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Transcriptomic Biomarker Signatures for Discrimination of Oral Cancer Surgical Margins
Relapse after surgery for oral squamous cell carcinoma (OSCC) contributes significantly to morbidity, mortality and poor outcomes. The current histopathological diagnostic techniques are insufficiently sensitive for the detection of oral cancer and minimal residual disease in surgical margins. We us...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8946245/ https://www.ncbi.nlm.nih.gov/pubmed/35327656 http://dx.doi.org/10.3390/biom12030464 |
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author | Fox, Simon A. Vacher, Michael Farah, Camile S. |
author_facet | Fox, Simon A. Vacher, Michael Farah, Camile S. |
author_sort | Fox, Simon A. |
collection | PubMed |
description | Relapse after surgery for oral squamous cell carcinoma (OSCC) contributes significantly to morbidity, mortality and poor outcomes. The current histopathological diagnostic techniques are insufficiently sensitive for the detection of oral cancer and minimal residual disease in surgical margins. We used whole-transcriptome gene expression and small noncoding RNA profiles from tumour, close margin and distant margin biopsies from 18 patients undergoing surgical resection for OSCC. By applying multivariate regression algorithms (sPLS-DA) suitable for higher dimension data, we objectively identified biomarker signatures for tumour and marginal tissue zones. We were able to define molecular signatures that discriminated tumours from the marginal zones and between the close and distant margins. These signatures included genes not previously associated with OSCC, such as MAMDC2, SYNPO2 and ARMH4. For discrimination of the normal and tumour sampling zones, we were able to derive an effective gene-based classifying model for molecular abnormality based on a panel of eight genes (MMP1, MMP12, MYO1B, TNFRSF12A, WDR66, LAMC2, SLC16A1 and PLAU). We demonstrated the classification performance of these gene signatures in an independent validation dataset of OSCC tumour and marginal gene expression profiles. These biomarker signatures may contribute to the earlier detection of tumour cells and complement existing surgical and histopathological techniques used to determine clear surgical margins. |
format | Online Article Text |
id | pubmed-8946245 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89462452022-03-25 Transcriptomic Biomarker Signatures for Discrimination of Oral Cancer Surgical Margins Fox, Simon A. Vacher, Michael Farah, Camile S. Biomolecules Article Relapse after surgery for oral squamous cell carcinoma (OSCC) contributes significantly to morbidity, mortality and poor outcomes. The current histopathological diagnostic techniques are insufficiently sensitive for the detection of oral cancer and minimal residual disease in surgical margins. We used whole-transcriptome gene expression and small noncoding RNA profiles from tumour, close margin and distant margin biopsies from 18 patients undergoing surgical resection for OSCC. By applying multivariate regression algorithms (sPLS-DA) suitable for higher dimension data, we objectively identified biomarker signatures for tumour and marginal tissue zones. We were able to define molecular signatures that discriminated tumours from the marginal zones and between the close and distant margins. These signatures included genes not previously associated with OSCC, such as MAMDC2, SYNPO2 and ARMH4. For discrimination of the normal and tumour sampling zones, we were able to derive an effective gene-based classifying model for molecular abnormality based on a panel of eight genes (MMP1, MMP12, MYO1B, TNFRSF12A, WDR66, LAMC2, SLC16A1 and PLAU). We demonstrated the classification performance of these gene signatures in an independent validation dataset of OSCC tumour and marginal gene expression profiles. These biomarker signatures may contribute to the earlier detection of tumour cells and complement existing surgical and histopathological techniques used to determine clear surgical margins. MDPI 2022-03-17 /pmc/articles/PMC8946245/ /pubmed/35327656 http://dx.doi.org/10.3390/biom12030464 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Fox, Simon A. Vacher, Michael Farah, Camile S. Transcriptomic Biomarker Signatures for Discrimination of Oral Cancer Surgical Margins |
title | Transcriptomic Biomarker Signatures for Discrimination of Oral Cancer Surgical Margins |
title_full | Transcriptomic Biomarker Signatures for Discrimination of Oral Cancer Surgical Margins |
title_fullStr | Transcriptomic Biomarker Signatures for Discrimination of Oral Cancer Surgical Margins |
title_full_unstemmed | Transcriptomic Biomarker Signatures for Discrimination of Oral Cancer Surgical Margins |
title_short | Transcriptomic Biomarker Signatures for Discrimination of Oral Cancer Surgical Margins |
title_sort | transcriptomic biomarker signatures for discrimination of oral cancer surgical margins |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8946245/ https://www.ncbi.nlm.nih.gov/pubmed/35327656 http://dx.doi.org/10.3390/biom12030464 |
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