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2016 ; 15
(ä): 91-102
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Bioinformatic Studies to Predict MicroRNAs with the Potential of Uncoupling RECK
Expression from Epithelial-Mesenchymal Transition in Cancer Cells
#MMPMID27226706
Wang Z
; Murakami R
; Yuki K
; Yoshida Y
; Noda M
Cancer Inform
2016[]; 15
(ä): 91-102
PMID27226706
show ga
RECK is downregulated in many tumors, and forced RECK expression in tumor cells
often results in suppression of malignant phenotypes. Recent findings suggest
that RECK is upregulated after epithelial-mesenchymal transition (EMT) in normal
epithelium-derived cells but not in cancer cells. Since several microRNAs (miRs)
are known to target RECK mRNA, we hypothesized that certain miR(s) may be
involved in this suppression of RECK upregulation after EMT in cancer cells. To
test this hypothesis, we used three approaches: (1) text mining to find miRs
relevant to EMT in cancer cells, (2) predicting miR targets using four
algorithms, and (3) comparing miR-seq data and RECK mRNA data using a novel
non-parametric method. These approaches identified the miR-183-96-182 cluster as
a strong candidate. We also looked for transcription factors and signaling
molecules that may promote cancer EMT, miR-183-96-182 upregulation, and RECK
downregulation. Here we describe our methods, findings, and a testable hypothesis
on how RECK expression could be regulated in cancer cells after EMT.