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

Abstract

Highlights: We adopt 'tipping-point' theory to identify distribution-transition in disease regulatory states  Critical transcriptional transition happens between low-risk and a high-risk neuroblastoma states  A critical transition signal (CTS) based on coherent expression of genes and lncRNAs shows prognostic significance  GWAS-scans with the CTS unveiled five overlooked genes and four lncRNAs with promising clinical implications  We propose a CTS-amplifier model that unveils complex but mastering transregulation in disease AbstractTipping-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 2 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.