{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["10(1)"],"submitter":["Celiku O"],"funding":["Intramural Research Program of the National Institutes of Health, National Cancer Institute."],"pubmed_abstract":["<h4>Background</h4>Epithelial to mesenchymal transition, and mimicking processes, contribute to cancer invasion and metastasis, and are known to be responsible for resistance to various therapeutic agents in many cancers. While a number of studies have proposed molecular signatures that characterize the spectrum of such transition, more work is needed to understand how the mesenchymal signature (MS) is regulated in non-epithelial cancers like gliomas, to identify markers with the most prognostic significance, and potential for therapeutic targeting.<h4>Results</h4>Computational analysis of 275 glioma samples from \"The Cancer Genome Atlas\" was used to identify the regulatory changes between low grade gliomas with little expression of MS, and high grade glioblastomas with high expression of "],"journal":["BMC medical genomics"],"pagination":["13"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC5345226"],"repository":["biostudies-literature"],"pubmed_title":["Computational analysis of the mesenchymal signature landscape in gliomas."],"pmcid":["PMC5345226"],"pubmed_authors":["Celiku O","Camphausen K","Shankavaram U","Tandle A","Hewitt SM","Chung JY"],"additional_accession":[]},"is_claimable":false,"name":"Computational analysis of the mesenchymal signature landscape in gliomas.","description":"<h4>Background</h4>Epithelial to mesenchymal transition, and mimicking processes, contribute to cancer invasion and metastasis, and are known to be responsible for resistance to various therapeutic agents in many cancers. While a number of studies have proposed molecular signatures that characterize the spectrum of such transition, more work is needed to understand how the mesenchymal signature (MS) is regulated in non-epithelial cancers like gliomas, to identify markers with the most prognostic significance, and potential for therapeutic targeting.<h4>Results</h4>Computational analysis of 275 glioma samples from \"The Cancer Genome Atlas\" was used to identify the regulatory changes between low grade gliomas with little expression of MS, and high grade glioblastomas with high expression of ","dates":{"release":"2017-01-01T00:00:00Z","publication":"2017 Mar","modification":"2026-06-15T03:19:40.541Z","creation":"2019-06-06T17:08:28Z"},"accession":"S-EPMC5345226","cross_references":{"pubmed":["28279210"],"doi":["10.1186/s12920-017-0252-7"]}}