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Identification of competitive endogenous RNAs network in breast cancer

Xiaojin Wang1, Jiahui Wan1, Zhanxiang Xu2

  • 1Department of Biochemistry and Molecular Biology, Mudanjiang Medical University, Mudanjiang, China.

Cancer Medicine
|April 2, 2019
PubMed
Abstract

Insights

This study constructed a competing endogenous RNA (ceRNA) network to identify novel breast cancer biomarkers. The network analysis revealed key molecular targets for improved breast cancer treatment and prognosis.

Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • MicroRNAs (miRNAs) regulate gene expression, and long noncoding RNAs (lncRNAs) can act as competing endogenous RNAs (ceRNAs) by binding miRNAs, affecting messenger RNA (mRNA) expression.
  • The precise mechanisms of ceRNA networks in breast cancer remain incompletely understood.

Purpose of the Study:

  • To construct a ceRNA network for breast cancer.
  • To explore novel therapeutic targets and prognostic biomarkers for breast cancer.

Main Methods:

  • Utilized The Cancer Genome Atlas (TCGA) database, analyzing 1,096 cancer tissues and 112 adjacent normal tissues.
  • Screened for significant differentially expressed mRNAs (DEMs), lncRNAs (DELs), and miRNAs (DEMis) to build the ceRNA network.
  • Performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and survival analysis.

Main Results:

  • Identified 2,139 DEMs, 1,059 DELs, and 84 DEMis, constructing a network with significant interactions.
  • GO analysis revealed distinct functions for upregulated (nucleosomes, extracellular regions) and downregulated DEMs (Z disk, muscle contraction).
  • KEGG analysis highlighted enrichment in retinol metabolism, steroid hormone biosynthesis, and tyrosine metabolism pathways. Survival analysis identified four DEMs, two DELs, and two DEMis associated with poor prognosis.

Conclusions:

  • Constructing a ceRNA network and performing survival analysis is a viable strategy for identifying significant treatment and prognosis targets and biomarkers in breast cancer.
Keywords:
GO enrichment analysisKEGG pathway analysisbreast cancerceRNA networkprognosissurvival analysis

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