Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

• Motivation: Cancer is known to stem from multiple, independent mutations, the effects of which aggregate to drive the cell into a cancerous state. To understand the complex interplay between affected genes, their gene regulatory network (GRN) needs to be uncovered, to revealing detailed insights of regulatory mechanisms. We therefore decided to infer a reliable GRN from perturbation responses of 40 genes known or suspected to have a role in human cancers yet whose regulatory interactions are poorly known.Results: siRNA knockdown experiments of each gene were done in a human squamous carcinoma cell line, after which the transcriptomic response was measured. From these data GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. The best GRN was shown to be significantly more predictive than the null model, both in crossvalidated benchmarks and for an independent dataset of the same genes but subjected to double perturbations. It agrees with many known links in addition to predicting a large number of novel interactions, a subset of which were experimentally validated. The inferred GRN captures regulatory interactions central to cancer-relevant processes and thus provides mechanistic insights that are useful for future cancer research.

Subject headings

NATURVETENSKAP  -- Biologi -- Bioinformatik och systembiologi (hsv//swe)

NATURAL SCIENCES  -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)

Keyword

Biochemistry towards Bioinformatics

biokemi med inriktning mot bioinformatik

Publication and Content Type

vet (subject category)

ovr (subject category)