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Updated: May 13, 2026

Molecular Profiling of the Invasive Tumor Microenvironment in a 3-Dimensional Model of Colorectal Cancer Cells and Ex vivo Fibroblasts
Published on: April 29, 2014
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
Abstract:
It is now widely accepted that microRNAs (miRNAs or miRs) along with transcription factors (TFs) weave a complex inter-regulatory network within the cell that is responsible for the combinatorial regulation of gene expression. Recently we have shown that miRNAs and TFs that form network clusters are also associated with a number of common diseases. However, the quest persists to find out topological structures that facilitate disease progression. In the current work we choose colorectal and breast cancers for our analysis. For this, the human genome wide TF-miRNA-gene network (TMG-net) is first built by combining experimentally validated and confidently predicted miRNA→gene (including TF genes), TF→gene and TF→miRNA interactions. Subnetworks active in colorectal and breast cancers are extracted from the TMG-net and then analyzed. Disease specific subnetworks are found to be significantly dense, having a pyramid shaped hierarchical backbone of interactions. Interestingly, most of the top level molecules (e.g., hsa-mir-210, hsa-mir-378) are found to be already established as oncomirs. TFs that are dysregulated in a particular cancer, are found to be well-linked via miRNAs and other TFs, with miRNAs being highly predominant. Analogous to density, a new measure called Inductive Converge (InCov) is proposed and used to analyze the natural association of molecules in the disease specific networks. Finally a web application called DisTMGneT (Disease Specific TF-miRNA-gene Network) is developed for disease specific subnetworks from the TMG-net, based on user supplied sets of dysregulated miRNAs, TFs and non TF genes. DisTMGneT is available at http://www.isical.ac.in/bioinfo_miu/dscsgen.php.
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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