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Globally ncRNAs Expression Profiling of TNBC and Screening of Functional lncRNA
One of the most well-known cancer subtypes worldwide is triple-negative breast cancer (TNBC) which has reduced prediction due to its antagonistic biotic actions and target’s deficiency for the treatment. The current work aims to discover the countenance outlines and possible roles of lncRNAs in the...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7860147/ https://www.ncbi.nlm.nih.gov/pubmed/33553110 http://dx.doi.org/10.3389/fbioe.2020.523127 |
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author | Kaushik, Aman Chandra Mehmood, Aamir Wang, Xiangeng Wei, Dong-Qing Dai, Xiaofeng |
author_facet | Kaushik, Aman Chandra Mehmood, Aamir Wang, Xiangeng Wei, Dong-Qing Dai, Xiaofeng |
author_sort | Kaushik, Aman Chandra |
collection | PubMed |
description | One of the most well-known cancer subtypes worldwide is triple-negative breast cancer (TNBC) which has reduced prediction due to its antagonistic biotic actions and target’s deficiency for the treatment. The current work aims to discover the countenance outlines and possible roles of lncRNAs in the TNBC via computational approaches. Long non-coding RNAs (lncRNAs) exert profound biological functions and are widely applied as prognostic features in cancer. We aim to identify a prognostic lncRNA signature for the TNBC. First, samples were filtered out with inadequate tumor purity and retrieved the lncRNA expression data stored in the TANRIC catalog. TNBC sufferers were divided into two prognostic classes which were dependent on their survival time (shorter or longer than 3 years). Random forest was utilized to select lncRNA features based on the lncRNAs differential expression between shorter and longer groups. The Stochastic gradient boosting method was used to construct the predictive model. As a whole, 353 lncRNAs were differentially transcribed amongst the shorter and longer groups. Using the recursive feature elimination, two lncRNAs were further selected. Trained by stochastic gradient boosting, we reached the highest accuracy of 69.69% and area under the curve of 0.6475. Our findings showed that the two-lncRNA signs can be proved as potential biomarkers for the prognostic grouping of TNBC’s sufferers. Many lncRNAs remained dysregulated in TNBC, while most of them are likely play a role in cancer biology. Some of these lncRNAs were linked to TNBC’s prediction, which makes them likely to be promising biomarkers. |
format | Online Article Text |
id | pubmed-7860147 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78601472021-02-05 Globally ncRNAs Expression Profiling of TNBC and Screening of Functional lncRNA Kaushik, Aman Chandra Mehmood, Aamir Wang, Xiangeng Wei, Dong-Qing Dai, Xiaofeng Front Bioeng Biotechnol Bioengineering and Biotechnology One of the most well-known cancer subtypes worldwide is triple-negative breast cancer (TNBC) which has reduced prediction due to its antagonistic biotic actions and target’s deficiency for the treatment. The current work aims to discover the countenance outlines and possible roles of lncRNAs in the TNBC via computational approaches. Long non-coding RNAs (lncRNAs) exert profound biological functions and are widely applied as prognostic features in cancer. We aim to identify a prognostic lncRNA signature for the TNBC. First, samples were filtered out with inadequate tumor purity and retrieved the lncRNA expression data stored in the TANRIC catalog. TNBC sufferers were divided into two prognostic classes which were dependent on their survival time (shorter or longer than 3 years). Random forest was utilized to select lncRNA features based on the lncRNAs differential expression between shorter and longer groups. The Stochastic gradient boosting method was used to construct the predictive model. As a whole, 353 lncRNAs were differentially transcribed amongst the shorter and longer groups. Using the recursive feature elimination, two lncRNAs were further selected. Trained by stochastic gradient boosting, we reached the highest accuracy of 69.69% and area under the curve of 0.6475. Our findings showed that the two-lncRNA signs can be proved as potential biomarkers for the prognostic grouping of TNBC’s sufferers. Many lncRNAs remained dysregulated in TNBC, while most of them are likely play a role in cancer biology. Some of these lncRNAs were linked to TNBC’s prediction, which makes them likely to be promising biomarkers. Frontiers Media S.A. 2021-01-21 /pmc/articles/PMC7860147/ /pubmed/33553110 http://dx.doi.org/10.3389/fbioe.2020.523127 Text en Copyright © 2021 Kaushik, Mehmood, Wang, Wei and Dai. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioengineering and Biotechnology Kaushik, Aman Chandra Mehmood, Aamir Wang, Xiangeng Wei, Dong-Qing Dai, Xiaofeng Globally ncRNAs Expression Profiling of TNBC and Screening of Functional lncRNA |
title | Globally ncRNAs Expression Profiling of TNBC and Screening of Functional lncRNA |
title_full | Globally ncRNAs Expression Profiling of TNBC and Screening of Functional lncRNA |
title_fullStr | Globally ncRNAs Expression Profiling of TNBC and Screening of Functional lncRNA |
title_full_unstemmed | Globally ncRNAs Expression Profiling of TNBC and Screening of Functional lncRNA |
title_short | Globally ncRNAs Expression Profiling of TNBC and Screening of Functional lncRNA |
title_sort | globally ncrnas expression profiling of tnbc and screening of functional lncrna |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7860147/ https://www.ncbi.nlm.nih.gov/pubmed/33553110 http://dx.doi.org/10.3389/fbioe.2020.523127 |
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