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

ABSTRACT:

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 t

SUBMITTER: Wu G 

PROVIDER: S-EPMC7245838 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature