M3db
Metastatic melanoma patients have a poor prognosis attributable to the underlying heterogeneity in the melanoma driver genes and altered gene expression profiles which also makes 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 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 these disease gene sets allow ranking/prioritization of different enriched dysregulated and conserved pathway terms in metastatic melanoma, in agreement with previous findings. The study results are presented as a freely available web resource M3db.
M3db hosts the predicted dysregulated pathways within two exclusive melanoma stages i.e. cutaneous metastasis (CM) and lymph node (LN) metastasis. The database contains several relevant information with respect to each each predicted pathways especially R-score which signifies the network properties deviation of a pathway in disease stage as compared to normal stage. Higher R-score means higher deviation thus dysregulation of intra-pathway genes connections. Users can also explore several the gene connectivity pattern for each pathway. Moreover predicted signalling pathways can also be analyzed separately along with hub gene analysis for each dysregulated pathway. User can start their analysis by browsing the different pathways terms or by just clicking on submit button.
cite: Kaushik A, Bhatia Y, Ali S, Gupta D (2015) Gene Network Rewiring to Study Melanoma Stage Progression
and Elements Essential for Driving Melanoma. PLoS ONE 10(11): e0142443. doi:10.1371/journal.pone.0142443
