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Prognostic gene expression signatures of breast cancer are lacking a sensible biological meaning

The identification of prognostic biomarkers for predicting cancer progression is an important problem for two reasons. First, such biomarkers find practical application in a clinical context for the treatment of patients. Second, interrogation of the biomarkers themselves is assumed to lead to novel...

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Autores principales: Manjang, Kalifa, Tripathi, Shailesh, Yli-Harja, Olli, Dehmer, Matthias, Glazko, Galina, Emmert-Streib, Frank
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794581/
https://www.ncbi.nlm.nih.gov/pubmed/33420139
http://dx.doi.org/10.1038/s41598-020-79375-y
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author Manjang, Kalifa
Tripathi, Shailesh
Yli-Harja, Olli
Dehmer, Matthias
Glazko, Galina
Emmert-Streib, Frank
author_facet Manjang, Kalifa
Tripathi, Shailesh
Yli-Harja, Olli
Dehmer, Matthias
Glazko, Galina
Emmert-Streib, Frank
author_sort Manjang, Kalifa
collection PubMed
description The identification of prognostic biomarkers for predicting cancer progression is an important problem for two reasons. First, such biomarkers find practical application in a clinical context for the treatment of patients. Second, interrogation of the biomarkers themselves is assumed to lead to novel insights of disease mechanisms and the underlying molecular processes that cause the pathological behavior. For breast cancer, many signatures based on gene expression values have been reported to be associated with overall survival. Consequently, such signatures have been used for suggesting biological explanations of breast cancer and drug mechanisms. In this paper, we demonstrate for a large number of breast cancer signatures that such an implication is not justified. Our approach eliminates systematically all traces of biological meaning of signature genes and shows that among the remaining genes, surrogate gene sets can be formed with indistinguishable prognostic prediction capabilities and opposite biological meaning. Hence, our results demonstrate that none of the studied signatures has a sensible biological interpretation or meaning with respect to disease etiology. Overall, this shows that prognostic signatures are black-box models with sensible predictions of breast cancer outcome but no value for revealing causal connections. Furthermore, we show that the number of such surrogate gene sets is not small but very large.
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spelling pubmed-77945812021-01-12 Prognostic gene expression signatures of breast cancer are lacking a sensible biological meaning Manjang, Kalifa Tripathi, Shailesh Yli-Harja, Olli Dehmer, Matthias Glazko, Galina Emmert-Streib, Frank Sci Rep Article The identification of prognostic biomarkers for predicting cancer progression is an important problem for two reasons. First, such biomarkers find practical application in a clinical context for the treatment of patients. Second, interrogation of the biomarkers themselves is assumed to lead to novel insights of disease mechanisms and the underlying molecular processes that cause the pathological behavior. For breast cancer, many signatures based on gene expression values have been reported to be associated with overall survival. Consequently, such signatures have been used for suggesting biological explanations of breast cancer and drug mechanisms. In this paper, we demonstrate for a large number of breast cancer signatures that such an implication is not justified. Our approach eliminates systematically all traces of biological meaning of signature genes and shows that among the remaining genes, surrogate gene sets can be formed with indistinguishable prognostic prediction capabilities and opposite biological meaning. Hence, our results demonstrate that none of the studied signatures has a sensible biological interpretation or meaning with respect to disease etiology. Overall, this shows that prognostic signatures are black-box models with sensible predictions of breast cancer outcome but no value for revealing causal connections. Furthermore, we show that the number of such surrogate gene sets is not small but very large. Nature Publishing Group UK 2021-01-08 /pmc/articles/PMC7794581/ /pubmed/33420139 http://dx.doi.org/10.1038/s41598-020-79375-y Text en © The Author(s) 2021 Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Manjang, Kalifa
Tripathi, Shailesh
Yli-Harja, Olli
Dehmer, Matthias
Glazko, Galina
Emmert-Streib, Frank
Prognostic gene expression signatures of breast cancer are lacking a sensible biological meaning
title Prognostic gene expression signatures of breast cancer are lacking a sensible biological meaning
title_full Prognostic gene expression signatures of breast cancer are lacking a sensible biological meaning
title_fullStr Prognostic gene expression signatures of breast cancer are lacking a sensible biological meaning
title_full_unstemmed Prognostic gene expression signatures of breast cancer are lacking a sensible biological meaning
title_short Prognostic gene expression signatures of breast cancer are lacking a sensible biological meaning
title_sort prognostic gene expression signatures of breast cancer are lacking a sensible biological meaning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794581/
https://www.ncbi.nlm.nih.gov/pubmed/33420139
http://dx.doi.org/10.1038/s41598-020-79375-y
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