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dbEMT: an epithelial-mesenchymal transition associated gene resource.

Zhao M, Kong L, Liu Y, Qu H - Sci Rep (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: School of Engineering, Faculty of Science, Health, Education and Engineering, University of the Sunshine Coast, Maroochydore DC, Queensland, 4558, Australia.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Reconstructed EMT map using protein-protein interaction data.(A) The 335 genes in orange are genes from the core dataset in our dbEMT. The remaining 36 genes in red are linker genes that bridge the 335 genes; (B) the degree distribution; (C) the short path length frequency; (D) the correlation between closeness centrality and the number of neighbors.
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f5: Reconstructed EMT map using protein-protein interaction data.(A) The 335 genes in orange are genes from the core dataset in our dbEMT. The remaining 36 genes in red are linker genes that bridge the 335 genes; (B) the degree distribution; (C) the short path length frequency; (D) the correlation between closeness centrality and the number of neighbors.

Mentions: In the past decade, the knowledge of the cellular pathway for metabolism and signaling transduction have been summarized in a few popular biological pathway data such as the KEGG pathway. Recently, the Pathway Commons database combined all the popular pathway databases to provide gene-gene functional interaction pairs and these are useful for further pathway reconstruction18. In our study, we utilized reliable public data sources and constructed a more comprehensive cellular map for EMT. The reconstructed EMT contains 371 genes and 2357 gene-gene interactions is based on current evidence from known biological pathways (Fig. 5A). Of the 371 nodes, 335 of them are from our curated 377 IQ-related genes. The remaining 36 are the novel genes that may potentially bridge the EMT-related gene to fully implement their cellular function. The majority of curated EMT-related genes are linked to each other in a highly modular structure. This not only support the accuracy of our data, but it also reveals the EMT-related genes are highly connected to each other and form a high density cellular modular.


dbEMT: an epithelial-mesenchymal transition associated gene resource.

Zhao M, Kong L, Liu Y, Qu H - Sci Rep (2015)

Reconstructed EMT map using protein-protein interaction data.(A) The 335 genes in orange are genes from the core dataset in our dbEMT. The remaining 36 genes in red are linker genes that bridge the 335 genes; (B) the degree distribution; (C) the short path length frequency; (D) the correlation between closeness centrality and the number of neighbors.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4477208&req=5

f5: Reconstructed EMT map using protein-protein interaction data.(A) The 335 genes in orange are genes from the core dataset in our dbEMT. The remaining 36 genes in red are linker genes that bridge the 335 genes; (B) the degree distribution; (C) the short path length frequency; (D) the correlation between closeness centrality and the number of neighbors.
Mentions: In the past decade, the knowledge of the cellular pathway for metabolism and signaling transduction have been summarized in a few popular biological pathway data such as the KEGG pathway. Recently, the Pathway Commons database combined all the popular pathway databases to provide gene-gene functional interaction pairs and these are useful for further pathway reconstruction18. In our study, we utilized reliable public data sources and constructed a more comprehensive cellular map for EMT. The reconstructed EMT contains 371 genes and 2357 gene-gene interactions is based on current evidence from known biological pathways (Fig. 5A). Of the 371 nodes, 335 of them are from our curated 377 IQ-related genes. The remaining 36 are the novel genes that may potentially bridge the EMT-related gene to fully implement their cellular function. The majority of curated EMT-related genes are linked to each other in a highly modular structure. This not only support the accuracy of our data, but it also reveals the EMT-related genes are highly connected to each other and form a high density cellular modular.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: School of Engineering, Faculty of Science, Health, Education and Engineering, University of the Sunshine Coast, Maroochydore DC, Queensland, 4558, Australia.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus