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Quantitative proteomics in resected renal cancer tissue for biomarker discovery and profiling

BACKGROUND: Proteomics-based approaches for biomarker discovery are promising strategies used in cancer research. We present state-of-art label-free quantitative proteomics method to assess proteome of renal cell carcinoma (RCC) compared with noncancer renal tissues. METHODS: Fresh frozen tissue sam...

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Autores principales: Atrih, A, Mudaliar, M A V, Zakikhani, P, Lamont, D J, Huang, J T-J, Bray, S E, Barton, G, Fleming, S, Nabi, G
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3960606/
https://www.ncbi.nlm.nih.gov/pubmed/24548857
http://dx.doi.org/10.1038/bjc.2014.24
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author Atrih, A
Mudaliar, M A V
Zakikhani, P
Lamont, D J
Huang, J T-J
Bray, S E
Barton, G
Fleming, S
Nabi, G
author_facet Atrih, A
Mudaliar, M A V
Zakikhani, P
Lamont, D J
Huang, J T-J
Bray, S E
Barton, G
Fleming, S
Nabi, G
author_sort Atrih, A
collection PubMed
description BACKGROUND: Proteomics-based approaches for biomarker discovery are promising strategies used in cancer research. We present state-of-art label-free quantitative proteomics method to assess proteome of renal cell carcinoma (RCC) compared with noncancer renal tissues. METHODS: Fresh frozen tissue samples from eight primary RCC lesions and autologous adjacent normal renal tissues were obtained from surgically resected tumour-bearing kidneys. Proteins were extracted by complete solubilisation of tissues using filter-aided sample preparation (FASP) method. Trypsin digested proteins were analysed using quantitative label-free proteomics approach followed by data interpretation and pathways analysis. RESULTS: A total of 1761 proteins were identified and quantified with high confidence (MASCOT ion score threshold of 35 and P-value <0.05). Of these, 596 proteins were identified as differentially expressed between cancer and noncancer tissues. Two upregulated proteins in tumour samples (adipose differentiation-related protein and Coronin 1A) were further validated by immunohistochemistry. Pathway analysis using IPA, KOBAS 2.0, DAVID functional annotation and FLink tools showed enrichment of many cancer-related biological processes and pathways such as oxidative phosphorylation, glycolysis and amino acid synthetic pathways. CONCLUSIONS: Our study identified a number of differentially expressed proteins and pathways using label-free proteomics approach in RCC compared with normal tissue samples. Two proteins validated in this study are the focus of on-going research in a large cohort of patients.
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spelling pubmed-39606062015-03-18 Quantitative proteomics in resected renal cancer tissue for biomarker discovery and profiling Atrih, A Mudaliar, M A V Zakikhani, P Lamont, D J Huang, J T-J Bray, S E Barton, G Fleming, S Nabi, G Br J Cancer Molecular Diagnostics BACKGROUND: Proteomics-based approaches for biomarker discovery are promising strategies used in cancer research. We present state-of-art label-free quantitative proteomics method to assess proteome of renal cell carcinoma (RCC) compared with noncancer renal tissues. METHODS: Fresh frozen tissue samples from eight primary RCC lesions and autologous adjacent normal renal tissues were obtained from surgically resected tumour-bearing kidneys. Proteins were extracted by complete solubilisation of tissues using filter-aided sample preparation (FASP) method. Trypsin digested proteins were analysed using quantitative label-free proteomics approach followed by data interpretation and pathways analysis. RESULTS: A total of 1761 proteins were identified and quantified with high confidence (MASCOT ion score threshold of 35 and P-value <0.05). Of these, 596 proteins were identified as differentially expressed between cancer and noncancer tissues. Two upregulated proteins in tumour samples (adipose differentiation-related protein and Coronin 1A) were further validated by immunohistochemistry. Pathway analysis using IPA, KOBAS 2.0, DAVID functional annotation and FLink tools showed enrichment of many cancer-related biological processes and pathways such as oxidative phosphorylation, glycolysis and amino acid synthetic pathways. CONCLUSIONS: Our study identified a number of differentially expressed proteins and pathways using label-free proteomics approach in RCC compared with normal tissue samples. Two proteins validated in this study are the focus of on-going research in a large cohort of patients. Nature Publishing Group 2014-03-18 2014-02-18 /pmc/articles/PMC3960606/ /pubmed/24548857 http://dx.doi.org/10.1038/bjc.2014.24 Text en Copyright © 2014 Cancer Research UK http://creativecommons.org/licenses/by-nc-sa/3.0/ From twelve months after its original publication, this work is licensed under the Creative Commons Attribution-NonCommercial-Share Alike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/
spellingShingle Molecular Diagnostics
Atrih, A
Mudaliar, M A V
Zakikhani, P
Lamont, D J
Huang, J T-J
Bray, S E
Barton, G
Fleming, S
Nabi, G
Quantitative proteomics in resected renal cancer tissue for biomarker discovery and profiling
title Quantitative proteomics in resected renal cancer tissue for biomarker discovery and profiling
title_full Quantitative proteomics in resected renal cancer tissue for biomarker discovery and profiling
title_fullStr Quantitative proteomics in resected renal cancer tissue for biomarker discovery and profiling
title_full_unstemmed Quantitative proteomics in resected renal cancer tissue for biomarker discovery and profiling
title_short Quantitative proteomics in resected renal cancer tissue for biomarker discovery and profiling
title_sort quantitative proteomics in resected renal cancer tissue for biomarker discovery and profiling
topic Molecular Diagnostics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3960606/
https://www.ncbi.nlm.nih.gov/pubmed/24548857
http://dx.doi.org/10.1038/bjc.2014.24
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