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Protocol to identify novel immunotherapy biomarkers based on transcriptomic data in human cancers
Immune checkpoint inhibitors have transformed the management of advanced cancers, but biomarkers for the prediction of therapeutic responses have not been fully uncovered. Here, we provide a step-by-step approach for the identification of novel biomarkers from public transcriptomic datasets. We comp...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10173013/ https://www.ncbi.nlm.nih.gov/pubmed/37119142 http://dx.doi.org/10.1016/j.xpro.2023.102258 |
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author | Mei, Jie Cai, Yun Xu, Rui Zhu, Yichao Zhao, Xinyuan Zhang, Yan Mao, Wenjun Xu, Junying Yin, Yongmei |
author_facet | Mei, Jie Cai, Yun Xu, Rui Zhu, Yichao Zhao, Xinyuan Zhang, Yan Mao, Wenjun Xu, Junying Yin, Yongmei |
author_sort | Mei, Jie |
collection | PubMed |
description | Immune checkpoint inhibitors have transformed the management of advanced cancers, but biomarkers for the prediction of therapeutic responses have not been fully uncovered. Here, we provide a step-by-step approach for the identification of novel biomarkers from public transcriptomic datasets. We comprehensively summarize the available transcriptomic datasets containing immunotherapy information and describe the necessary procedures to evaluate the effectiveness of a novel immunotherapy biomarker, which may accelerate the identification of novel immunotherapy biomarkers. For complete details on the use and execution of this protocol, please refer to Mei et al.(1) |
format | Online Article Text |
id | pubmed-10173013 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-101730132023-05-12 Protocol to identify novel immunotherapy biomarkers based on transcriptomic data in human cancers Mei, Jie Cai, Yun Xu, Rui Zhu, Yichao Zhao, Xinyuan Zhang, Yan Mao, Wenjun Xu, Junying Yin, Yongmei STAR Protoc Protocol Immune checkpoint inhibitors have transformed the management of advanced cancers, but biomarkers for the prediction of therapeutic responses have not been fully uncovered. Here, we provide a step-by-step approach for the identification of novel biomarkers from public transcriptomic datasets. We comprehensively summarize the available transcriptomic datasets containing immunotherapy information and describe the necessary procedures to evaluate the effectiveness of a novel immunotherapy biomarker, which may accelerate the identification of novel immunotherapy biomarkers. For complete details on the use and execution of this protocol, please refer to Mei et al.(1) Elsevier 2023-04-28 /pmc/articles/PMC10173013/ /pubmed/37119142 http://dx.doi.org/10.1016/j.xpro.2023.102258 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Protocol Mei, Jie Cai, Yun Xu, Rui Zhu, Yichao Zhao, Xinyuan Zhang, Yan Mao, Wenjun Xu, Junying Yin, Yongmei Protocol to identify novel immunotherapy biomarkers based on transcriptomic data in human cancers |
title | Protocol to identify novel immunotherapy biomarkers based on transcriptomic data in human cancers |
title_full | Protocol to identify novel immunotherapy biomarkers based on transcriptomic data in human cancers |
title_fullStr | Protocol to identify novel immunotherapy biomarkers based on transcriptomic data in human cancers |
title_full_unstemmed | Protocol to identify novel immunotherapy biomarkers based on transcriptomic data in human cancers |
title_short | Protocol to identify novel immunotherapy biomarkers based on transcriptomic data in human cancers |
title_sort | protocol to identify novel immunotherapy biomarkers based on transcriptomic data in human cancers |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10173013/ https://www.ncbi.nlm.nih.gov/pubmed/37119142 http://dx.doi.org/10.1016/j.xpro.2023.102258 |
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