Dataset Information

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

ABSTRACT: 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.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.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.

SUBMITTER: Lee S

PROVIDER: S-EPMC5549916 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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

Lee Sanghoon S Jiang Xia X

PloS one 20170809 8

<h4>Background</h4>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 a ...[more]

PMID: 28793339

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Project description:In this study we performed a systematic evaluation of functional miRNA-mRNA interactions associated with the aggressiveness of breast cancer cells using a combination of integrated miRNA and mRNA expression profiling, bioinformatics prediction, and functional assays. Analysis of the miRNA expression identified 11 miRNAs that were differentially expressed, including 7 down-regulated (miR-200c, miR-205, miR-203, miR-141, miR-34a, miR-183, and miR-375) and 4 up-regulated miRNAs (miR-146a, miR-138, miR-125b1 and miR-100), in aggressive cell lines when compared to normal and less aggressive cell lines. Transient overexpression of miR-200c, miR-205, and miR-375 in MDA-MB-231 cells led to the inhibition of cell migration and invasion. The integrated analysis of miRNA and mRNA expression identified 35 known and novel target genes of miR-200c, miR-205, and mir-375, including CFL2, LAMC1, TIMP2, ZEB1, CDH11, PRKCA, PTPRJ, PTPRM, LDHB, and SEC23A. Surprisingly, the majority of these genes (27 genes) were target genes of miR-200c, suggesting that it plays a more important role in regulating the aggressiveness of breast cancer cells. We characterized one of the target genes of miR-200c, CFL2, and demonstrated that CFL2 is overexpressed in aggressive breast cancer cell lines and can be significantly down-regulated by exogenous miR-200c. Tissue microarray analysis further revealed that CFL2 expression in primary breast cancer tissue correlated with tumor grade. To our knowledge, this study is the first systematic screening of functional miRNA target genes in aggressive breast cancer cells. The results obtained from this study may improve our understanding of the role of these candidate miRNAs and their target genes in relation to breast cancer aggressiveness and ultimately lead to the identification of novel biomarkers associated with prognosis. Affymetrix miRNA 1.0 arrays were performed according to the manufacturer's directions on small RNA extracted from 12 breast cancer cell lines including 4 aggressive cell lines, 6 less aggressive cell lines and 2 imortalized breast epithelium cell lines

Project description:Chemotherapy resistance frequently drives tumor progression. However, the underlying molecular mechanisms are poorly characterized. Epithelial-to-mesenchymal transition (EMT) has been shown to correlate with therapy resistance, but the functional link and signaling pathways remain to be elucidated. We report here that miR-30c, a human breast tumor prognostic marker, plays a pivotal role in chemo-resistance by a direct targeting of the actintransporter TWF1, which promotes EMT. An IL-6 family member, IL-11 was identified as a secondary target of TWF1 in the miR-30c signaling pathway. Expression of miR-30c inversely correlated with IL-11 expression in clinical tumors and IL-11 correlated with relapse-free survival in breast cancer patients. Identification of a novel miRNA-mediated pathway that regulates chemo-resistance in breast cancer will facilitate the development of new management strategies. For the primary breast tumor and normal breast samples, both mRNAs (Agilent array data deposited in GSE22049) and miRNAs (Exiqon array data deposited here) profiles have been examined. Patient information include age, tumor subtype and outcome etc. When clustering samples and genes 152 of 757 miRNA passed the filtering criteria on variation across samples; standard deviation > 0.50. Hence, the upper 152 miRNAs in the expression matrix were used in the two-way hierarchical clustering of genes. The matrix numbers are all log2(Hy3/Hy5) ratios, meaning sample/pool. The percentages above the matrix indicates the present call in each sample/slide. When calling of a particular miRNA failed on an array this is indicated as a blank in the row containing this miRNA and in the column corresponding to this array. The criteria for deciding that the calling of a miRNA had failed on a particular array, was that 2 or more of the 4 replicated measures of this miRNA were flagged 1 or 2 (i.e. the signal is below background) by the image analysis software. Further in the expression matrix all capture probes with Hy3 and Hy5 signals lower than 1.5x of the median signal intensity of the given slide is indicated as a blank.

Dataset Information

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

Publications

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

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