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Related Experiment Video

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Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells
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Exploring novel key regulators in breast cancer network.

Shahnawaz Ali1,2, Md Zubbair Malik1,2, Soibam Shyamchand Singh2

  • 1Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi-110025, India.

Plos One
|June 22, 2018
PubMed
Summary

This study reveals breast cancer networks exhibit fractal properties and modular organization. Key regulators, including p53, were identified, highlighting efficient communication and potential therapeutic targets within the network.

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Area of Science:

  • Systems Biology
  • Network Science
  • Genomics

Background:

  • Understanding the complex interactions of genes in breast cancer is crucial for identifying therapeutic targets.
  • Network theory provides a framework to analyze gene interactions and network topology.

Purpose of the Study:

  • To analyze the topological properties of a breast cancer gene network.
  • To identify key regulatory genes and understand communication pathways within the network.

Main Methods:

  • Construction of a breast cancer gene network using 70 experimentally verified genes.
  • Application of network theoretical approaches, including topological parameter analysis and local community paradigm.
  • Analysis of fractal rules, centrality, and hub characteristics.

Main Results:

  • The breast cancer network exhibits a hierarchical, scale-free nature with modular organization and divergent hubs.
  • Topological parameters follow fractal rules, indicating efficient communication and absence of a strict centrality-lethality rule.
  • Key regulators were identified, with p53 showing cross-talk with BRCA2 and BRCA3 despite its low network rank.
  • Hub popularity changes unpredictably, demonstrating disassortive characteristics.

Conclusions:

  • The breast cancer network possesses a robust, fractal architecture facilitating efficient communication.
  • Identification of key regulators and their interactions provides potential targets for breast cancer therapy.
  • The network's modular organization suggests distinct functional modules with varying communication efficiencies.