Abstract
Ewing's sarcoma (ES) is the second most common bone and soft tissue malignancy in children and adolescents with a poor prognosis. The identification of genes with prognostic value may contribute to the prediction and treatment of this disease. The GSE17679, GSE68776, GSE63155, and GSE63156 datasets were downloaded from the Gene Expression Omnibus database and qualified. Prognostic value of differentially expressed genes (DEGs) between the normal and tumor groups and immune cell infiltration were explored by several algorithms. A prognostic model was established and validated. Finally, functional analyses of the DEGs were performed. Proline rich 11 (PRR11) and mast cell infiltration were noted as the key indicators for the prognosis of ES. Kaplan-Meier and scatter plots for the training and two validation sets showed that patients in the low-PRR11 expression group were associated with better outcomes than those in the high-PRR11 expression group. The concordance indices and calibration analyses of the prognostic model indicated good predictive accuracy in the training and validation sets. The area under the curve values obtained through the receiver operating characteristic analysis for 1-, 3-, 5-year prediction were ≥ 0.75 in the three cohorts, suggesting satisfactory sensitivity and specificity of the model. Decision curve analyses suggested that patients could benefit more from the model than the other strategies. Functional analyses suggested that DEGs were mainly clustered in the cell cycle pathway. PRR11 and mast cell infiltration are potential prognostic indicators in ES. PRR11 possibly affects the prognosis of patients with ES through the cell cycle pathway.