dc.contributor.author | Wang, J | en_US |
dc.contributor.author | Harwood, CA | en_US |
dc.contributor.author | Bailey, E | en_US |
dc.contributor.author | Bewicke-Copley, F | en_US |
dc.contributor.author | Anene, CA | en_US |
dc.contributor.author | Thomson, J | en_US |
dc.contributor.author | Qamar, MJ | en_US |
dc.contributor.author | Laban, R | en_US |
dc.contributor.author | Nourse, C | en_US |
dc.contributor.author | Schoenherr, C | en_US |
dc.contributor.author | Treanor-Taylor, M | en_US |
dc.contributor.author | Healy, E | en_US |
dc.contributor.author | Lai, C | en_US |
dc.contributor.author | Craig, P | en_US |
dc.contributor.author | Moyes, C | en_US |
dc.contributor.author | Rickaby, W | en_US |
dc.contributor.author | Martin, J | en_US |
dc.contributor.author | Proby, C | en_US |
dc.contributor.author | Inman, GJ | en_US |
dc.contributor.author | Leigh, IM | en_US |
dc.date.accessioned | 2023-08-29T12:36:11Z | |
dc.date.available | 2023-08-01 | en_US |
dc.date.issued | 2023-08-14 | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/90263 | |
dc.description.abstract | BACKGROUND: Metastasis of cutaneous squamous cell carcinoma (cSCC) is uncommon. Current staging methods are reported to have sub-optimal performances in metastasis prediction. Accurate identification of patients with tumours at high risk of metastasis would have a significant impact on management. OBJECTIVE: To develop a robust and validated gene expression profile (GEP) signature for predicting primary cSCC metastatic risk using an unbiased whole transcriptome discovery-driven approach. METHODS: Archival formalin-fixed paraffin-embedded primary cSCC with perilesional normal tissue from 237 immunocompetent patients (151 non-metastasising and 86 metastasising) were collected retrospectively from four centres. TempO-seq was used to probe the whole transcriptome and machine learning algorithms were applied to derive predictive signatures, with a 3:1 split for training and testing datasets. RESULTS: A 20-gene prognostic model was developed and validated, with an accuracy of 86.0%, sensitivity of 85.7%, specificity of 86.1%, and positive predictive value of 78.3% in the testing set, providing more stable, accurate prediction than pathological staging systems. A linear predictor was also developed, significantly correlating with metastatic risk. LIMITATIONS: This was a retrospective 4-centre study and larger prospective multicentre studies are now required. CONCLUSION: The 20-gene signature prediction is accurate, with the potential to be incorporated into clinical workflows for cSCC. | en_US |
dc.language | eng | en_US |
dc.relation.ispartof | J Am Acad Dermatol | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.subject | Cutaneous squamous cell carcinoma | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Metastasis | en_US |
dc.subject | Prognosis | en_US |
dc.subject | Risk stratification | en_US |
dc.subject | Transcriptomics | en_US |
dc.title | Transcriptomic analysis of cutaneous squamous cell carcinoma reveals a multi-gene prognostic signature associated with metastasis. | en_US |
dc.type | Article | |
dc.identifier.doi | 10.1016/j.jaad.2023.08.012 | en_US |
pubs.author-url | https://www.ncbi.nlm.nih.gov/pubmed/37586461 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published online | en_US |
dcterms.dateAccepted | 2023-08-01 | en_US |