Abstract

Gene expression profiles from RNA-sequencing provide a window to the activity inside tumor cells. This view has enabled researchers to identify driver genes for the pathology, but often these results fail to validate or only hold for the particular cancer at hand. In this study, we investigate a robust method for identifying cancer-related genes. Two criteria are used: a gene must show functional changes among clinically different groups of patients, and the gene must have clinical relevance. These conditions are assessed through a differential network analysis and by modelling patient survival times. Preliminary results are shown from a neuroblastoma and breast cancer dataset.