Evidence map›Paper›PMID 27258544›Full record

SynthesisPloS one2016

Meta-Analysis of EMT Datasets Reveals Different Types of EMT.

Lining Liang, Hao Sun, Wei Zhang, Mengdan Zhang, Xiao Yang, Rui Kuang, Hui Zheng

Abstract readMeta-Analysis
In one paragraph

Synthesis in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
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

22 citing papers in PubMed.

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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

7 authors.

Lining LiangCAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong, China.
Hao SunCAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong, China.
Wei ZhangDepartment of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America.
Mengdan ZhangCAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong, China.
Xiao YangCAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong, China.
Rui KuangDepartment of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America.
Hui ZhengCAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As a critical process during embryonic development, cancer progression and cell fate conversions, epithelial-mesenchymal transition (EMT) has been extensively studied over the last several decades. To further understand the nature of EMT, we performed meta-analysis of multiple microarray datasets to identify the related generic signature. In this study, 24 human and 17 mouse microarray datasets were integrated to identify conserved gene expression changes in different types of EMT. Our integrative analysis revealed that there is low agreement among the list of the identified signature genes and three other lists in previous studies. Since removing the datasets with weakly-induced EMT from the analysis did not significantly improve the overlapping in the signature-gene lists, we hypothesized the existence of different types of EMT. This hypothesis was further supported by the grouping of 74 human EMT-induction samples into five distinct clusters, and the identification of distinct pathways in these different clusters of EMT samples. The five clusters of EMT-induction samples also improves the understanding of the characteristics of different EMT types. Therefore, we concluded the existence of different types of EMT was the possible reason for its complex role in multiple biological processes.

Indexed as

AnimalsDatabases, GeneticEpithelial-Mesenchymal TransitionGene Expression ProfilingHumansMice

Identifiers

PMID27258544
PMCPMC4892621

What OpenQuestion holds

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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.