Database Commons
Database Commons

a catalog of worldwide biological databases

Database Profile

dbEMT

General information

URL: http://dbemt.bioinfo-minzhao.org
Full name: Epithelial-Mesenchymal Transition gene database
Description: dbEMT is the first literature-based EMT-related gene resource that serves as a reference dataset for understanding the cellular mechanisms of EMT-related processes such as initiation of metastasis for cancer progression. This database could have profound implications for the diagnosis, treatment and prevention of EMT-related diseases such as cancer metastasis.
Year founded: 2015
Last update:
Version:
Accessibility:
Accessible
Country/Region: China

Contact information

University/Institution: Peking University
Address: Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, College of Life Sciences, Peking University, Beijing 100871, P.R. China
City: Beijing
Province/State: Beijing
Country/Region: China
Contact name (PI/Team): Hong Qu
Contact email (PI/Helpdesk): quh@mail.cbi.pku.edu.cn

Publications

26099468
dbEMT: an epithelial-mesenchymal transition associated gene resource. [PMID: 26099468]
Zhao M, Kong L, Liu Y, Qu H.

As a cellular process that changes epithelial cells to mesenchymal cells, Epithelial-mesenchymal transition (EMT) plays important roles in development and cancer metastasis. Recent studies on cancer metastasis have identified many new susceptibility genes that control this transition. However, there is no comprehensive resource for EMT by integrating various genetic studies and the relationship between EMT and the risk of complex diseases such as cancer are still unclear. To investigate the cellular complexity of EMT, we have constructed dbEMT (http://dbemt.bioinfo-minzhao.org/), the first literature-based gene resource for exploring EMT-related human genes. We manually curated 377 experimentally verified genes from literature. Functional analyses highlighted the prominent role of proteoglycans in tumor metastatic cascades. In addition, the disease enrichment analysis provides a clue for the potential transformation in affected tissues or cells in Alzheimer's disease and Type 2 Diabetes. Moreover, the global mutation pattern of EMT-related genes across multiple cancers may reveal common cancer metastasis mechanisms. Our further reconstruction of the EMT-related protein-protein interaction network uncovered a highly modular structure. These results illustrate the importance of dbEMT to our understanding of cell development and cancer metastasis, and also highlight the utility of dbEMT for elucidating the functions of EMT-related genes.

Sci Rep. 2015:5() | 112 Citations (from Europe PMC, 2025-12-20)

Ranking

All databases:
1302/6895 (81.131%)
Gene genome and annotation:
426/2021 (78.971%)
Interaction:
262/1194 (78.141%)
Pathway:
76/451 (83.37%)
Health and medicine:
313/1738 (82.048%)
Literature:
119/577 (79.549%)
Genotype phenotype and variation:
181/1005 (82.09%)
Expression:
253/1347 (81.292%)
Modification:
72/337 (78.932%)
Metadata:
128/719 (82.337%)
1302
Total Rank
109
Citations
10.9
z-index

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Record metadata

Created on: 2018-01-28
Curated by:
Sidra Younas [2018-04-12]
Meiye Jiang [2018-01-28]