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Updated: Dec 10, 2025

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

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Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

Daniel Morgan1, Matthew Studham1, Andreas Tjärnberg1,2

  • 1Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121, Solna, Sweden.

Scientific Reports
|August 27, 2020
PubMed

Insights

This study infers gene regulatory networks (GRNs) in cancer cells to uncover disease mechanisms. The developed computational framework accurately predicts regulatory interactions, offering new insights for cancer therapy development.

Area of Science:

  • Systems biology
  • Genomics
  • Computational biology

Background:

  • Gene regulatory networks (GRNs) govern cellular functions.
  • Dysregulation of GRNs is linked to diseases like cancer.
  • Understanding GRNs can reveal novel disease mechanisms and therapeutic targets.

Purpose of the Study:

  • To infer cancer-relevant gene regulatory networks (GRNs).
  • To identify novel regulatory interactions in squamous carcinoma.
  • To provide mechanistic insights for cancer research and therapy development.

Main Methods:

  • Applied a computational inference framework to perturbation data from A431 squamous carcinoma cells.
  • Inferred GRNs using multiple methods with false discovery rate control via the NestBoot framework.
  • Developed a novel approach to assess GRN predictiveness against validation data.

Main Results:

  • The best inferred GRN demonstrated significantly higher predictiveness than a null model.
  • Predictiveness was validated through cross-validated benchmarks and an independent dataset.
  • The GRN captured known cancer-relevant interactions and predicted novel, experimentally validated interactions.

Conclusions:

  • Inferred GRNs provide valuable mechanistic insights into cancer biology.
  • The computational framework effectively identifies predictive regulatory interactions.
  • This approach aids in discovering novel therapeutic strategies for cancer.

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