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Discovering the 'Dark matters' in expression data of miRNA based on the miRNA-mRNA and miRNA-lncRNA networks

Cong Pian1, Guangle Zhang2, Sanling Wu3

  • 1Ministry of Agriculture Key Lab of Molecular Biology of Crop Pathogens and Insects, Institute of Insect Science, Zhejiang University, Hangzhou, 310058, Zhejiang, China.

BMC Bioinformatics
|October 18, 2018
PubMed
Abstract

Insights

Researchers developed a new algorithm to find "dark matter" microRNAs (DM-miRNAs) missed by traditional analysis. This method identifies crucial non-differentially expressed miRNAs and their interactions as potential cancer biomarkers.

Area of Science:

  • Oncology
  • Bioinformatics
  • Genomics

Background:

  • MicroRNAs (miRNAs) are crucial in various cancers.
  • Differential expression analysis often misses important cancer-related miRNAs, termed 'dark matter' miRNAs (DM-miRNAs).

Purpose of the Study:

  • To develop an algorithm for discovering DM-miRNAs.
  • To identify novel cancer-related miRNAs beyond those with differential expression.

Main Methods:

  • Constructed basic miRNA-mRNA networks (BMMN).
  • Built basic miRNA-lncRNA networks (BMLN).
  • Utilized network-based approaches to mine potential DM-miRNAs.

Main Results:

  • An effective method for finding DM-miRNAs was developed.
  • miRNA-mRNA and miRNA-lncRNA interactions were identified as potential cancer biomarkers.
  • The BMMN and BMLN successfully excavated non-differentially expressed miRNAs crucial in cancer.

Conclusions:

  • BMMN and BMLN can identify important, non-differentially expressed miRNAs in cancer.
  • miRNA-mRNA and miRNA-lncRNA interactions (edge biomarkers) offer more information than individual miRNAs (node biomarkers).
  • This approach can aid in developing novel therapeutic candidates for cancer treatment.
Keywords:
BiomarkersDark mattersmiRNA-lncRNA networkmiRNA-mRNA network

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