MicroRNA Expression Profiles for Classification and Analysis of Tumor Samples

Abstract

One kind of functional noncoding RNAs, microRNAs (miRNAs), form a class of endogenous RNAs that can have important regulatory roles in animals and plants by targeting transcripts for cleavage or translation repression. Researches on both experimental and computational approaches have shown that miRNAs indeed involve in the human cancer development and progression. However, the miRNAs that contribute more information to the distinction between the normal and tumor samples (tissues) are still undetermined. Recently, the high-throughput microarray technology was used as a powerful technique to measure the expression level of miRNAs in cells. Analyzing this expression data can allow us to determine the functional roles of miRNAs in the living cells. In this paper, we present a computational method to (1) predicting the tumor tissues using high-throughput miRNA expression profiles; (2) finding the informative miRNAs that show strong distinction of expression level in tumor tissues. To this end, we perform a support vector machine (SVM) based method to deeply examine one recent miRNA expression dataset. The experimental results show that SVM-based method outperforms other supervised learning methods such as decision trees, Bayesian networks, and backpropagation neural networks. Furthermore, by using the miRNA-target information and Gene Ontology annotations, we showed that the informative miRNAs have strong evidences related to some types of human cancer including breast, lung, and colon cancer.