PMID- 28793339
OWN - NLM
STAT- MEDLINE
DCOM- 20171010
LR  - 20171010
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 12
IP  - 8
DP  - 2017
TI  - Modeling miRNA-mRNA interactions that cause phenotypic abnormality in breast
      cancer patients.
PG  - e0182666
LID - 10.1371/journal.pone.0182666 [doi]
AB  - BACKGROUND: The dysregulation of microRNAs (miRNAs) alters expression level of
      pro-oncogenic or tumor suppressive mRNAs in breast cancer, and in the long run,
      causes multiple biological abnormalities. Identification of such interactions of 
      miRNA-mRNA requires integrative analysis of miRNA-mRNA expression profile data.
      However, current approaches have limitations to consider the regulatory
      relationship between miRNAs and mRNAs and to implicate the relationship with
      phenotypic abnormality and cancer pathogenesis. METHODOLOGY/FINDINGS: We modeled 
      causal relationships between genomic expression and clinical data using a
      Bayesian Network (BN), with the goal of discovering miRNA-mRNA interactions that 
      are associated with cancer pathogenesis. The Multiple Beam Search (MBS) algorithm
      learned interactions from data and discovered that hsa-miR-21, hsa-miR-10b,
      hsa-miR-448, and hsa-miR-96 interact with oncogenes, such as, CCND2, ESR1, MET,
      NOTCH1, TGFBR2 and TGFB1 that promote tumor metastasis, invasion, and cell
      proliferation. We also calculated Bayesian network posterior probability (BNPP)
      for the models discovered by the MBS algorithm to validate true models with high 
      likelihood. CONCLUSION/SIGNIFICANCE: The MBS algorithm successfully learned miRNA
      and mRNA expression profile data using a BN, and identified miRNA-mRNA
      interactions that probabilistically affect breast cancer pathogenesis. The MBS
      algorithm is a potentially useful tool for identifying interacting gene pairs
      implicated by the deregulation of expression.
FAU - Lee, Sanghoon
AU  - Lee S
AD  - Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh,
      Pennsylvania, United States of America.
FAU - Jiang, Xia
AU  - Jiang X
AUID- ORCID: http://orcid.org/0000-0003-0349-8126
AD  - Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh,
      Pennsylvania, United States of America.
LA  - eng
GR  - R01 LM011663/LM/NLM NIH HHS/United States
PT  - Journal Article
DEP - 20170809
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
RN  - 0 (MicroRNAs)
SB  - IM
MH  - Algorithms
MH  - Bayes Theorem
MH  - Breast Neoplasms/*genetics/pathology
MH  - Female
MH  - Gene Expression Regulation, Neoplastic/genetics
MH  - Humans
MH  - MicroRNAs/*metabolism
MH  - Models, Genetic
MH  - Phenotype
MH  - Transcriptome
PMC - PMC5549916
EDAT- 2017/08/10 06:00
MHDA- 2017/10/11 06:00
CRDT- 2017/08/10 06:00
PHST- 2017/04/27 00:00 [received]
PHST- 2017/07/13 00:00 [accepted]
PHST- 2017/08/10 06:00 [entrez]
PHST- 2017/08/10 06:00 [pubmed]
PHST- 2017/10/11 06:00 [medline]
AID - 10.1371/journal.pone.0182666 [doi]
AID - PONE-D-17-16332 [pii]
PST - epublish
SO  - PLoS One. 2017 Aug 9;12(8):e0182666. doi: 10.1371/journal.pone.0182666.
      eCollection 2017.