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The tumor immune microenvironment and immune-related signature predict the chemotherapy response in patients with osteosarcoma
BACKGROUND: Genome-wide expression profiles have been shown to predict the response to chemotherapy. The purpose of this study was to develop a novel predictive signature for chemotherapy in patients with osteosarcoma. METHODS: We analysed the relevance of immune cell infiltration and gene expressio...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8138974/ https://www.ncbi.nlm.nih.gov/pubmed/34016089 http://dx.doi.org/10.1186/s12885-021-08328-z |
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author | He, Lijiang Yang, Hainan Huang, Jingshan |
author_facet | He, Lijiang Yang, Hainan Huang, Jingshan |
author_sort | He, Lijiang |
collection | PubMed |
description | BACKGROUND: Genome-wide expression profiles have been shown to predict the response to chemotherapy. The purpose of this study was to develop a novel predictive signature for chemotherapy in patients with osteosarcoma. METHODS: We analysed the relevance of immune cell infiltration and gene expression profiles of the tumor samples of good responders with those of poor responders from the TARGET and GEO databases. Immune cell infiltration was evaluated using a single-sample gene set enrichment analysis (ssGSEA) and the CIBERSORT algorithm between good and poor chemotherapy responders. Differentially expressed genes were identified based on the chemotherapy response. LASSO regression and binary logistic regression analyses were applied to select the differentially expressed immune-related genes (IRGs) and developed a predictive signature in the training cohort. A receiver operating characteristic (ROC) curve analysis was employed to assess and validate the predictive accuracy of the predictive signature in the validation cohort. RESULTS: The analysis of immune infiltration showed a positive relationship between high-level immune infiltration and good responders, and T follicular helper cells and CD8 T cells were significantly more abundant in good responders with osteosarcoma. Two hundred eighteen differentially expressed genes were detected between good and poor responders, and a five IRGs panel comprising TNFRSF9, CD70, EGFR, PDGFD and S100A6 was determined to show predictive power for the chemotherapy response. A chemotherapy-associated predictive signature was developed based on these five IRGs. The accuracy of the predictive signature was 0.832 for the training cohort and 0.720 for the validation cohort according to ROC analysis. CONCLUSIONS: The novel predictive signature constructed with five IRGs can be effectively utilized to predict chemotherapy responsiveness and help improve the efficacy of chemotherapy in patients with osteosarcoma. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-08328-z. |
format | Online Article Text |
id | pubmed-8138974 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-81389742021-05-21 The tumor immune microenvironment and immune-related signature predict the chemotherapy response in patients with osteosarcoma He, Lijiang Yang, Hainan Huang, Jingshan BMC Cancer Research BACKGROUND: Genome-wide expression profiles have been shown to predict the response to chemotherapy. The purpose of this study was to develop a novel predictive signature for chemotherapy in patients with osteosarcoma. METHODS: We analysed the relevance of immune cell infiltration and gene expression profiles of the tumor samples of good responders with those of poor responders from the TARGET and GEO databases. Immune cell infiltration was evaluated using a single-sample gene set enrichment analysis (ssGSEA) and the CIBERSORT algorithm between good and poor chemotherapy responders. Differentially expressed genes were identified based on the chemotherapy response. LASSO regression and binary logistic regression analyses were applied to select the differentially expressed immune-related genes (IRGs) and developed a predictive signature in the training cohort. A receiver operating characteristic (ROC) curve analysis was employed to assess and validate the predictive accuracy of the predictive signature in the validation cohort. RESULTS: The analysis of immune infiltration showed a positive relationship between high-level immune infiltration and good responders, and T follicular helper cells and CD8 T cells were significantly more abundant in good responders with osteosarcoma. Two hundred eighteen differentially expressed genes were detected between good and poor responders, and a five IRGs panel comprising TNFRSF9, CD70, EGFR, PDGFD and S100A6 was determined to show predictive power for the chemotherapy response. A chemotherapy-associated predictive signature was developed based on these five IRGs. The accuracy of the predictive signature was 0.832 for the training cohort and 0.720 for the validation cohort according to ROC analysis. CONCLUSIONS: The novel predictive signature constructed with five IRGs can be effectively utilized to predict chemotherapy responsiveness and help improve the efficacy of chemotherapy in patients with osteosarcoma. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-08328-z. BioMed Central 2021-05-21 /pmc/articles/PMC8138974/ /pubmed/34016089 http://dx.doi.org/10.1186/s12885-021-08328-z Text en © The Author(s) 2021 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research He, Lijiang Yang, Hainan Huang, Jingshan The tumor immune microenvironment and immune-related signature predict the chemotherapy response in patients with osteosarcoma |
title | The tumor immune microenvironment and immune-related signature predict the chemotherapy response in patients with osteosarcoma |
title_full | The tumor immune microenvironment and immune-related signature predict the chemotherapy response in patients with osteosarcoma |
title_fullStr | The tumor immune microenvironment and immune-related signature predict the chemotherapy response in patients with osteosarcoma |
title_full_unstemmed | The tumor immune microenvironment and immune-related signature predict the chemotherapy response in patients with osteosarcoma |
title_short | The tumor immune microenvironment and immune-related signature predict the chemotherapy response in patients with osteosarcoma |
title_sort | tumor immune microenvironment and immune-related signature predict the chemotherapy response in patients with osteosarcoma |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8138974/ https://www.ncbi.nlm.nih.gov/pubmed/34016089 http://dx.doi.org/10.1186/s12885-021-08328-z |
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