Evidence map›Paper›PMID 27226706›Full record

ArticleCancer informatics2016

Bioinformatic Studies to Predict MicroRNAs with the Potential of Uncoupling RECK Expression from Epithelial-Mesenchymal Transition in Cancer Cells.

Zhipeng Wang, Ryusuke Murakami, Kanako Yuki, Yoko Yoshida, Makoto Noda

Open access · goldAbstract read
In one paragraph

Article in Cancer informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 38% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Zhipeng WangLaboratory for Malignancy Control Research, Medical Innovation Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Ryusuke MurakamiDepartment of Gynecology and Obstetrics, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Kanako YukiLaboratory for Malignancy Control Research, Medical Innovation Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Yoko YoshidaLaboratory for Malignancy Control Research, Medical Innovation Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Makoto NodaLaboratory for Malignancy Control Research, Medical Innovation Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan.; Department of Molecular Oncology, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Kyoto University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

cancerEMTmiR-183-96-182paired data correlationRECKSOX2TEAD4

Identifiers

PMID27226706
PMCPMC4874744
OpenAlexW2407728998

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.