Peng Sihua, Zeng Xiaomin, Li Xiaobo, Peng Xiaoning, Chen Liangbiao
Department of Pathology, School of Medicine, Zhejiang University, Hangzhou 310058, China.
J Genet Genomics. 2009 Jul;36(7):409-16. doi: 10.1016/S1673-8527(08)60130-7.
Both microRNA (miRNA) and mRNA expression profiles are important methods for cancer type classification. A comparative study of their classification performance will be helpful in choosing the means of classification. Here we evaluated the classification performance of miRNA and mRNA profiles using a new data mining approach based on a novel SVM (Support Vector Machines) based recursive feature elimination (nRFE) algorithm. Computational experiments showed that information encoded in miRNAs is not sufficient to classify cancers; gut-derived samples cluster more accurately when using mRNA expression profiles compared with using miRNA profiles; and poorly differentiated tumors (PDT) could be classified by mRNA expression profiles at the accuracy of 100% versus 93.8% when using miRNA profiles. Furthermore, we showed that mRNA expression profiles have higher capacity in normal tissue classifications than miRNA. We concluded that classification performance using mRNA profiles is superior to that of miRNA profiles in multiple-class cancer classifications.
微小RNA（miRNA）和信使核糖核酸（mRNA）表达谱都是癌症类型分类的重要方法。对它们的分类性能进行比较研究将有助于选择分类方法。在此，我们使用一种基于新型支持向量机（SVM）的递归特征消除（nRFE）算法的新数据挖掘方法，评估了miRNA和mRNA谱的分类性能。计算实验表明，miRNA中编码的信息不足以对癌症进行分类；与使用miRNA谱相比，使用mRNA表达谱时，肠道来源的样本聚类更准确；对于低分化肿瘤（PDT），使用mRNA表达谱的分类准确率为100%，而使用miRNA谱时为93.8%。此外，我们表明mRNA表达谱在正常组织分类中的能力高于miRNA。我们得出结论，在多类癌症分类中，使用mRNA谱的分类性能优于miRNA谱。
