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A novel risk score model based on eight genes and a nomogram for predicting overall survival of patients with osteosarcoma
BACKGROUND: This study aims to identify a predictive model to predict survival outcomes of osteosarcoma (OS) patients. METHODS: A RNA sequencing dataset (the training set) and a microarray dataset (the validation set) were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7245838/ https://www.ncbi.nlm.nih.gov/pubmed/32448271 http://dx.doi.org/10.1186/s12885-020-06741-4 |
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author | Wu, Guangzhi Zhang, Minglei |
author_facet | Wu, Guangzhi Zhang, Minglei |
author_sort | Wu, Guangzhi |
collection | PubMed |
description | BACKGROUND: This study aims to identify a predictive model to predict survival outcomes of osteosarcoma (OS) patients. METHODS: A RNA sequencing dataset (the training set) and a microarray dataset (the validation set) were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, respectively. Differentially expressed genes (DEGs) between metastatic and non-metastatic OS samples were identified in training set. Prognosis-related DEGs were screened and optimized by support vector machine (SVM) recursive feature elimination. A SVM classifier was built to classify metastatic and non-metastatic OS samples. Independent prognosic genes were extracted by multivariate regression analysis to build a risk score model followed by performance evaluation in two datasets by Kaplan-Meier (KM) analysis. Independent clinical prognostic indicators were identified followed by nomogram analysis. Finally, functional analyses of survival-related genes were conducted. RESULT: Totally, 345 DEGs and 45 prognosis-related genes were screened. A SVM classifier could distinguish metastatic and non-metastatic OS samples. An eight-gene signature was an independent prognostic marker and used for constructing a risk score model. The risk score model could separate OS samples into high and low risk groups in two datasets (training set: log-rank p < 0.01, C-index = 0.805; validation set: log-rank p < 0.01, C-index = 0.797). Tumor metastasis and RS model status were independent prognostic factors and nomogram model exhibited accurate survival prediction for OS. Additionally, functional analyses of survival-related genes indicated they were closely associated with immune responses and cytokine-cytokine receptor interaction pathway. CONCLUSION: An eight-gene predictive model and nomogram were developed to predict OS prognosis. |
format | Online Article Text |
id | pubmed-7245838 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-72458382020-06-01 A novel risk score model based on eight genes and a nomogram for predicting overall survival of patients with osteosarcoma Wu, Guangzhi Zhang, Minglei BMC Cancer Research Article BACKGROUND: This study aims to identify a predictive model to predict survival outcomes of osteosarcoma (OS) patients. METHODS: A RNA sequencing dataset (the training set) and a microarray dataset (the validation set) were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, respectively. Differentially expressed genes (DEGs) between metastatic and non-metastatic OS samples were identified in training set. Prognosis-related DEGs were screened and optimized by support vector machine (SVM) recursive feature elimination. A SVM classifier was built to classify metastatic and non-metastatic OS samples. Independent prognosic genes were extracted by multivariate regression analysis to build a risk score model followed by performance evaluation in two datasets by Kaplan-Meier (KM) analysis. Independent clinical prognostic indicators were identified followed by nomogram analysis. Finally, functional analyses of survival-related genes were conducted. RESULT: Totally, 345 DEGs and 45 prognosis-related genes were screened. A SVM classifier could distinguish metastatic and non-metastatic OS samples. An eight-gene signature was an independent prognostic marker and used for constructing a risk score model. The risk score model could separate OS samples into high and low risk groups in two datasets (training set: log-rank p < 0.01, C-index = 0.805; validation set: log-rank p < 0.01, C-index = 0.797). Tumor metastasis and RS model status were independent prognostic factors and nomogram model exhibited accurate survival prediction for OS. Additionally, functional analyses of survival-related genes indicated they were closely associated with immune responses and cytokine-cytokine receptor interaction pathway. CONCLUSION: An eight-gene predictive model and nomogram were developed to predict OS prognosis. BioMed Central 2020-05-24 /pmc/articles/PMC7245838/ /pubmed/32448271 http://dx.doi.org/10.1186/s12885-020-06741-4 Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver (http://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 Article Wu, Guangzhi Zhang, Minglei A novel risk score model based on eight genes and a nomogram for predicting overall survival of patients with osteosarcoma |
title | A novel risk score model based on eight genes and a nomogram for predicting overall survival of patients with osteosarcoma |
title_full | A novel risk score model based on eight genes and a nomogram for predicting overall survival of patients with osteosarcoma |
title_fullStr | A novel risk score model based on eight genes and a nomogram for predicting overall survival of patients with osteosarcoma |
title_full_unstemmed | A novel risk score model based on eight genes and a nomogram for predicting overall survival of patients with osteosarcoma |
title_short | A novel risk score model based on eight genes and a nomogram for predicting overall survival of patients with osteosarcoma |
title_sort | novel risk score model based on eight genes and a nomogram for predicting overall survival of patients with osteosarcoma |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7245838/ https://www.ncbi.nlm.nih.gov/pubmed/32448271 http://dx.doi.org/10.1186/s12885-020-06741-4 |
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