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Enhancing identification of cancer types via lowly-expressed microRNAs
The primary function of microRNAs (miRNAs) is to maintain cell homeostasis. In cancerous tissues miRNAs’ expression undergo drastic alterations. In this study, we use miRNA expression profiles from The Cancer Genome Atlas of 24 cancer types and 3 healthy tissues, collected from >8500 samples. We...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5435932/ https://www.ncbi.nlm.nih.gov/pubmed/28379430 http://dx.doi.org/10.1093/nar/gkx210 |
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author | Rasnic, Roni Linial, Nathan Linial, Michal |
author_facet | Rasnic, Roni Linial, Nathan Linial, Michal |
author_sort | Rasnic, Roni |
collection | PubMed |
description | The primary function of microRNAs (miRNAs) is to maintain cell homeostasis. In cancerous tissues miRNAs’ expression undergo drastic alterations. In this study, we use miRNA expression profiles from The Cancer Genome Atlas of 24 cancer types and 3 healthy tissues, collected from >8500 samples. We seek to classify the cancer's origin and tissue identification using the expression from 1046 reported miRNAs. Despite an apparent uniform appearance of miRNAs among cancerous samples, we recover indispensable information from lowly expressed miRNAs regarding the cancer/tissue types. Multiclass support vector machine classification yields an average recall of 58% in identifying the correct tissue and tumor types. Data discretization had led to substantial improvement, reaching an average recall of 91% (95% median). We propose a straightforward protocol as a crucial step in classifying tumors of unknown primary origin. Our counter-intuitive conclusion is that in almost all cancer types, highly expressing miRNAs mask the significant signal that lower expressed miRNAs provide. |
format | Online Article Text |
id | pubmed-5435932 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-54359322017-05-22 Enhancing identification of cancer types via lowly-expressed microRNAs Rasnic, Roni Linial, Nathan Linial, Michal Nucleic Acids Res Computational Biology The primary function of microRNAs (miRNAs) is to maintain cell homeostasis. In cancerous tissues miRNAs’ expression undergo drastic alterations. In this study, we use miRNA expression profiles from The Cancer Genome Atlas of 24 cancer types and 3 healthy tissues, collected from >8500 samples. We seek to classify the cancer's origin and tissue identification using the expression from 1046 reported miRNAs. Despite an apparent uniform appearance of miRNAs among cancerous samples, we recover indispensable information from lowly expressed miRNAs regarding the cancer/tissue types. Multiclass support vector machine classification yields an average recall of 58% in identifying the correct tissue and tumor types. Data discretization had led to substantial improvement, reaching an average recall of 91% (95% median). We propose a straightforward protocol as a crucial step in classifying tumors of unknown primary origin. Our counter-intuitive conclusion is that in almost all cancer types, highly expressing miRNAs mask the significant signal that lower expressed miRNAs provide. Oxford University Press 2017-05-19 2017-04-03 /pmc/articles/PMC5435932/ /pubmed/28379430 http://dx.doi.org/10.1093/nar/gkx210 Text en © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Computational Biology Rasnic, Roni Linial, Nathan Linial, Michal Enhancing identification of cancer types via lowly-expressed microRNAs |
title | Enhancing identification of cancer types via lowly-expressed microRNAs |
title_full | Enhancing identification of cancer types via lowly-expressed microRNAs |
title_fullStr | Enhancing identification of cancer types via lowly-expressed microRNAs |
title_full_unstemmed | Enhancing identification of cancer types via lowly-expressed microRNAs |
title_short | Enhancing identification of cancer types via lowly-expressed microRNAs |
title_sort | enhancing identification of cancer types via lowly-expressed micrornas |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5435932/ https://www.ncbi.nlm.nih.gov/pubmed/28379430 http://dx.doi.org/10.1093/nar/gkx210 |
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