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An integrated landscape of protein expression in human cancer
Using 11 proteomics datasets, mostly available through the PRIDE database, we assembled a reference expression map for 191 cancer cell lines and 246 clinical tumour samples, across 13 lineages. We found unique peptides identified only in tumour samples despite a much higher coverage in cell lines. T...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8065022/ https://www.ncbi.nlm.nih.gov/pubmed/33893311 http://dx.doi.org/10.1038/s41597-021-00890-2 |
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author | Jarnuczak, Andrew F. Najgebauer, Hanna Barzine, Mitra Kundu, Deepti J. Ghavidel, Fatemeh Perez-Riverol, Yasset Papatheodorou, Irene Brazma, Alvis Vizcaíno, Juan Antonio |
author_facet | Jarnuczak, Andrew F. Najgebauer, Hanna Barzine, Mitra Kundu, Deepti J. Ghavidel, Fatemeh Perez-Riverol, Yasset Papatheodorou, Irene Brazma, Alvis Vizcaíno, Juan Antonio |
author_sort | Jarnuczak, Andrew F. |
collection | PubMed |
description | Using 11 proteomics datasets, mostly available through the PRIDE database, we assembled a reference expression map for 191 cancer cell lines and 246 clinical tumour samples, across 13 lineages. We found unique peptides identified only in tumour samples despite a much higher coverage in cell lines. These were mainly mapped to proteins related to regulation of signalling receptor activity. Correlations between baseline expression in cell lines and tumours were calculated. We found these to be highly similar across all samples with most similarity found within a given sample type. Integration of proteomics and transcriptomics data showed median correlation across cell lines to be 0.58 (range between 0.43 and 0.66). Additionally, in agreement with previous studies, variation in mRNA levels was often a poor predictor of changes in protein abundance. To our knowledge, this work constitutes the first meta-analysis focusing on cancer-related public proteomics datasets. We therefore also highlight shortcomings and limitations of such studies. All data is available through PRIDE dataset identifier PXD013455 and in Expression Atlas. |
format | Online Article Text |
id | pubmed-8065022 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-80650222021-05-05 An integrated landscape of protein expression in human cancer Jarnuczak, Andrew F. Najgebauer, Hanna Barzine, Mitra Kundu, Deepti J. Ghavidel, Fatemeh Perez-Riverol, Yasset Papatheodorou, Irene Brazma, Alvis Vizcaíno, Juan Antonio Sci Data Analysis Using 11 proteomics datasets, mostly available through the PRIDE database, we assembled a reference expression map for 191 cancer cell lines and 246 clinical tumour samples, across 13 lineages. We found unique peptides identified only in tumour samples despite a much higher coverage in cell lines. These were mainly mapped to proteins related to regulation of signalling receptor activity. Correlations between baseline expression in cell lines and tumours were calculated. We found these to be highly similar across all samples with most similarity found within a given sample type. Integration of proteomics and transcriptomics data showed median correlation across cell lines to be 0.58 (range between 0.43 and 0.66). Additionally, in agreement with previous studies, variation in mRNA levels was often a poor predictor of changes in protein abundance. To our knowledge, this work constitutes the first meta-analysis focusing on cancer-related public proteomics datasets. We therefore also highlight shortcomings and limitations of such studies. All data is available through PRIDE dataset identifier PXD013455 and in Expression Atlas. Nature Publishing Group UK 2021-04-23 /pmc/articles/PMC8065022/ /pubmed/33893311 http://dx.doi.org/10.1038/s41597-021-00890-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Analysis Jarnuczak, Andrew F. Najgebauer, Hanna Barzine, Mitra Kundu, Deepti J. Ghavidel, Fatemeh Perez-Riverol, Yasset Papatheodorou, Irene Brazma, Alvis Vizcaíno, Juan Antonio An integrated landscape of protein expression in human cancer |
title | An integrated landscape of protein expression in human cancer |
title_full | An integrated landscape of protein expression in human cancer |
title_fullStr | An integrated landscape of protein expression in human cancer |
title_full_unstemmed | An integrated landscape of protein expression in human cancer |
title_short | An integrated landscape of protein expression in human cancer |
title_sort | integrated landscape of protein expression in human cancer |
topic | Analysis |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8065022/ https://www.ncbi.nlm.nih.gov/pubmed/33893311 http://dx.doi.org/10.1038/s41597-021-00890-2 |
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