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Integrative analysis of TCGA data identifies miRNAs as drug-specific survival biomarkers
Biomarkers predictive of drug-specific outcomes are important tools for personalized medicine. In this study, we present an integrative analysis to identify miRNAs that are predictive of drug-specific survival outcome in cancer. Using the clinical data from TCGA, we defined subsets of cancer patient...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9042876/ https://www.ncbi.nlm.nih.gov/pubmed/35474090 http://dx.doi.org/10.1038/s41598-022-10662-6 |
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author | Lin, Shuting Zhou, Jie Xiao, Yiqiong Neary, Bridget Teng, Yong Qiu, Peng |
author_facet | Lin, Shuting Zhou, Jie Xiao, Yiqiong Neary, Bridget Teng, Yong Qiu, Peng |
author_sort | Lin, Shuting |
collection | PubMed |
description | Biomarkers predictive of drug-specific outcomes are important tools for personalized medicine. In this study, we present an integrative analysis to identify miRNAs that are predictive of drug-specific survival outcome in cancer. Using the clinical data from TCGA, we defined subsets of cancer patients who suffered from the same cancer and received the same drug treatment, which we call cancer-drug groups. We then used the miRNA expression data in TCGA to evaluate each miRNA’s ability to predict the survival outcome of patients in each cancer-drug group. As a result, the identified miRNAs are predictive of survival outcomes in a cancer-specific and drug-specific manner. Notably, most of the drug-specific miRNA survival markers and their target genes showed consistency in terms of correlations in their expression and their correlations with survival. Some of the identified miRNAs were supported by published literature in contexts of various cancers. We explored several additional breast cancer datasets that provided miRNA expression and survival data, and showed that our drug-specific miRNA survival markers for breast cancer were able to effectively stratify the prognosis of patients in those additional datasets. Together, this analysis revealed drug-specific miRNA markers for cancer survival, which can be promising tools toward personalized medicine. |
format | Online Article Text |
id | pubmed-9042876 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-90428762022-04-27 Integrative analysis of TCGA data identifies miRNAs as drug-specific survival biomarkers Lin, Shuting Zhou, Jie Xiao, Yiqiong Neary, Bridget Teng, Yong Qiu, Peng Sci Rep Article Biomarkers predictive of drug-specific outcomes are important tools for personalized medicine. In this study, we present an integrative analysis to identify miRNAs that are predictive of drug-specific survival outcome in cancer. Using the clinical data from TCGA, we defined subsets of cancer patients who suffered from the same cancer and received the same drug treatment, which we call cancer-drug groups. We then used the miRNA expression data in TCGA to evaluate each miRNA’s ability to predict the survival outcome of patients in each cancer-drug group. As a result, the identified miRNAs are predictive of survival outcomes in a cancer-specific and drug-specific manner. Notably, most of the drug-specific miRNA survival markers and their target genes showed consistency in terms of correlations in their expression and their correlations with survival. Some of the identified miRNAs were supported by published literature in contexts of various cancers. We explored several additional breast cancer datasets that provided miRNA expression and survival data, and showed that our drug-specific miRNA survival markers for breast cancer were able to effectively stratify the prognosis of patients in those additional datasets. Together, this analysis revealed drug-specific miRNA markers for cancer survival, which can be promising tools toward personalized medicine. Nature Publishing Group UK 2022-04-26 /pmc/articles/PMC9042876/ /pubmed/35474090 http://dx.doi.org/10.1038/s41598-022-10662-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Lin, Shuting Zhou, Jie Xiao, Yiqiong Neary, Bridget Teng, Yong Qiu, Peng Integrative analysis of TCGA data identifies miRNAs as drug-specific survival biomarkers |
title | Integrative analysis of TCGA data identifies miRNAs as drug-specific survival biomarkers |
title_full | Integrative analysis of TCGA data identifies miRNAs as drug-specific survival biomarkers |
title_fullStr | Integrative analysis of TCGA data identifies miRNAs as drug-specific survival biomarkers |
title_full_unstemmed | Integrative analysis of TCGA data identifies miRNAs as drug-specific survival biomarkers |
title_short | Integrative analysis of TCGA data identifies miRNAs as drug-specific survival biomarkers |
title_sort | integrative analysis of tcga data identifies mirnas as drug-specific survival biomarkers |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9042876/ https://www.ncbi.nlm.nih.gov/pubmed/35474090 http://dx.doi.org/10.1038/s41598-022-10662-6 |
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