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Proteomic Profiling of Archived Tissue of Primary Melanoma Identifies Proteins Associated with Metastasis.
Shapanis, Andrew; Lai, Chester; Sommerlad, Mathew; Parkinson, Erika; Healy, Eugene; Skipp, Paul.
Afiliação
  • Shapanis A; Centre for Proteomic Research, Biological Sciences, University of Southampton, Southampton SO17 1BJ, UK.
  • Lai C; Dermatopharmacology, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK.
  • Sommerlad M; Dermatology, University Hospital Southampton NHS Foundation Trust, Southampton SO16 6YD, UK.
  • Parkinson E; Histopathology, University Hospital Southampton NHS Foundation Trust, Southampton SO16 6YD, UK.
  • Healy E; Centre for Proteomic Research, Biological Sciences, University of Southampton, Southampton SO17 1BJ, UK.
  • Skipp P; Dermatopharmacology, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK.
Int J Mol Sci ; 21(21)2020 Oct 31.
Article em En | MEDLINE | ID: mdl-33142795
ABSTRACT
Formalin-fixed paraffin embedded (FFPE) clinical tissues represent an abundant and unique resource for translational proteomic studies. In the US, melanoma is the 5th and 6th most common cancer in men and women, respectively, affecting over 230,000 people annually and metastasising in 5-15% of cases. Median survival time for distant metastatic melanoma is 6-9 months with a 5-year-survival of < 15%. In this study, 24 primary FFPE tumours which have metastasised (P-M) and 24 primary FFPE tumours which did not metastasise (P-NM) were subjected to proteomic profiling. In total, 2750 proteins were identified, of which 16 were significantly differentially expressed. Analysis of TCGA data demonstrated that expression of the genes encoding for 6 of these 16 proteins had a significant effect on survival in cutaneous melanoma. Pathway analysis of the proteomics data revealed mechanisms likely involved in the process of melanoma metastasis, including cytoskeleton rearrangement, extracellular changes and immune system alterations. A machine learning prediction model scoring an AUC of 0.922, based on these 16 differentially expressed proteins was able to accurately classify samples into P-M and P-NM. This study has identified potential biomarkers and key processes relating to melanoma metastasis using archived clinical samples, providing a basis for future studies in larger cohorts.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Cutâneas / Biomarcadores Tumorais / Fixação de Tecidos / Inclusão em Parafina / Proteoma / Melanoma Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Female / Humans / Male Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Cutâneas / Biomarcadores Tumorais / Fixação de Tecidos / Inclusão em Parafina / Proteoma / Melanoma Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Female / Humans / Male Idioma: En Ano de publicação: 2020 Tipo de documento: Article