Identifying survival subtypes with autoencoder using multiple types of high-dimensional genomic data from studies of...

Analysis of multiple types of omics data facilitates a comprehensive revelation of molecular-level complexity and interactions among genomic features. This knowledge promotes the development of new therapies for treating different genomic diseases. An integrative study of multiple types of genomic data instead of a single type of genomic data will be more informative in understanding the complicated molecular activities and their interactions. In this work, we integrated RNA-sequencing (RNA-seq), methylation, and DNA copy number variation data, downloaded from the TCGA public repository, of glioblastoma multiforme (GBM), reduced the dimension of these high-dimensional genomic data using an autoencoder, a deep learning-based method, and then used Cox-PH model to select the autoencoder-transformed features that have a significant contribution to patient survival. We utilized the significant set of autoencoder-transformed features to classify the survival subtypes using the integrated data. We built a classification model with a penalization technique, sparse group LASSO, and evaluated the approach using cross-validation. As a result, two survival subgroups, with overall different survival profiles and linking to various genomic features, are discovered for respective GBM patients. Finally, the results are interpreted biologically by differential expression analysis and pathway analysis.