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

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Department of Pathology, School of Medicine, Zhejiang University, Hangzhou 310058, China
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Department of Health Statistics and Epidemiology, Central South University, Changsha 410008, China
c.
School of Medicine, Hunan Normal University, Changsha 410006, China
d.
Key Laboratory of Molecular and Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China

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Corresponding author: E-mail address: lbchen@genetics.ac.cn (Liangbiao Chen)

摘要

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Abstract: 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.
- cancer classification /
- microRNA /
- mRNA /
- gene expression /
- feature selection /
- SVM

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参考文献(25)

[1]	Alizadeh, A.A., Eisen, M.B., Davis, R.E. et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling Nature, 403 (2000),pp. 503-511
[2]	Barad, O., Meiri, E., Avniel, A. et al. MicroRNA expression detected by oligonucleotide microarrays: System establishment and expression profiling in human tissues Genome Res., 14 (2004),pp. 2486-2494
[3]	Cai, Z., Goebel, R., Salavatipour, M.R. et al. Selecting dissimilar genes for multi-class classification, an application in cancer subtyping BMC Bioinformatics, 8 (2007),p. 206
[4]	Calin, G.A., Ferracin, M., Cimmino, A. et al. A microRNA signature associated with prognosis and progression in chronic lymphocytic leukemia N. Engl. J. Med., 353 (2005),pp. 1793-1801
[5]	Furey, T.S., Cristianini, N., Duffy, N. et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data Bioinformatics, 16 (2000),pp. 906-914
[6]	Guyon, I., Weston, J., Barnhill, S. et al. Gene selection for cancer classification using support vector machines Machine Learning, 46 (2002),pp. 389-422
[7]	Johnson, S.M., Grosshans, H., Shingara, J. et al. RAS is regulated by the let-7 MicroRNA family Cell, 120 (2005),pp. 635-647
[8]	Kohavi, R., John, G.H. Wrappers for feature subset selection Artificial Intelligence, 97 (1997),pp. 273-324
[9]	Lassmann, S., Kreutz, C., Schoepflin, A. et al. A novel approach for reliable microarray analysis of microdissected tumor cells from formalin-fixed and paraffin-embedded colorectal cancer resection specimens J. Mol. Med., 87 (2009),pp. 211-224
[10]	Li, J., Tang, X.L., Zhao, W. et al. A new framework for identifying differentially expressed genes Pattern Recognit., 40 (2007),pp. 3249-3262
[11]	Lin, T.C., Liu, R.S., Chen, C.Y. et al. Pattern classification in DNA microarray data of multiple tumor types Pattern Recognit., 39 (2006),pp. 2426-2438
[12]	Liu, K.H., Xu, C.G. A genetic programming-based approach to the classification of multiclass microarray datasets Bioinformatics, 25 (2009),pp. 331-337
[13]	Lu, J., Getz, G., Miska, E.A. et al. MicroRNA expression profiles classify human cancers Nature, 435 (2005),pp. 834-838
[14]	Neely, L.A., Patel, S., Garver, J. et al. A single-molecule method for the quantitation of microRNA gene expression Nat. Methods, 3 (2006),pp. 41-46
[15]	Peng, S., Xu, Q., Ling, X.B. et al. Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines FEBS Lett., 555 (2003),pp. 358-362
[16]	Ramaswamy, S., Tamayo, P., Rifkin, R. et al. Multiclass cancer diagnosis using tumor gene expression signatures Proc. Natl. Acad. Sci. USA, 98 (2001),pp. 15149-15154
[17]	Reunanen, J. Overfitting in making comparisons between variable selection methods J. Mach. Learn. Res., 3 (2003),pp. 371-1382
[18]	Schobesberger, M., Baltzer, A., Oberli, A. et al. Gene expression variation between distinct areas of breast cancer measured from paraffin-embedded tissue cores BMC Cancer, 8 (2008),p. 343
[19]	Su, A.I., Welsh, J.B., Sapinoso, L.M. et al. Molecular classification of human carcinomas by use of gene expression signatures Cancer Res., 61 (2001),pp. 7388-7393
[20]	Su, Y., Murali, T.M., Pavlovic, V. et al. RankGene: Identification of diagnostic genes based on expression data Bioinformatics, 19 (2003),pp. 1578-1579
[21]	Tusher, V.G., Tibshirani, R., Chu, G. Significance analysis of microarrays applied to the ionizing radiation response Proc. Natl. Acad. Sci. USA, 98 (2001),pp. 5116-5121
[22]	Wang, L., Zhu, J., Zou, H. Hybrid huberized support vector machines for microarray classification and gene selection Bioinformatics, 24 (2008),pp. 412-419
[23]	Xu, R., Anagnostopoulos, G.C., Wunsch, D.C. Multiclass cancer classification using semisupervised ellipsoid ARTMAP and particle swarm optimization with gene expression data IEEE/ACM Trans. Comput. Biol. Bioinform., 4 (2007),pp. 65-77
[24]	Yukinawa, N., Oba, S., Kato, K. et al. A multi-class predictor based on a probabilistic model: Application to gene expression profiling-based diagnosis of thyroid tumors BMC Genomics, 7 (2006),pp. 1-13
[25]	Zhou, X., Tuck, D.P. MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data Bioinformatics, 23 (2007),pp. 1106-1114