Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

Sci Rep. 2020 Aug 25;10(1):14149. doi: 10.1038/s41598-020-70941-y.


The gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in cross-validated benchmarks and for an independent dataset of the same genes under a different perturbation design. The inferred GRN captures many known regulatory interactions central to cancer-relevant processes in addition to predicting many novel interactions, some of which were experimentally validated, thus providing mechanistic insights that are useful for future cancer research.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Brain Neoplasms / genetics
  • Brain Neoplasms / pathology
  • Carcinogenesis / genetics*
  • Carcinoma, Squamous Cell / genetics
  • Carcinoma, Squamous Cell / pathology
  • Cell Line, Tumor
  • Culture Media, Serum-Free
  • Gene Knockdown Techniques
  • Gene Regulatory Networks*
  • Genes, Neoplasm*
  • Glioma / genetics
  • Glioma / pathology
  • Humans
  • Monte Carlo Method
  • RNA Interference
  • RNA, Small Interfering / genetics


  • Culture Media, Serum-Free
  • RNA, Small Interfering