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TIRSF: a web server for screening gene signatures to predict Tumor immunotherapy response
Immune checkpoint blockade (ICB) therapy has been successfully applied to clinically therapeutics in multiple cancers, but its efficacy varies greatly among different patients and cancer types. Therefore, the construction of gene signatures to identify patients who could benefit from ICB therapy is...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9252797/ https://www.ncbi.nlm.nih.gov/pubmed/35554556 http://dx.doi.org/10.1093/nar/gkac374 |
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author | Chen, Li Chen, Tianjian Zhang, Ya Lin, Haichen Wang, Ruihan Wang, Yihang Li, Hongyu Zuo, Zhixiang Ren, Jian Xie, Yubin |
author_facet | Chen, Li Chen, Tianjian Zhang, Ya Lin, Haichen Wang, Ruihan Wang, Yihang Li, Hongyu Zuo, Zhixiang Ren, Jian Xie, Yubin |
author_sort | Chen, Li |
collection | PubMed |
description | Immune checkpoint blockade (ICB) therapy has been successfully applied to clinically therapeutics in multiple cancers, but its efficacy varies greatly among different patients and cancer types. Therefore, the construction of gene signatures to identify patients who could benefit from ICB therapy is particularly important for precision cancer treatment. However, due to the lack of a user-friendly platform, the construction of such gene signatures is a great challenge for clinical investigators who have limited programming skills. In light of this challenge, we developed a web server called Tumor Immunotherapy Response Signature Finder(TIRSF) for the construction of gene signatures to predict ICB therapy response in cancer patients. TIRSF consists of three functional modules. The first module is the Signature Discovery module which provides signature construction and performance evaluation functionalities. The second is a module for response prediction based on the TIRSF signatures, which enables response prediction and prognostic analysis of immunotherapy samples. The last is a module for response prediction based on existing signatures. This module currently integrates 24 published signatures for ICB therapy response prediction. Together, all of above features can be freely accessed at http://tirsf.renlab.org/. |
format | Online Article Text |
id | pubmed-9252797 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-92527972022-07-05 TIRSF: a web server for screening gene signatures to predict Tumor immunotherapy response Chen, Li Chen, Tianjian Zhang, Ya Lin, Haichen Wang, Ruihan Wang, Yihang Li, Hongyu Zuo, Zhixiang Ren, Jian Xie, Yubin Nucleic Acids Res Web Server Issue Immune checkpoint blockade (ICB) therapy has been successfully applied to clinically therapeutics in multiple cancers, but its efficacy varies greatly among different patients and cancer types. Therefore, the construction of gene signatures to identify patients who could benefit from ICB therapy is particularly important for precision cancer treatment. However, due to the lack of a user-friendly platform, the construction of such gene signatures is a great challenge for clinical investigators who have limited programming skills. In light of this challenge, we developed a web server called Tumor Immunotherapy Response Signature Finder(TIRSF) for the construction of gene signatures to predict ICB therapy response in cancer patients. TIRSF consists of three functional modules. The first module is the Signature Discovery module which provides signature construction and performance evaluation functionalities. The second is a module for response prediction based on the TIRSF signatures, which enables response prediction and prognostic analysis of immunotherapy samples. The last is a module for response prediction based on existing signatures. This module currently integrates 24 published signatures for ICB therapy response prediction. Together, all of above features can be freely accessed at http://tirsf.renlab.org/. Oxford University Press 2022-05-12 /pmc/articles/PMC9252797/ /pubmed/35554556 http://dx.doi.org/10.1093/nar/gkac374 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://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 | Web Server Issue Chen, Li Chen, Tianjian Zhang, Ya Lin, Haichen Wang, Ruihan Wang, Yihang Li, Hongyu Zuo, Zhixiang Ren, Jian Xie, Yubin TIRSF: a web server for screening gene signatures to predict Tumor immunotherapy response |
title | TIRSF: a web server for screening gene signatures to predict Tumor immunotherapy response |
title_full | TIRSF: a web server for screening gene signatures to predict Tumor immunotherapy response |
title_fullStr | TIRSF: a web server for screening gene signatures to predict Tumor immunotherapy response |
title_full_unstemmed | TIRSF: a web server for screening gene signatures to predict Tumor immunotherapy response |
title_short | TIRSF: a web server for screening gene signatures to predict Tumor immunotherapy response |
title_sort | tirsf: a web server for screening gene signatures to predict tumor immunotherapy response |
topic | Web Server Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9252797/ https://www.ncbi.nlm.nih.gov/pubmed/35554556 http://dx.doi.org/10.1093/nar/gkac374 |
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