Estimating the predictive power of silent mutations on cancer classification and prognosis

Abstract In recent years it has been shown that silent mutations, in and out of the coding region, can affect gene expression and may be related to tumorigenesis and cancer cell fitness. However, the predictive ability of these mutations for cancer type diagnosis and prognosis has not been evaluated...

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Autores principales:	Tal Gutman, Guy Goren, Omri Efroni, Tamir Tuller
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Genetics QH426-470
Acceso en línea:	https://doaj.org/article/203ba88f31ec479484064f94e1944f18
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spelling	oai:doaj.org-article:203ba88f31ec479484064f94e1944f182021-12-02T15:07:50ZEstimating the predictive power of silent mutations on cancer classification and prognosis10.1038/s41525-021-00229-12056-7944https://doaj.org/article/203ba88f31ec479484064f94e1944f182021-08-01T00:00:00Zhttps://doi.org/10.1038/s41525-021-00229-1https://doaj.org/toc/2056-7944Abstract In recent years it has been shown that silent mutations, in and out of the coding region, can affect gene expression and may be related to tumorigenesis and cancer cell fitness. However, the predictive ability of these mutations for cancer type diagnosis and prognosis has not been evaluated yet. In the current study, based on the analysis of 9,915 cancer genomes and approximately three million mutations, we provide a comprehensive quantitative evaluation of the predictive power of various types of silent and non-silent mutations over cancer classification and prognosis. The results indicate that silent-mutation models outperform the equivalent null models in classifying all examined cancer types and in estimating the probability of survival 10 years after the initial diagnosis. Additionally, combining both non-silent and silent mutations achieved the best classification results for 68% of the cancer types and the best survival estimation results for up to nine years after the diagnosis. Thus, silent mutations hold considerable predictive power over both cancer classification and prognosis, most likely due to their effect on gene expression. It is highly advised that silent mutations are integrated in cancer research in order to unravel the full genomic landscape of cancer and its ramifications on cancer fitness.Tal GutmanGuy GorenOmri EfroniTamir TullerNature PortfolioarticleMedicineRGeneticsQH426-470ENnpj Genomic Medicine, Vol 6, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Genetics QH426-470
spellingShingle	Medicine R Genetics QH426-470 Tal Gutman Guy Goren Omri Efroni Tamir Tuller Estimating the predictive power of silent mutations on cancer classification and prognosis
description	Abstract In recent years it has been shown that silent mutations, in and out of the coding region, can affect gene expression and may be related to tumorigenesis and cancer cell fitness. However, the predictive ability of these mutations for cancer type diagnosis and prognosis has not been evaluated yet. In the current study, based on the analysis of 9,915 cancer genomes and approximately three million mutations, we provide a comprehensive quantitative evaluation of the predictive power of various types of silent and non-silent mutations over cancer classification and prognosis. The results indicate that silent-mutation models outperform the equivalent null models in classifying all examined cancer types and in estimating the probability of survival 10 years after the initial diagnosis. Additionally, combining both non-silent and silent mutations achieved the best classification results for 68% of the cancer types and the best survival estimation results for up to nine years after the diagnosis. Thus, silent mutations hold considerable predictive power over both cancer classification and prognosis, most likely due to their effect on gene expression. It is highly advised that silent mutations are integrated in cancer research in order to unravel the full genomic landscape of cancer and its ramifications on cancer fitness.
format	article
author	Tal Gutman Guy Goren Omri Efroni Tamir Tuller
author_facet	Tal Gutman Guy Goren Omri Efroni Tamir Tuller
author_sort	Tal Gutman
title	Estimating the predictive power of silent mutations on cancer classification and prognosis
title_short	Estimating the predictive power of silent mutations on cancer classification and prognosis
title_full	Estimating the predictive power of silent mutations on cancer classification and prognosis
title_fullStr	Estimating the predictive power of silent mutations on cancer classification and prognosis
title_full_unstemmed	Estimating the predictive power of silent mutations on cancer classification and prognosis
title_sort	estimating the predictive power of silent mutations on cancer classification and prognosis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/203ba88f31ec479484064f94e1944f18
work_keys_str_mv	AT talgutman estimatingthepredictivepowerofsilentmutationsoncancerclassificationandprognosis AT guygoren estimatingthepredictivepowerofsilentmutationsoncancerclassificationandprognosis AT omriefroni estimatingthepredictivepowerofsilentmutationsoncancerclassificationandprognosis AT tamirtuller estimatingthepredictivepowerofsilentmutationsoncancerclassificationandprognosis
_version_	1718388371469893632

Estimating the predictive power of silent mutations on cancer classification and prognosis

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