A Risk Assessment Model for the Prognosis of Osteosarcoma Utilizing Differentially Expressed LncRNAs

The present study was conducted to establish a risk assessment model for evaluating osteosarcoma prognosis based on prognosis-associated long non-coding RNA (lncRNA) expression. Human osteosarcoma expression profiles were obtained from the NCBI GEO and EBI ArrayExpress databases and differently expressed lncRNAs between good and poor prognosis groups were evaluated using Student's t-test and Wilcoxon rank test in R (v. 3.1.0). A multivariate Cox regression was used to establish a risk assessment system based on lncRNA expression levels, with the associated regression coefficients used as the weight. Survival analysis and receiver operating characteristic (ROC) curves were constructed to verify the accuracy of the risk assessment model. Associations between the prognosis, risk assessment model and clinical features were also investigated using univariate and multivariate Cox regression analyses. Furthermore, differentially expressed genes associated with the lncRNAs in the risk assessment model were identified, and functional enrichment analysis was performed. A total of 9 from the 211 differentially expressed lncRNAs were selected to establish the risk assessment model. The risk assessment model exhibited a good prognostic prediction ability, with high area under the curve values in the training and validation sets. Additionally, the calculated risk score based on the 9 selected lncRNAs was identified to be an independent prognostic factor for osteosarcoma. Furthermore, differentially expressed genes were primarily enriched in the cell cycle, oxidative phosphorylation and cell adhesion processes. The present study described a risk assessment model based on 9 significantly differentially expressed lncRNAs, which was identified to have a high accuracy in potentially predicting patient prognosis.