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Development of personalized classifier based on metastasis and the immune microenvironment to predict the prognosis of osteosarcoma patients

BACKGROUND: Osteosarcoma is a common malignant bone tumor with a poor prognosis. The progression and metastasis of osteosarcoma are significantly influenced by the tumor microenvironment (TME). This study aimed to develop a personalized classifier based on metastasis and immune cells in the TME to a...

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Autores principales: Zhang, Zhifeng, Yuan, Jianyong, Wang, Yi, Zhang, Yanquan, Guan, Zhengmao, Su, Xu, Wang, Yizhou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9843316/
https://www.ncbi.nlm.nih.gov/pubmed/36660665
http://dx.doi.org/10.21037/atm-22-5856
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author Zhang, Zhifeng
Yuan, Jianyong
Wang, Yi
Zhang, Yanquan
Guan, Zhengmao
Su, Xu
Wang, Yizhou
author_facet Zhang, Zhifeng
Yuan, Jianyong
Wang, Yi
Zhang, Yanquan
Guan, Zhengmao
Su, Xu
Wang, Yizhou
author_sort Zhang, Zhifeng
collection PubMed
description BACKGROUND: Osteosarcoma is a common malignant bone tumor with a poor prognosis. The progression and metastasis of osteosarcoma are significantly influenced by the tumor microenvironment (TME). This study aimed to develop a personalized classifier based on metastasis and immune cells in the TME to achieve better prognostic prediction in osteosarcoma. METHODS: Firstly, osteosarcoma metastasis-related differentially expressed genes (DEGs) and infiltrating immune cells in the TME were analyzed using a series of bioinformatics methods. The metastasis-related gene signature (MRS) and TME score of osteosarcoma patients were then developed. On this basis, a personalized MRS-TME classifier was constructed and validated in other clinical cohorts and different subgroups. In addition, the relationship between the MRS-related genes and the immune microenvironment was also clarified. Finally, the signaling pathways and immune response genes in osteosarcoma patients among different MRS-TME subgroups were analyzed to explore the underlying molecular mechanism. RESULTS: We first identified the metastasis-related DEGs in osteosarcoma, which were primarily involved in the muscle system process, calcium ion homeostasis, cell chemotaxis, and leukocyte migration. A personalized MRS-TME classifier was then constructed by integrating the MRS (10 genes) and TME (six immune cells) scores. The MRS-TME classifier demonstrated a potent capacity of predicting the survival prognosis in diverse osteosarcoma cohorts as well as in the clinical feature subgroups. The MRS score was negatively associated with the TME score, and patients in the MRS(low)/TME(high) subgroup exhibited a better prognosis compared to all other subgroups. Significant differences existed between the cellular signaling pathways and immune response profiles among the different MRS-TME subgroups, especially in relation to the metabolism-related biological processes and the inflammatory response. CONCLUSIONS: The MRS-TME classifier might be a beneficial tool to aid in the prognostic evaluation and risk stratification of osteosarcoma patients.
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spelling pubmed-98433162023-01-18 Development of personalized classifier based on metastasis and the immune microenvironment to predict the prognosis of osteosarcoma patients Zhang, Zhifeng Yuan, Jianyong Wang, Yi Zhang, Yanquan Guan, Zhengmao Su, Xu Wang, Yizhou Ann Transl Med Original Article BACKGROUND: Osteosarcoma is a common malignant bone tumor with a poor prognosis. The progression and metastasis of osteosarcoma are significantly influenced by the tumor microenvironment (TME). This study aimed to develop a personalized classifier based on metastasis and immune cells in the TME to achieve better prognostic prediction in osteosarcoma. METHODS: Firstly, osteosarcoma metastasis-related differentially expressed genes (DEGs) and infiltrating immune cells in the TME were analyzed using a series of bioinformatics methods. The metastasis-related gene signature (MRS) and TME score of osteosarcoma patients were then developed. On this basis, a personalized MRS-TME classifier was constructed and validated in other clinical cohorts and different subgroups. In addition, the relationship between the MRS-related genes and the immune microenvironment was also clarified. Finally, the signaling pathways and immune response genes in osteosarcoma patients among different MRS-TME subgroups were analyzed to explore the underlying molecular mechanism. RESULTS: We first identified the metastasis-related DEGs in osteosarcoma, which were primarily involved in the muscle system process, calcium ion homeostasis, cell chemotaxis, and leukocyte migration. A personalized MRS-TME classifier was then constructed by integrating the MRS (10 genes) and TME (six immune cells) scores. The MRS-TME classifier demonstrated a potent capacity of predicting the survival prognosis in diverse osteosarcoma cohorts as well as in the clinical feature subgroups. The MRS score was negatively associated with the TME score, and patients in the MRS(low)/TME(high) subgroup exhibited a better prognosis compared to all other subgroups. Significant differences existed between the cellular signaling pathways and immune response profiles among the different MRS-TME subgroups, especially in relation to the metabolism-related biological processes and the inflammatory response. CONCLUSIONS: The MRS-TME classifier might be a beneficial tool to aid in the prognostic evaluation and risk stratification of osteosarcoma patients. AME Publishing Company 2022-12 /pmc/articles/PMC9843316/ /pubmed/36660665 http://dx.doi.org/10.21037/atm-22-5856 Text en 2022 Annals of Translational Medicine. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Zhang, Zhifeng
Yuan, Jianyong
Wang, Yi
Zhang, Yanquan
Guan, Zhengmao
Su, Xu
Wang, Yizhou
Development of personalized classifier based on metastasis and the immune microenvironment to predict the prognosis of osteosarcoma patients
title Development of personalized classifier based on metastasis and the immune microenvironment to predict the prognosis of osteosarcoma patients
title_full Development of personalized classifier based on metastasis and the immune microenvironment to predict the prognosis of osteosarcoma patients
title_fullStr Development of personalized classifier based on metastasis and the immune microenvironment to predict the prognosis of osteosarcoma patients
title_full_unstemmed Development of personalized classifier based on metastasis and the immune microenvironment to predict the prognosis of osteosarcoma patients
title_short Development of personalized classifier based on metastasis and the immune microenvironment to predict the prognosis of osteosarcoma patients
title_sort development of personalized classifier based on metastasis and the immune microenvironment to predict the prognosis of osteosarcoma patients
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9843316/
https://www.ncbi.nlm.nih.gov/pubmed/36660665
http://dx.doi.org/10.21037/atm-22-5856
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