Long non-coding RNA based cancer classification using deep neural networks

Abstract

Recent studies indicate that lncRNA plays key roles in tumorigene-sis and misexpression of lncRNAs can lead to change in expression profiles of various target genes involved in different aspects of cancer progression. However, research on classifying multiple cancer types using only lncRNA is rarely found. In this paper, we explored the capability of lncRNA in classifying cancer types by employing four deep neural networks - multi-layer perceptron (MLP), long-short-term memory (LSTM), convolutional neural network (CNN) and deep autoencoder (DAE). For experiment, RNA-seq expression values from TCGA for 8 cancers - BLCA, CESC, COAD, HNSC, KIRP, LGG, LIHC, and LUAD - are used. The combined dataset consists of 3656 patients with expression values for 12309 lncRNAs. The performance of the models in terms of accuracy ranges from 94% to 98%, which shows lncRNA expression profiles as the better signature compared to the mRNA expression profiles in classifying cancer types.