Gene expression data represents a unique challenge in predictive model building, because of the small number of samples (n) compared to the huge amount of features (p). This ``n< < p'' property has hampered application of deep learning techniques for disease outcome classification. Sparse learning by incorporating external gene network information could be a potential solution to this issue. Still, the problem is very challenging because (1) there are tens of thousands of features and only hundreds of training samples, (2) the scale-free structure of the gene network is unfriendly to the setup of convolutional neural networks. To address these issues and build a robust classification model, we propose methods to integrate external relational information and correlations of features into the deep neural network architecture. The methods are able to achieve sparse connection between network layers to prevent overfitting. We validated the methods on synthetic and real RNAseq data.