PLoS Computational BiologySystems BiologyPeer-reviewed journal article
Network Topology Metrics Explaining Enrichment of Hybrid Epithelial Mesenchymal Phenotypes in Metastasis
Mubasher RashidiD, Kishore HariiD, John Thampi, Nived Krishnan SanthoshiD, Mohit Kumar JollyiD
Abstract
Epithelial to mesenchymal transition (EMT) and its reverse mesenchymal to epithelial transition (MET) are hallmarks of metastasis. Cancer cells use this reversible cellular programming to switch among Epithelial (E), Mesenchymal (M), and hybrid Epithelial/Mesenchymal (hybrid E/M) state(s) and seed tumors at distant sites. Hybrid E/M cells are often more aggressive and metastatic than the "pure" E and M cells. Thus, identifying mechanisms to inhibit hybrid E/M cells can be promising in curtailing metastasis. While multiple gene regulatory networks (GRNs) based mathematical models for EMT/MET have been developed recently, identifying topological signatures enriching for hybrid E/M phenotypes remains to be done. Here, we investigate the dynamics of 13 different GRNs and identify which network topologies can enrich for hybrid E/M phenotype(s) across GRNs. We found that increasing negative feedback loops enhances the "hybridness" of these networks, but increasing the number of positive feedback loops or their specific combinations can decrease the frequency of hybrid E/M phenotype. Thus, our analysis identifies network topology based signatures that can give rise to, as well as prevent, the emergence of hybrid E/M phenotype in GRNs underlying EMP. Our results can have implications in terms of targeting specific interactions in GRNs as a potent way to restrict switching to the hybrid E/M phenotype(s) to curtail metastasis.
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
- Citations
- 25
- FWCI
- 1.55
- Field percentile
- top 17%
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