Feature Selection and Classification Reveal Key lncRNAs for Multiple Cancers
Feature Selection and Classification Reveal Key lncRNAs for Multiple Cancers
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DOI：
10.1109/bibm47256.2019.8983413
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Abdullah Al Mamun;A. Mondal
中科院分区：
文献类型：
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作者：
Abdullah Al Mamun;A. Mondal
长链非编码RNA（lncRNA）在肿瘤发生中起着重要作用。lncRNA的错误表达可导致各种靶基因表达谱的改变，这些靶基因参与癌症的发生和发展。因此，确定癌症的关键lncRNA将有助于开发癌症治疗方法。通常，为了鉴定癌症的关键lncRNA，需要正常和癌症样品的lncRNA的表达谱。但是，这种数据并不适用于所有癌症。在本研究中，开发了一个计算框架，仅使用癌症患者的lncRNA表达来识别癌症特异性关键lncRNA。该框架包括两种最先进的特征选择技术-递归特征消除（RFE）和最小绝对收缩和选择算子（LASSO）;以及五种机器学习模型-朴素贝叶斯，K最近邻，随机森林，支持向量机和深度神经网络。对于实验，使用来自TCGA的8种癌症- BLCA、CESC、COAD、HNSC、KIRP、LGG、LIHC和LUAD的lncRNA表达值。合并的数据集由3，656名患者组成，表达值为12，309个lncRNA。通过使用特征选择算法RFE和LASSO鉴定重要特征或关键lncRNA。通过五种分类模型的性能检查这些关键lncRNA在分类8种不同癌症中的能力。这项研究确定了37个关键的lncRNA，可以对8种不同的癌症类型进行分类，准确率从94%到97%不等。最后，生存分析支持发现的关键lncRNA能够区分高风险和低风险患者。
Long noncoding RNA (lncRNA) plays key roles in tumorigenesis. Misexpression of lncRNA can lead to changes in expression profiles of various target genes, which are involved in cancer initiation and progression. So, identifying key lncRNAs for a cancer would help develop the cancer therapy. Usually, to identify key lncRNAs for a cancer, expression profiles of lncRNAs for normal and cancer samples are required. But, this kind of data are not available for all cancers. In the present study, a computational framework is developed to identify cancer specific key lncRNAs using the lncRNA expression of cancer patients only. The framework consists of two state-of-the-art feature selection techniques - Recursive Feature Elimination (RFE) and Least Absolute Shrinkage and Selection Operator (LASSO); and five machine learning models - Naive Bayes, K-Nearest Neighbor, Random Forest, Support Vector Machine, and Deep Neural Network. For experiment, expression values of lncRNAs for 8 cancers - BLCA, CESC, COAD, HNSC, KIRP, LGG, LIHC, and LUAD - from TCGA are used. The combined dataset consists of 3,656 patients with expression values of 12,309 lncRNAs. Important features or key lncRNAs are identified by using feature selection algorithms RFE and LASSO. Capability of these key lncRNAs in classifying 8 different cancers is checked by the performance of five classification models. This study identified 37 key lncRNAs that can classify 8 different cancer types with an accuracy ranging from 94% to 97%. Finally, survival analysis supports that the discovered key lncRNAs are capable of differentiating between high-risk and low-risk patients.