Automatic network generation describes differential gene data in user friendly and expeditiously analyzable network views

Abstract

Cancer is a group of diseases that involves abnormal cell growth, resulting from genetic perturbations in signaling mechanisms. High resolution RNAseq and microarray assays enable the evaluation of the transcriptional activity of high number of signaling molecules. Furthermore, many signaling pathways are described in publically available databases. Today's challenge lies in the connection of signaling pathways and signaling data to produce predictive models which have the power to validate and identify targets in disease treatment. Curating networks manually can be exhaustive handiwork. We designed an ensemble approach of gene set enrichment on seven pathway databases. It generates a basic gene set mapping of the complex input data on comprehensive pathways. Using two publically available protein-protein interaction databases, the novel algorithm automatically reconstructs a comprehensive biological system representation from these mappings. The reconstruction was based on a newly shortest path algorithm. Using a microarray data set from hepatocellular cancer cells as input, a network with well-known cancer signaling mechanisms was derived. Furthermore, nodes accounting for hormone signaling were found as being modified in liver cancer that can be used as future research targets. Two recent publically available networks were adequately inferred when testing the method to reconstruct manually curated signaling networks. Finally, our method shows that integration of raw data and publically available knowledge expeditiously generates convenient and analyzable network views.