Tipping-point analysis uncovers critical transition signals from gene expression profiles

Tipping-point models have had success identifying transcriptional critical-transition signal (CTS) in tumor phenotypes using time-course data. Can these mathematical models be adopted to cross-sectional transcriptome profiles? Furthermore, can the CTS analysis that characterizes tumor progression be applied to lncRNA-expression patterns? This study introduces a novel network-perturbation signature (NPS) scoring scheme to model a phenotype-defined tumor regulatory system. Applying NPS to neuroblastoma transcriptome of two patient populations yielded two CTSs that reproducibly identified a critical system transition between the low-risk and a high-risk state. The coherent expression pattern of one specific CTS, consisting of mRNA and lncRNA components, showed prognostic significance. Associating GWAS-scans with the CTS unveiled four overlooked intergenic loci and five genes with promising clinical significance. Additionally, a new mechanism of CTS-amplifier is proposed, modeling how CTS-transcript fluctuation response to complex master regulators such as c-MYC and HNF4A uniquely in the transition state. Overall, NPS is a powerful computational approach that provides a breakthrough in phenomenological analysis of collective regulatory trajectory by applying tipping-point theory to -omics data.nnHighlightsO_LIWe adopt tipping-point theory to identify distribution-transition in disease regulatory statesnC_LIO_LICritical transcriptional transition happens between low-risk and a high-risk neuroblastoma statesnC_LIO_LIA critical transition signal (CTS) based on coherent expression of genes and lncRNAs shows prognostic significancenC_LIO_LIGWAS-scans with the CTS unveiled five overlooked genes and four lncRNAs with promising clinical implicationsnC_LIO_LIWe propose a CTS-amplifier model that unveils complex but mastering trans-regulation in diseasenC_LI

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