{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Sathasivam HP"],"funding":["Ministry of Health Malaysia.","Medical Research Council"],"pagination":["413-421"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8329212"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["125(3)"],"pubmed_abstract":["<h4>Background</h4>This study was undertaken to develop and validate a gene expression signature that characterises oral potentially malignant disorders (OPMD) with a high risk of undergoing malignant transformation.<h4>Methods</h4>Patients with oral epithelial dysplasia at one hospital were selected as the 'training set' (n = 56) whilst those at another hospital were selected for the 'test set' (n = 66). RNA was extracted from formalin-fixed paraffin-embedded (FFPE) diagnostic biopsies and analysed using the NanoString nCounter platform. A targeted panel of 42 genes selected on their association with oral carcinogenesis was used to develop a prognostic gene signature. Following data normalisation, uni- and multivariable analysis, as well as prognostic modelling, were employed to develop a"],"journal":["British journal of cancer"],"pubmed_title":["Predicting the clinical outcome of oral potentially malignant disorders using transcriptomic-based molecular pathology."],"pmcid":["PMC8329212"],"funding_grant_id":["MR/N005872/1"],"pubmed_authors":["Sathasivam HP","Nugent M","Robinson M","Kist R","Sloan P","Haider S","Thomson P","Alexander J"],"additional_accession":[]},"is_claimable":false,"name":"Predicting the clinical outcome of oral potentially malignant disorders using transcriptomic-based molecular pathology.","description":"<h4>Background</h4>This study was undertaken to develop and validate a gene expression signature that characterises oral potentially malignant disorders (OPMD) with a high risk of undergoing malignant transformation.<h4>Methods</h4>Patients with oral epithelial dysplasia at one hospital were selected as the 'training set' (n = 56) whilst those at another hospital were selected for the 'test set' (n = 66). RNA was extracted from formalin-fixed paraffin-embedded (FFPE) diagnostic biopsies and analysed using the NanoString nCounter platform. A targeted panel of 42 genes selected on their association with oral carcinogenesis was used to develop a prognostic gene signature. Following data normalisation, uni- and multivariable analysis, as well as prognostic modelling, were employed to develop a","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Aug","modification":"2026-05-31T23:57:31.989Z","creation":"2024-10-17T20:01:23.186Z"},"accession":"S-EPMC8329212","cross_references":{"pubmed":["33972745"],"doi":["10.1038/s41416-021-01411-z"]}}