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Identifying miRNA synergism using multiple-intervention causal inference.

Junpeng Zhang1,2, Vu Viet Hoang Pham3, Lin Liu3

  • 1Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan, China.

BMC Bioinformatics
|December 29, 2019
PubMed
Summary
This summary is machine-generated.

We developed miRsyn, a novel framework to infer microRNA (miRNA) synergism using causal inference on observational data. miRsyn reveals that shared miRNA targets don't always mean synergistic activity, identifying key miRNA networks in breast cancer.

Keywords:
Breast cancerMultiple intervention causal inferencemiRNAmiRNA synergistic modulemiRNA synergistic network

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Area of Science:

  • Genomics
  • Computational Biology
  • Systems Biology

Background:

  • Understanding microRNA (miRNA) synergism is crucial for deciphering complex human diseases.
  • Current methods often assume miRNA synergism based on shared sequence-level targets, which may not reflect actual coordinated gene regulation.
  • Experimental validation of miRNA synergism through multiple knock-downs is often impractical due to the large number of combinations.

Purpose of the Study:

  • To present a novel computational framework, miRsyn, for inferring miRNA synergism.
  • To differentiate between miRNAs with shared targets and those exhibiting true synergistic activity at the expression level.
  • To explore the biological relevance and network properties of identified miRNA synergism, particularly in the context of breast cancer.

Main Methods:

  • Developed miRsyn, a framework employing causal inference to mimic multiple miRNA intervention experiments using observational data.
  • Applied miRsyn to analyze miRNA expression patterns and identify synergistic miRNA pairs.
  • Compared the performance of the proposed multiple-intervention causal inference method against single-intervention methods.

Main Results:

  • Identified that several miRNA-miRNA pairs with shared sequence targets do not exhibit synergistic activity at the expression level.
  • Characterized the inferred miRNA synergistic network as small-world and biologically meaningful.
  • Found significant enrichment of miRNA synergistic modules in breast cancer, with most synergistic pairs showing similar expression patterns.
  • Demonstrated superior performance of the multiple-intervention causal inference method over single-intervention methods.

Conclusions:

  • miRsyn is a promising framework for accurately identifying miRNA synergism.
  • The findings enhance the understanding of miRNA-miRNA interactions and their role in gene regulation.
  • The study provides valuable insights into miRNA synergism in breast cancer, paving the way for further research and potential therapeutic strategies.