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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

The shared somatic variants related to EMT across 19 cancer types.The length of circularly arranged segments is proportional to the total variants in each cancer type. The ribbons connecting different segments represent the number of shared variants between cancer types. The three outer rings are stacked bar plots that represent relative contribution of other cancer types to the cancer type’s totals.
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f3: The shared somatic variants related to EMT across 19 cancer types.The length of circularly arranged segments is proportional to the total variants in each cancer type. The ribbons connecting different segments represent the number of shared variants between cancer types. The three outer rings are stacked bar plots that represent relative contribution of other cancer types to the cancer type’s totals.

Mentions: To further explore the common and distinct EMT-related somatic variants in different cancer types, we mapped the 377 EMT-related genes to the mutation data of TCGA pan-cancer14. To focus on the functional variants, we excluded non-sense and silent mutations. The remaining contains mis-senses, splicings, frameshift SNVs and INDELs. As shown in the Fig. 3, majority of EMT-related somatic variants are shared between cancers. For instance, it is interesting that Uterine Corpus Endometrioid Carcinoma (UCEC) has many overlapping variants with colon and rectal adenocarcinoma (COADREAD) and suggests that the three cancers might have similar processes related to EMT. Or is simply because of the closeness of two organs. The similar comparison can be applied to other cancers. In summary, not all the cancers share the same set of EMT-related mutations. Considering both Figs 2 and 3, it is concluded that different cancers may have different mutation numbers and contents for EMT-related genes. According to the mutation frequency, some driver mutations occurred in some of critical genes, including MUC16 (1720 mutations), TP53 (1113 mutations), PTEN (1113 mutations), PIK3CA (291 mutations), NF1 (288 mutations), VCAN (274 mutations), ATM (236 mutations) and LAMA1 (230 mutations) in all the pan-cancer samples. These driver mutations in EMT-related pathway may induce the cancer cells in the progressive direction during EMT. To further test whether these top 100 EMT related-genes are mutated more frequently than expected by chance, we conducted 1000 resamplings to choose 100 mutated genes from 19 pan-cancer data. To evaluate the statistical significance, we checked each randomly selected gene set whether the number of genes with meaning mutations was more than the actual number of mutated genes in our top 100 EMT related-genes. Using the number of randomly selected node sets with more mutated genes as input, we calculated the empirical P-values (See Methods section). As shown in Table S2, all the 19 empirical P-values are all less than 0.01, which means the top 100 EMT related genes are highly mutated in the 19 pan-cancer datasets.


dbEMT: an epithelial-mesenchymal transition associated gene resource.

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

The shared somatic variants related to EMT across 19 cancer types.The length of circularly arranged segments is proportional to the total variants in each cancer type. The ribbons connecting different segments represent the number of shared variants between cancer types. The three outer rings are stacked bar plots that represent relative contribution of other cancer types to the cancer type’s totals.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f3: The shared somatic variants related to EMT across 19 cancer types.The length of circularly arranged segments is proportional to the total variants in each cancer type. The ribbons connecting different segments represent the number of shared variants between cancer types. The three outer rings are stacked bar plots that represent relative contribution of other cancer types to the cancer type’s totals.
Mentions: To further explore the common and distinct EMT-related somatic variants in different cancer types, we mapped the 377 EMT-related genes to the mutation data of TCGA pan-cancer14. To focus on the functional variants, we excluded non-sense and silent mutations. The remaining contains mis-senses, splicings, frameshift SNVs and INDELs. As shown in the Fig. 3, majority of EMT-related somatic variants are shared between cancers. For instance, it is interesting that Uterine Corpus Endometrioid Carcinoma (UCEC) has many overlapping variants with colon and rectal adenocarcinoma (COADREAD) and suggests that the three cancers might have similar processes related to EMT. Or is simply because of the closeness of two organs. The similar comparison can be applied to other cancers. In summary, not all the cancers share the same set of EMT-related mutations. Considering both Figs 2 and 3, it is concluded that different cancers may have different mutation numbers and contents for EMT-related genes. According to the mutation frequency, some driver mutations occurred in some of critical genes, including MUC16 (1720 mutations), TP53 (1113 mutations), PTEN (1113 mutations), PIK3CA (291 mutations), NF1 (288 mutations), VCAN (274 mutations), ATM (236 mutations) and LAMA1 (230 mutations) in all the pan-cancer samples. These driver mutations in EMT-related pathway may induce the cancer cells in the progressive direction during EMT. To further test whether these top 100 EMT related-genes are mutated more frequently than expected by chance, we conducted 1000 resamplings to choose 100 mutated genes from 19 pan-cancer data. To evaluate the statistical significance, we checked each randomly selected gene set whether the number of genes with meaning mutations was more than the actual number of mutated genes in our top 100 EMT related-genes. Using the number of randomly selected node sets with more mutated genes as input, we calculated the empirical P-values (See Methods section). As shown in Table S2, all the 19 empirical P-values are all less than 0.01, which means the top 100 EMT related genes are highly mutated in the 19 pan-cancer datasets.

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