Differential C3NET reveals disease networks of direct physical interactions

Abstract

BackgroundGenes might have different gene interactions in different cell conditions, which might be mapped into different networks. Differential analysis of gene networks allows spotting condition-specific interactions that, for instance, form disease networks if the conditions are a disease, such as cancer, and normal. This could potentially allow developing better and subtly targeted drugs to cure cancer. Differential network analysis with direct physical gene interactions needs to be explored in this endeavour.ResultsC3NET is a recently introduced information theory based gene network inference algorithm that infers direct physical gene interactions from expression data, which was shown to give consistently higher inference performances over various networks than its competitors. In this paper, we present, DC3net, an approach to employ C3NET in inferring disease networks. We apply DC3net on a synthetic and real prostate cancer datasets, which show promising results. With loose cutoffs, we predicted 18583 interactions from tumor and normal samples in total. Although there are no reference interactions databases for the specific conditions of our samples in the literature, we found verifications for 54 of our predicted direct physical interactions from only four of the biological interaction databases. As an example, we predicted that RAD50 with TRF2 have prostate cancer specific interaction that turned out to be having validation from the literature. It is known that RAD50 complex associates with TRF2 in the S phase of cell cycle, which suggests that this predicted interaction may promote telomere maintenance in tumor cells in order to allow tumor cells to divide indefinitely. Our enrichment analysis suggests that the identified tumor specific gene interactions may be potentially important in driving the growth in prostate cancer. Additionally, we found that the highest connected subnetwork of our predicted tumor specific network is enriched for all proliferation genes, which further suggests that the genes in this network may serve in the process of oncogenesis.ConclusionsOur approach reveals disease specific interactions. It may help to make experimental follow-up studies more cost and time efficient by prioritizing disease relevant parts of the global gene network.

Integrative approaches for finding modular structure in biological networks

“…For instance, we applied an approach termed differential epistasis mapping (dE-MAP) to compare genetic networks induced by different types of DNA damaging agents 27,141 . In another example, gene co-expression networks from transcriptomic profiles of normal or prostate cancer samples were compared to identify subnetworks induced in prostate cancer 142 . Differential, but not static networks, in this study successfully recognized known prostate cancer-specific interactions for RAD50 and TRF2.…”

Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods

“…More precisely, several attempts have been made to identify disease networks [110,111] that corresponds to particular pathways. For instance, in [110] the C3NET inference method [116,117] has been used to infer pathway specific networks for prostate cancer. A structural comparison between the pathway-specific networks, similar to [106] which is based on testing the hypothesis in Eqn.…”

Gene Regulatory Cross Networks: Inferring Gene Level Cell-to-Cell Communications of Immune Cells

“…It is worth mentioning that, raw GRCN parts are inspired from Ac3net [29] and C3NET [9] of single cell type GRNI algorithms. Moreover, differential analysis part is also inspired from DC3NET [30]. GRCNone is based on modification, adaption and integration of those well-established single cell type GRNI algorithms for GRCN inference.…”