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Clinicopathological Analysis of miRNA Expression in Breast Cancer Tissues by Using miRNA In Situ Hybridization
06:01

Clinicopathological Analysis of miRNA Expression in Breast Cancer Tissues by Using miRNA In Situ Hybridization

Published on: June 7, 2016

7.1K

Modeling miRNA-mRNA interactions that cause phenotypic abnormality in breast cancer patients

Sanghoon Lee1, Xia Jiang1

  • 1Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.

Plos One
|August 10, 2017
PubMed
Abstract

Insights

This study identifies key microRNA-mRNA interactions driving breast cancer progression using a novel Bayesian Network approach. The findings reveal specific microRNAs and oncogenes involved in tumor metastasis and proliferation, offering new insights into cancer pathogenesis.

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Biology

Background:

  • MicroRNA (miRNA) dysregulation is implicated in breast cancer, affecting oncogenic and tumor-suppressive messenger RNA (mRNA) levels.
  • Identifying miRNA-mRNA interactions is crucial for understanding breast cancer pathogenesis but current methods have limitations.
  • Integrative analysis of miRNA-mRNA expression profiles is needed to link molecular interactions with clinical abnormalities.

Purpose of the Study:

  • To discover miRNA-mRNA interactions associated with breast cancer pathogenesis using causal modeling.
  • To develop and apply a Bayesian Network (BN) approach for analyzing genomic and clinical data.
  • To identify specific miRNA-mRNA pairs that influence tumor progression.

Main Methods:

  • Utilized a Bayesian Network (BN) to model causal relationships between genomic expression and clinical data.
  • Employed the Multiple Beam Search (MBS) algorithm to learn interactions from expression profile data.
  • Validated discovered models using Bayesian Network Posterior Probability (BNPP) to ensure high likelihood.

Main Results:

  • The MBS algorithm identified interactions between specific miRNAs (hsa-miR-21, hsa-miR-10b, hsa-miR-448, hsa-miR-96) and oncogenes (CCND2, ESR1, MET, NOTCH1, TGFBR2, TGFB1).
  • These interactions are linked to critical cancer processes including tumor metastasis, invasion, and cell proliferation.
  • BNPP calculations confirmed the validity and high probability of the discovered miRNA-mRNA interaction models.

Conclusions:

  • The MBS algorithm effectively analyzed miRNA and mRNA expression data using BNs to identify crucial miRNA-mRNA interactions in breast cancer.
  • The identified interactions probabilistically contribute to breast cancer pathogenesis.
  • The MBS algorithm presents a valuable tool for discovering gene pairs involved in expression deregulation and cancer development.

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