Topological patterns in microRNA-gene regulatory network: studies in colorectal and breast cancer

Debarka Sengupta1, Sanghamitra Bandyopadhyay

  • 1Machine Intelligence Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata-700108, India. debarka_r@isical.ac.in

Molecular Biosystems
|March 12, 2013
PubMed

Insights

This study reveals that disease-specific networks of microRNAs (miRNAs) and transcription factors (TFs) exhibit dense, hierarchical structures. These findings offer insights into cancer progression and the development of targeted therapies.

Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • MicroRNAs (miRNAs) and transcription factors (TFs) form complex regulatory networks governing gene expression.
  • Previous research linked miRNA-TF network clusters to common diseases.
  • Identifying specific network structures that drive disease progression remains a challenge.

Purpose of the Study:

  • To analyze the topological structures of TF-miRNA-gene networks (TMG-nets) in colorectal and breast cancers.
  • To identify disease-specific subnetworks and their characteristics.
  • To develop a tool for exploring these disease-specific networks.

Main Methods:

  • Construction of a human genome-wide TF-miRNA-gene network (TMG-net) using validated and predicted interactions.
  • Extraction and analysis of cancer-specific subnetworks from the TMG-net.
  • Proposal and application of a new measure, Inductive Converge (InCov), to analyze molecular associations.

Main Results:

  • Disease-specific subnetworks are densely organized with a hierarchical, pyramid-shaped backbone.
  • Key molecules identified, including known oncomiRs like hsa-mir-210 and hsa-mir-378.
  • Dysregulated TFs in cancer are highly interconnected via miRNAs, highlighting miRNA predominance.

Conclusions:

  • The identified network structures provide insights into disease mechanisms.
  • The proposed InCov measure aids in understanding molecular associations within disease networks.
  • A web application, DisTMGneT, is developed for accessing and analyzing these disease-specific networks.

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