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Predicting radiotherapy response for patients with soft tissue sarcoma by developing a molecular signature
Soft tissue sarcomas are rare and aggressive tumors arising from connective tissues. Adjuvant radiotherapy is a commonly used treatment approach for the majority of sarcomas. We attempted to identify a gene signature that can predict radiosensitive patients who are most likely to have a better treat...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
D.A. Spandidos
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5780036/ https://www.ncbi.nlm.nih.gov/pubmed/29048650 http://dx.doi.org/10.3892/or.2017.5999 |
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author | Tang, Zaixiang Zeng, Qinghua Li, Yan Zhang, Xinyan Suto, Mark J. Xu, Bo Yi, Nengjun |
author_facet | Tang, Zaixiang Zeng, Qinghua Li, Yan Zhang, Xinyan Suto, Mark J. Xu, Bo Yi, Nengjun |
author_sort | Tang, Zaixiang |
collection | PubMed |
description | Soft tissue sarcomas are rare and aggressive tumors arising from connective tissues. Adjuvant radiotherapy is a commonly used treatment approach for the majority of sarcomas. We attempted to identify a gene signature that can predict radiosensitive patients who are most likely to have a better treatment response from radiotherapy, compared with disease progression. Using the publicly available data of soft tissue sarcoma from The Cancer Genome Atlas, we developed a cross-validation procedure to identify a predictive gene signature for radiosensitivity. The results showed that the predicted radiosensitive patients who received radiotherapy had significantly improved treatment response. We further provide supportive evidence to validate our sensitivity prediction. Results showed that the predicted radiosensitive patients who received radiotherapy had significantly improved survival than patients who did not. ROC analysis showed that the developed gene signature had a powerful prediction on treatment response. We further found that predicted radiosensitive patients who received radiotherapy had a significantly reduced rate of new tumor events. Finally, we validated our gene signature using a hierarchical cluster analysis, and found that the predicted sensitivities were well-matched with results from the cluster analysis. These results are consistent with our expectation, suggesting that the identified gene signature and radiosensitivity prediction are effective. The genes involved in the signature may provide a molecular basis for prognostic studies and radiotherapy target discovery. |
format | Online Article Text |
id | pubmed-5780036 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | D.A. Spandidos |
record_format | MEDLINE/PubMed |
spelling | pubmed-57800362018-02-12 Predicting radiotherapy response for patients with soft tissue sarcoma by developing a molecular signature Tang, Zaixiang Zeng, Qinghua Li, Yan Zhang, Xinyan Suto, Mark J. Xu, Bo Yi, Nengjun Oncol Rep Articles Soft tissue sarcomas are rare and aggressive tumors arising from connective tissues. Adjuvant radiotherapy is a commonly used treatment approach for the majority of sarcomas. We attempted to identify a gene signature that can predict radiosensitive patients who are most likely to have a better treatment response from radiotherapy, compared with disease progression. Using the publicly available data of soft tissue sarcoma from The Cancer Genome Atlas, we developed a cross-validation procedure to identify a predictive gene signature for radiosensitivity. The results showed that the predicted radiosensitive patients who received radiotherapy had significantly improved treatment response. We further provide supportive evidence to validate our sensitivity prediction. Results showed that the predicted radiosensitive patients who received radiotherapy had significantly improved survival than patients who did not. ROC analysis showed that the developed gene signature had a powerful prediction on treatment response. We further found that predicted radiosensitive patients who received radiotherapy had a significantly reduced rate of new tumor events. Finally, we validated our gene signature using a hierarchical cluster analysis, and found that the predicted sensitivities were well-matched with results from the cluster analysis. These results are consistent with our expectation, suggesting that the identified gene signature and radiosensitivity prediction are effective. The genes involved in the signature may provide a molecular basis for prognostic studies and radiotherapy target discovery. D.A. Spandidos 2017-11 2017-09-25 /pmc/articles/PMC5780036/ /pubmed/29048650 http://dx.doi.org/10.3892/or.2017.5999 Text en Copyright: © Tang et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. |
spellingShingle | Articles Tang, Zaixiang Zeng, Qinghua Li, Yan Zhang, Xinyan Suto, Mark J. Xu, Bo Yi, Nengjun Predicting radiotherapy response for patients with soft tissue sarcoma by developing a molecular signature |
title | Predicting radiotherapy response for patients with soft tissue sarcoma by developing a molecular signature |
title_full | Predicting radiotherapy response for patients with soft tissue sarcoma by developing a molecular signature |
title_fullStr | Predicting radiotherapy response for patients with soft tissue sarcoma by developing a molecular signature |
title_full_unstemmed | Predicting radiotherapy response for patients with soft tissue sarcoma by developing a molecular signature |
title_short | Predicting radiotherapy response for patients with soft tissue sarcoma by developing a molecular signature |
title_sort | predicting radiotherapy response for patients with soft tissue sarcoma by developing a molecular signature |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5780036/ https://www.ncbi.nlm.nih.gov/pubmed/29048650 http://dx.doi.org/10.3892/or.2017.5999 |
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