中。剩下的23个miRNAs为未来的研究提供了潜在的有希望的途径。
Background: MicroRNAs (miRNAs) are noncoding RNA molecules heavily involved in human tumors, in which few of them circulating the human body. Finding a tumor-associated signature of miRNA, that is, the minimum miRNA entities to be measured for discriminating both different types of cancer and normal tissues, is of utmost importance. Feature selection techniques applied in machine learning can help however they often provide naive or biased results.Results: An ensemble feature selection strategy for miRNA signatures is proposed. miRNAs are chosen based on consensus on feature relevance from high-accuracy classifiers of different typologies. This methodology aims to identify signatures that are considerably more robust and reliable when used in clinically relevant prediction tasks. Using the proposed method, a 100-miRNA signature is identified in a dataset of 8023 samples, extracted from TCGA. When running eight-state-of-the-art classifiers along with the 100-miRNA signature against the original 1046 features, it could be detected that global accuracy differs only by 1.4%. Importantly, this 100-miRNA signature is sufficient to distinguish between tumor and normal tissues. The approach is then compared against other feature selection methods, such as UFS, RFE, EN, LASSO, Genetic Algorithms, and EFS-CLA. The proposed approach provides better accuracy when tested on a 10-fold cross-validation with different classifiers and it is applied to several GEO datasets across different platforms with some classifiers showing more than 90% classification accuracy, which proves its cross-platform applicability.Conclusions: The 100-miRNA signature is sufficiently stable to provide almost the same classification accuracy as the complete TCGA dataset, and it is further validated on several GEO datasets, across different types of cancer and platforms. Furthermore, a bibliographic analysis confirms that 77 out of the 100 miRNAs in the signature appear in lists of circulating miRNAs used in cancer studies, in stem-loop or mature-sequence form. The remaining 23 miRNAs offer potentially promising avenues for future research.