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Related Concept Videos

MicroRNAs01:22

MicroRNAs

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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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MicroRNAs01:22

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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...
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Network based multifactorial modelling of miRNA-target interactions.

Selcen Ari Yuka1, Alper Yilmaz1

  • 1Department of Bioengineering, Yildiz Technical University, Istanbul, Turkey.

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|March 29, 2021
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Summary
This summary is machine-generated.

This study introduces a network model to analyze competing endogenous RNA (ceRNA) interactions, revealing complex gene regulations. The model identifies key genes and miRNAs involved in breast cancer, offering insights into cellular crosstalk.

Keywords:
Network biologyNetwork modellingceRNAsmiRNA:target interaction

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Competing endogenous RNA (ceRNA) networks involve complex crosstalk between non-coding RNAs.
  • MicroRNA (miRNA):target interactions are crucial but can be indirectly affected by network dynamics.
  • Understanding ceRNA networks is vital for deciphering gene regulation.

Purpose of the Study:

  • To develop a network-based model for analyzing miRNA:ceRNA interactions and their impact on gene expression.
  • To investigate the effects of perturbations within miRNA:target networks, considering factors like binding energy.
  • To identify key regulatory elements in breast cancer using this novel approach.

Main Methods:

  • Developed a network model integrating miRNA:ceRNA interactions with gene expression data.
  • Calculated network-wide effects of expression perturbations, incorporating miRNA binding characteristics.
  • Analyzed large-scale miRNA:target networks derived from breast cancer patient data.

Main Results:

  • Identified highly perturbing genes and miRNAs that are significantly associated with breast cancer.
  • The network-based approach effectively accounts for the 'sponge effect' in miRNA regulation.
  • Unveiled intricate crosstalk between network nodes, highlighting previously unrecognized regulatory mechanisms.

Conclusions:

  • The developed network model provides a powerful tool for understanding complex ceRNA regulations.
  • It has the potential to uncover novel biological insights by considering network context.
  • The R package 'ceRNAnetsim' is scalable and adaptable for emerging RNA effectors like circRNAs and lncRNAs.