Meta-analysis of EMT datasets reveals different types of EMT

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

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


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.

Original languageEnglish (US)
Article numbere0156839
JournalPloS one
Issue number6
StatePublished - Jun 1 2016

Bibliographical note

Funding Information:
This work was supported by the National Natural Science Foundation of China (31422032, 31421004, 91519305), the Guangdong Natural Science Foundation (2014A030308002), the Guangdong Science and Technology Planning Project (2013B010404040), the Guangzhou Health Care Collaborative Innovation Program (201508020250), and the Guangdong special support program (2014TQ01R157). This work was also partially supported by National Science Foundation of United States (III 1117153).

Publisher Copyright:
© 2016 Liang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Dive into the research topics of 'Meta-analysis of EMT datasets reveals different types of EMT'. Together they form a unique fingerprint.

Cite this