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.
Highlights
Many studies leveraging genomic assays explore associations between genes and diseases
As noted in the Methods section, we extracted genes from the WikiPathway version of the MAPK pathway (n = 224). These 224 genes were mapped to the probesets used in the Affymetrix gene expression chips applied to the CCLE and Translational Genomics Research Institute (TGen) cell lines at our disposal for these analyses based on the maximum average intensity for each gene (Supplemental Table 2)
There has been a great deal of attention given to the development and use of pathway and genetic network analysis tools in understanding disease pathogenesis and drug targeting, these tools often rely upon the use of pathway and network information derived from analyses of genes in normal and non-diseased cells, cell lines, and tissues (St Onge et al, 2007; Califano, 2011; Ideker and Krogan, 2012; Hofree et al, 2013)
Summary
Many studies leveraging genomic assays explore associations between genes and diseases. If any associations are found strategies to make broader claims about genetically-mediated processes, networks, and pathways that influence those diseases are pursued; for example, by studying the expression levels or protein function of disease-associated genes. Expressed genes could be extracted to determine if all, or some subset, of those genes participate in a coherent network or contribute to particular processes affecting disease pathogenesis. One strategy for making claims about whether or not the differentially expressed genes (DEGs) all participate in a particular network is to compare a list of DEGs to databases, such as Kyoto Encyclopedia of Genes and Genomes (Kanehisa and Goto, 2000) (KEGG) or WikiPathways (Pico et al, 2008; Kelder et al, 2012; Kutmon et al, 2016), which provide network, function, or pathway information associated with each gene. If the DEGs match lists of genes in, e.g., known networks, it can be inferred that those networks are likely to be contributing to the pathogenesis of the disease (Costanzo et al, 2010; Blomen et al, 2015)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
