Multi-class cancer classification through gene expression profiles: microRNA versus mRNA  

作　　者：Sihua Peng Xiaomin Zeng Xiaobo Li Xiaoning Peng Liangbiao Chen 

机构地区：[1]Department of Pathology, School of Medicine, Zhejiang University, Hangzhou 310058, China [2]Department of Health Statistics and Epidemiology, Central South University, Changsha 410008, China [3]School of Medicine, Hunan Normal University, Changsha 410006, China [4]Key Laboratory of Molecular and Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China

出　　处：《Journal of Genetics and Genomics》2009年第7期409-416,共8页遗传学报（英文版）

基　　金：supported by a grant from the National High-tech R&D Program (863 Program, No. 2006AA02Z331) to Liangbiao Chen

摘　　要：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 fea- ture 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.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 fea- ture 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.

关 键 词：cancer classification MICRORNA MRNA gene expression feature selection SVM 

分 类 号：R73[医药卫生—肿瘤]