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Updated: Aug 16, 2025

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
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A network-based method for identifying cancer driver genes based on node control centrality.

Feng Li1, Han Li1, Junliang Shang1

  • 1School of Computer Science, Qufu Normal University, Rizhao 276826, China.

Experimental Biology and Medicine (Maywood, N.J.)
|December 27, 2022
PubMed
Summary
This summary is machine-generated.

A new network-based method (NMDGCC) identifies both coding and non-coding cancer driver genes. This approach improves upon existing methods and reveals key drivers in breast cancer, including specific subtypes.

Keywords:
Cancerdriver geneinteraction networknode control centrality

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Area of Science:

  • Biomedical research
  • Cancer genomics
  • Computational biology

Background:

  • Cancer remains a leading cause of mortality globally.
  • Identifying cancer driver genes is crucial for understanding cancer progression.
  • Existing computational models often overlook non-coding cancer drivers.

Purpose of the Study:

  • To propose a novel method, Network-based Method for identifying cancer Driver Genes based on node Control Centrality (NMDGCC), for identifying both coding and non-coding cancer drivers.
  • To enhance the accuracy and scope of cancer driver gene identification.
  • To investigate cancer drivers in specific subtypes of breast cancer.

Main Methods:

  • Construction of a gene interaction network using mRNA and miRNA expression data from cancer samples.
  • Application of node control centrality to identify cancer drivers within the constructed network.
  • Validation using breast cancer datasets from The Cancer Genome Atlas (TCGA).

Main Results:

  • NMDGCC demonstrates superior performance compared to existing cancer driver identification methods.
  • Identified 295 miRNAs as non-coding cancer drivers, with 158 linked to BRCA tumorigenesis.
  • Successfully identified cancer drivers specific to different breast cancer subtypes.

Conclusions:

  • NMDGCC is an effective method for identifying both coding and non-coding cancer driver genes.
  • The method provides valuable insights into breast cancer tumorigenesis and subtype-specific drivers.
  • NMDGCC advances the field of cancer genomics and personalized medicine.