Cancer Subtype Classification based on Superlayered Neural Network

Abstract

Targeted treatment on different cancer subtype has been of clinical interest. However, accurate molecular identification of pathological cancer subtypes has been challenging. To meet such needs, a new subtype classification model based on deep neural network, Sparse Cross-modal Superlayered Neural Network is presented in this study. The model focuses on selecting biomarkers with considering integration of the high dimensional RNA sequencing data and DNA methylation data. For a real data application, the multi-omics data of lung adenocarcinoma and squamous cell carcinoma from The Cancer Genomic Atlas was fitted to the proposed model. Our model was compared to other existing methods such as principal component analysis, penalized logistic regression, and artificial neural network. With only a small number of biomarkers, the proposed model was able to effectively classify these lung cancer subtypes. Gene set analysis of selected biomarkers revealed significant difference in epidermis development and cornification pathway activation level between two lung cancer subtypes.