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Mendeley readers
| Title |
Key nodes of a microRNA network associated with the integrated mesenchymal subtype of high‐grade serous ovarian cancer
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|---|---|
| Published in |
Cancer Communications, June 2015
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| DOI | 10.5732/cjc.014.10284 |
| Pubmed ID | |
| Authors |
Yan Sun, Fei Guo, Marina Bagnoli, Feng‐Xia Xue, Bao‐Cun Sun, Ilya Shmulevich, Delia Mezzanzanica, Ke‐Xin Chen, Anil K. Sood, Da Yang, Wei Zhang |
| Abstract |
Metastasis is the main cause of cancer mortality. One of the initiating events of cancer metastasis of epithelial tumors is epithelial-to-mesenchymal transition (EMT), during which cells dedifferentiate from a relatively rigid cell structure/morphology to a flexible and changeable structure/morphology often associated with mesenchymal cells. The presence of EMT in human epithelial tumors is reflected by the increased expression of genes and levels of proteins that are preferentially present in mesenchymal cells. The combined presence of these genes forms the basis of mesenchymal gene signatures, which are the foundation for classifying a mesenchymal subtype of tumors. Indeed, tumor classification schemes that use clustering analysis of large genomic characterizations, like The Cancer Genome Atlas (TCGA), have defined mesenchymal subtype in a number of cancer types, such as high-grade serous ovarian cancer and glioblastoma. However, recent analyses have shown that gene expression-based classifications of mesenchymal subtypes often do not associate with poor survival. This "paradox" can be ameliorated using integrated analysis that combines multiple data types. We recently found that integrating mRNA and microRNA (miRNA) data revealed an integrated mesenchymal subtype that is consistently associated with poor survival in multiple cohorts of patients with serous ovarian cancer. This network consists of 8 major miRNAs and 214 mRNAs. Among the 8 miRNAs, 4 are known to be regulators of EMT. This review provides a summary of these 8 miRNAs, which were associated with the integrated mesenchymal subtype of serous ovarian cancer. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 3% |
| Unknown | 37 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 24% |
| Student > Ph. D. Student | 8 | 21% |
| Other | 4 | 11% |
| Professor > Associate Professor | 2 | 5% |
| Student > Master | 2 | 5% |
| Other | 4 | 11% |
| Unknown | 9 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 10 | 26% |
| Agricultural and Biological Sciences | 8 | 21% |
| Medicine and Dentistry | 7 | 18% |
| Neuroscience | 2 | 5% |
| Arts and Humanities | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 9 | 24% |