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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
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Updated: Oct 12, 2025

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
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Modular network inference between miRNA-mRNA expression profiles using weighted co-expression network analysis.

Nisar Wani1, Debmalya Barh2,3, Khalid Raza4

  • 1Computer Science and Engineering Department, Govt. College of Engineering and Technology Safapora, Ganderbal Kashmir, J&K, India.

Journal of Integrative Bioinformatics
|November 20, 2021
PubMed
Summary

This study connects gene and microRNA regulatory networks in breast cancer. It identifies key gene and microRNA modules and their interactions, revealing pathways crucial for cancer progression.

Keywords:
gene expressionhubsmiRNAmodule detectionmodule eigengene networksnetwork inference

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Biology

Background:

  • Understanding gene regulatory mechanisms requires connecting transcriptional and post-transcriptional networks.
  • Breast cancer progression is influenced by complex gene and microRNA interactions.

Purpose of the Study:

  • To build and analyze co-expression network modules for mRNA and miRNA in breast cancer data.
  • To infer interaction networks between gene and miRNA modules.
  • To identify pathways associated with cancer progression through these interactions.

Main Methods:

  • Weighted Gene Co-expression Network Analysis (WGCNA) was used to construct gene and miRNA co-expression modules.
  • Interaction networks were inferred between specific gene and miRNA modules (turquoise module).
  • Pathway enrichment analysis was performed using the miRsystem web tool.

Main Results:

  • Significant gene and miRNA co-expression modules associated with the cancer phenotype were identified.
  • An interaction network between mRNA and miRNA hubs within the turquoise module was established.
  • Enrichment analysis revealed key cancer-associated pathways involving miRNA hubs and their targets.

Conclusions:

  • Connecting transcriptional and post-transcriptional regulatory networks provides insights into gene regulation in breast cancer.
  • Identified gene-miRNA interactions and pathways offer potential targets for understanding and treating breast cancer progression.