A novel risk score model based on eight genes and a nomogram for predicting overall survival of patients with osteosarcoma release_yddyrb4x3jey3c7w552ylzldz4

by Guangzhi Wu, Minglei Zhang

Abstract

This study aims to identify a predictive model to predict survival outcomes of osteosarcoma (OS) patients. 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. 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. An eight-gene predictive model and nomogram were developed to predict OS prognosis.
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Published in BMC Cancer
ISSN-L 1471-2407
Volume 20
Issue 1
Page(s) 456
Release Date 2020-05-24
Primary Language en (lookup)

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Date   2020-05-24
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