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Identification and Development of a Novel 4-Gene Immune-Related Signature to Predict Osteosarcoma Prognosis
Osteosarcoma (OS) is a malignant disease that develops rapidly and is associated with poor prognosis. Immunotherapy may provide new insights into clinical treatment strategies for OS. The purpose of this study was to identify immune-related genes that could predict OS prognosis. The gene expression...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7785859/ https://www.ncbi.nlm.nih.gov/pubmed/33425993 http://dx.doi.org/10.3389/fmolb.2020.608368 |
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author | Cao, Mingde Zhang, Junhui Xu, Hualiang Lin, Zhujian Chang, Hong Wang, Yuchen Huang, Xusheng Chen, Xiang Wang, Hua Song, Yancheng |
author_facet | Cao, Mingde Zhang, Junhui Xu, Hualiang Lin, Zhujian Chang, Hong Wang, Yuchen Huang, Xusheng Chen, Xiang Wang, Hua Song, Yancheng |
author_sort | Cao, Mingde |
collection | PubMed |
description | Osteosarcoma (OS) is a malignant disease that develops rapidly and is associated with poor prognosis. Immunotherapy may provide new insights into clinical treatment strategies for OS. The purpose of this study was to identify immune-related genes that could predict OS prognosis. The gene expression profiles and clinical data of 84 OS patients were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. According to non-negative matrix factorization, two molecular subtypes of immune-related genes, C1 and C2, were acquired, and 597 differentially expressed genes between C1 and C2 were identified. Univariate Cox analysis was performed to get 14 genes associated with survival, and 4 genes (GJA5, APBB1IP, NPC2, and FKBP11) obtained through least absolute shrinkage and selection operator (LASSO)-Cox regression were used to construct a 4-gene signature as a prognostic risk model. The results showed that high FKBP11 expression was correlated with high risk (a risk factor), and that high GJA5, APBB1IP, or NPC2 expression was associated with low risk (protective factors). The testing cohort and entire TARGET cohort were used for internal verification, and the independent GSE21257 cohort was used for external validation. The study suggested that the model we constructed was reliable and performed well in predicting OS risk. The functional enrichment of the signature was studied through gene set enrichment analysis, and it was found that the risk score was related to the immune pathway. In summary, our comprehensive study found that the 4-gene signature could be used to predict OS prognosis, and new biomarkers of great significance for understanding the therapeutic targets of OS were identified. |
format | Online Article Text |
id | pubmed-7785859 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-77858592021-01-07 Identification and Development of a Novel 4-Gene Immune-Related Signature to Predict Osteosarcoma Prognosis Cao, Mingde Zhang, Junhui Xu, Hualiang Lin, Zhujian Chang, Hong Wang, Yuchen Huang, Xusheng Chen, Xiang Wang, Hua Song, Yancheng Front Mol Biosci Molecular Biosciences Osteosarcoma (OS) is a malignant disease that develops rapidly and is associated with poor prognosis. Immunotherapy may provide new insights into clinical treatment strategies for OS. The purpose of this study was to identify immune-related genes that could predict OS prognosis. The gene expression profiles and clinical data of 84 OS patients were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. According to non-negative matrix factorization, two molecular subtypes of immune-related genes, C1 and C2, were acquired, and 597 differentially expressed genes between C1 and C2 were identified. Univariate Cox analysis was performed to get 14 genes associated with survival, and 4 genes (GJA5, APBB1IP, NPC2, and FKBP11) obtained through least absolute shrinkage and selection operator (LASSO)-Cox regression were used to construct a 4-gene signature as a prognostic risk model. The results showed that high FKBP11 expression was correlated with high risk (a risk factor), and that high GJA5, APBB1IP, or NPC2 expression was associated with low risk (protective factors). The testing cohort and entire TARGET cohort were used for internal verification, and the independent GSE21257 cohort was used for external validation. The study suggested that the model we constructed was reliable and performed well in predicting OS risk. The functional enrichment of the signature was studied through gene set enrichment analysis, and it was found that the risk score was related to the immune pathway. In summary, our comprehensive study found that the 4-gene signature could be used to predict OS prognosis, and new biomarkers of great significance for understanding the therapeutic targets of OS were identified. Frontiers Media S.A. 2020-12-23 /pmc/articles/PMC7785859/ /pubmed/33425993 http://dx.doi.org/10.3389/fmolb.2020.608368 Text en Copyright © 2020 Cao, Zhang, Xu, Lin, Chang, Wang, Huang, Chen, Wang and Song. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Molecular Biosciences Cao, Mingde Zhang, Junhui Xu, Hualiang Lin, Zhujian Chang, Hong Wang, Yuchen Huang, Xusheng Chen, Xiang Wang, Hua Song, Yancheng Identification and Development of a Novel 4-Gene Immune-Related Signature to Predict Osteosarcoma Prognosis |
title | Identification and Development of a Novel 4-Gene Immune-Related Signature to Predict Osteosarcoma Prognosis |
title_full | Identification and Development of a Novel 4-Gene Immune-Related Signature to Predict Osteosarcoma Prognosis |
title_fullStr | Identification and Development of a Novel 4-Gene Immune-Related Signature to Predict Osteosarcoma Prognosis |
title_full_unstemmed | Identification and Development of a Novel 4-Gene Immune-Related Signature to Predict Osteosarcoma Prognosis |
title_short | Identification and Development of a Novel 4-Gene Immune-Related Signature to Predict Osteosarcoma Prognosis |
title_sort | identification and development of a novel 4-gene immune-related signature to predict osteosarcoma prognosis |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7785859/ https://www.ncbi.nlm.nih.gov/pubmed/33425993 http://dx.doi.org/10.3389/fmolb.2020.608368 |
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