| Title |
Oncopig Soft-Tissue Sarcomas Recapitulate Key Transcriptional Features of Human Sarcomas
|
|---|---|
| Published in |
Scientific Reports, June 2017
|
| DOI | 10.1038/s41598-017-02912-9 |
| Pubmed ID | |
| Authors |
Kyle M. Schachtschneider, Yingkai Liu, Suvi Mäkeläinen, Ole Madsen, Laurie A. Rund, Martien A. M. Groenen, Lawrence B. Schook |
| Abstract |
Human soft-tissue sarcomas (STS) are rare mesenchymal tumors with a 5-year survival rate of 50%, highlighting the need for further STS research. Research has been hampered by limited human sarcoma cell line availability and the large number of STS subtypes, making development of STS cell lines and animal models representative of the diverse human STS subtypes critical. Pigs represent ideal human disease models due to their similar size, anatomy, metabolism, and genetics compared to humans. The Oncopig encodes inducible KRAS (G12D) and TP53 (R167H) transgenes, allowing for STS modeling in a spatial and temporal manner. This study utilized Oncopig STS cell line (fibroblast) and tumor (leiomyosarcoma) RNA-seq data to compare Oncopig and human STS expression profiles. Altered expression of 3,360 and 7,652 genes was identified in Oncopig STS cell lines and leiomyosarcomas, respectively. Transcriptional hallmarks of human STS were observed in Oncopig STS, including altered TP53 signaling, Wnt signaling activation, and evidence of epigenetic reprogramming. Furthermore, master regulators of Oncopig STS expression were identified, including FOSL1, which was previously identified as a potential human STS therapeutic target. These results demonstrate the Oncopig STS model's ability to mimic human STS transcriptional profiles, providing a valuable resource for sarcoma research and cell line development. |
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 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 29 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Other | 4 | 14% |
| Student > Bachelor | 4 | 14% |
| Professor | 4 | 14% |
| Student > Master | 4 | 14% |
| Student > Ph. D. Student | 3 | 10% |
| Other | 3 | 10% |
| Unknown | 7 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 5 | 17% |
| Agricultural and Biological Sciences | 5 | 17% |
| Biochemistry, Genetics and Molecular Biology | 4 | 14% |
| Veterinary Science and Veterinary Medicine | 1 | 3% |
| Decision Sciences | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 12 | 41% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 June 2017.
All research outputs
#18,552,700
of 22,977,819 outputs
Outputs from Scientific Reports
#93,893
of 124,056 outputs
Outputs of similar age
#241,429
of 316,526 outputs
Outputs of similar age from Scientific Reports
#2,812
of 3,946 outputs
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