title: Gene Network Rewiring to Study Melanoma Stage Progression and Elements Essential for Driving Melanoma.
abstract: Metastatic melanoma patients have a poor prognosis, mainly attributable to the underlying heterogeneity in melanoma driver genes and altered gene expression profiles. These characteristics of melanoma also make the development of drugs and identification of novel drug targets for metastatic melanoma a daunting task. Systems biology offers an alternative approach to re-explore the genes or gene sets that display dysregulated behaviour without being differentially expressed. In this study, we have performed systems biology studies to enhance our knowledge about the conserved property of disease genes or gene sets among mutually exclusive datasets representing melanoma progression. We meta-analysed 642 microarray samples to generate melanoma reconstructed networks representing four different stages of melanoma progression to extract genes with altered molecular circuitry wiring as compared to a normal cellular state. Intriguingly, a majority of the melanoma network-rewired genes are not differentially expressed and the disease genes involved in melanoma progression consistently modulate its activity by rewiring network connections. We found that the shortlisted disease genes in the study show strong and abnormal network connectivity, which enhances with the disease progression. Moreover, the deviated network properties of the disease gene sets allow ranking/prioritization of different enriched, dysregulated and conserved pathway terms in metastatic melanoma, in agreement with previous findings. Our analysis also reveals presence of distinct network hubs in different stages of metastasizing tumor for the same set of pathways in the statistically conserved gene sets. The study results are also presented as a freely available database at http://bioinfo.icgeb.res.in/m3db/. The web-based database resource consists of results from the analysis presented here, integrated with cytoscape web and user-friendly tools for visualization, retrieval and further analysis.
authors.affiliation: Bioinformatics Laboratory, Structural and Computational Biology Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, 110067, India.
authors.forename: Abhinav
authors.initials: A
authors.lastname: Kaushik
authors.affiliation: Bioinformatics Laboratory, Structural and Computational Biology Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, 110067, India.
authors.forename: Yashuma
authors.initials: Y
authors.lastname: Bhatia
authors.affiliation: Department of Biochemistry, Faculty of Science, Jamia Hamdard, New Delhi, 110062, India.
authors.forename: Shakir
authors.initials: S
authors.lastname: Ali
authors.affiliation: Bioinformatics Laboratory, Structural and Computational Biology Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, 110067, India.
authors.forename: Dinesh
authors.initials: D
authors.lastname: Gupta
mesh_terms: D019295:Computational Biology; D016208:Databases, Factual; D018450:Disease Progression; D053263:Gene Regulatory Networks; D006801:Humans; D008545:Melanoma / Q000235:genetics / Q000378:metabolism / Q000473:pathology*; D009362:Neoplasm Metastasis; D015398:Signal Transduction / Q000235:genetics; D059467:Transcriptome; D014584:User-Computer Interface