Predicting neuroblastoma using developmental signals and a logic-based model.

ABSTRACT: Genomic information from human patient samples of pediatric neuroblastoma cancers and known outcomes have led to specific gene lists put forward as high risk for disease progression. However, the reliance on gene expression correlations rather than mechanistic insight has shown limited potential and suggests a critical need for molecular network models that better predict neuroblastoma progression. In this study, we construct and simulate a molecular network of developmental genes and downstream signals in a 6-gene input logic model that predicts a favorable/unfavorable outcome based on the outcome of the four cell states including cell differentiation, proliferation, apoptosis, and angiogenesis. We simulate the mis-expression of the tyrosine receptor kinases, trkA and trkB, two prognostic

SUBMITTER: Kasemeier-Kulesa JC 

PROVIDER: S-EPMC6016551 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature