Pan-Cancer Metastasis Prediction Based on Graph Deep Learning Method - Xu, Yining; Cui, Xinran; Wang, Yadong (Frontiers in Cell and Developmental Biology, 2021)
Tumor metastasis is the major cause of mortality from cancer. From this perspective, detecting cancer gene-expression and transcriptome changes is important for exploring tumor metastasis molecular mechanisms and cellular events. Precisely estimating a patient’s cancer state and prognosis is the key challenge to develop patient’s therapeutic schedule. In the last years, a variety of machine-learning techniques widely contributed to analyzing real-world gene-expression data and predicting tumor outcomes. In this area, data mining and machine learning techniques have widely contributed to gene-expression data analysis by supplying computational models to supporting decision-making on real-world data. Nevertheless, limitation of real-world data extremely restricted model predictive performance, complexity of data makes it difficult to extract vital features. Besides, efficacy of standard machine learning pipelines is far from being satisfactory despite diverse feature selection strategy had been applied. To address these problems, we developed Directed Relation-Graph Convolutional Network (DR-GCN) to provide advanced feature extraction strategy. We first constructed gene regulation network and extracted gene expression features based on Relational Graph Convolutional Network (R-GCN) method. The high-dimensional features of each sample were regarded as an image pixel and Convolutional Neural Network (CNN) was implemented to predict risk of metastasis for each patient. 10 cross-validation on 1779 cases from The Cancer Genome Atlas (TCGA) show our model’s performance (AUC=0.837, AUPRC=0.717) outstands existing network-based method (AUC=0.707, AUPRC=0.555).