Network Rewiring in Cancer: Applications to Melanoma Cell Lines and the Cancer Genome Atlas Patients
Year: 2018
Type: article
Abstract: Genes do not work in isolation, but rather as part of networks that have many feedback and redundancy mechanisms. Studying the properties of genetic networks and how individual genes contribute to overall network functions can provide insight into genetically-mediated disease processes. Most analytical techniques assume a network topology based on normal state networks. However, gene perturbations often lead to the rewiring of relevant networks and impact relationships among other genes. We apply a suite of analysis methodologies to assess the degree of transcriptional network rewiring observed in different sets of melanoma cell lines using whole genome gene expression microarray profiles. We assess evidence for network rewiring in melanoma patient tumor samples using RNA-sequence data available from The Cancer Genome Atlas. We make a distinction between "unsupervised" and "supervised" network-based methods and contrast their use in identifying consistent differences in networks between subsets of cell lines and tumor samples. We find that different genes play more central roles within subsets of genes within a broader network and hence are likely to be better drug targets in a disease state. Ultimately, we argue that our results have important implications for understanding the molecular pathology of melanoma as well as the choice of treatments to combat that pathology.
Source: Frontiers in Genetics
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,801
Funders: American Association for Cancer Research, Melanoma Research Alliance, Entertainment Industry Foundation, National Institutes of Health, Stand Up To Cancer, National Institute on Aging, National Institute of Mental Health, National Institute on Drug Abuse, National Cancer Institute, National Center for Advancing Translational Sciences, Janssen Pharmaceuticals
Awards: G1172940527, G2100737286, G2928778974, G4447157184, Deep sequencing studies for cannabis and stimulant dependence, G4498387036, Identifying the Polygenic Basis of the Human Brain and Psychiatric Disorders, G751602845, G772794905, G9000004289, G9000018859, G9000053782, G9000063610, G9000086042, G9000112398, G9000143299, G9000154000, G9000187487, G9000220843, G9000222291, G949115552
OpenAlex ID: W2868192553