Cancer Stage Prediction From Gene Expression Data Using Weighted Graph Convolution Network

Abstract

The early detection of cancer stage is a crucial step for effective treatment. In contrast to traditional approaches, RNA -Seq is the current state of the art technique for gene expression estimation. RNA -Seq data have been used in research and in production as input data for several classification and prediction models in many disease including cancer staging. We present a novel cancer stage prediction approach based on gene expression data. Our approach is based on Weighted Graph Convolution Networks (GCN). GCN is the application of deep learning back-propagation on graph structures. In this work, we used correlation between genes to generate a gene network graph. A neural network model with weighted graph convolution layer was trained to predict the cancer stage for cancer patients. We employed the Kidney Renal Clear Cell Carcinoma dataset (TCGA-KIRC) from the Human Cancer Genome Atlas (TCGA) to predict the cancer stage for each patient. The TCGA-KIRC dataset includes 4 cancer stages, I, II, III, and IV. We generated a binary classification problem where stages I and II are classified as “early cancer stage” and stages III and IV are classified as “late cancer stage”. We compared our approach to the state of the art approaches such as random forest and support vector machine. Our approach achieved an accuracy of 82% which outperformed existing approaches with more than 3% increase.