Evaluation of the prognostic value of <i>PRR11</i> and the prognostic model.

(A) Scatter plots for the survival characteristics of patients with increasing PRR11 expression in GSE17679; the left side of the vertical dashed line represents patients in the low-expression group, and the right side represents patients in the high-expression group. (B) KM plot for high- and low-PRR11 expression groups in GSE17679. (C) Nomogram for the Cox proportional hazards regression model in GSE17679. (D) Calibration analysis of the model in GSE17679; the x-axis represents predicted overall survival rate by the model, and the y-axis represents observed overall survival rate; the diagonal (dashed line) refers to the ideal line. (E) Time-dependent ROC analysis of the model in GSE17679; the x-axis represents the 1-specificity of the model, and the y-axis represents the sensitivity of the model. (F) 1-year DCA in GSE17679; the x-axis represents the threshold probability for treatment or intervention, and the y-axis represents net benefit. DCA, decision curve analysis; KM, Kaplan–Meier; PRR11, proline rich 11; ROC, receiver operating characteristic.</p

Quality control of the datasets.

(A) Gene expression profile of the ES samples in GSE17679 by boxplot. (B) Gene expression profile of the ES samples in GSE17679 by density plot. (C) Gene expression profile of the samples in GSE68776 by the boxplot. (D) Gene expression profile of the samples in GSE68776 by density plot. (E) Gene expression profile of the samples in GSE63155 by boxplot. (F) Gene expression profile of the samples in GSE63155 by density plot. (G) Gene expression profile of the samples in GSE63156 by boxplot. (H) Gene expression profile of the samples in GSE63156 by density plot. ES, Ewing sarcoma.</p

Cell infiltration profile in samples and tumor-infiltrating immune cells with prognostic value.

(A) Cell infiltration profile of patients in GSE17679 by heatmap. (B) Cell infiltration profile of patients in GSE63155 by heatmap. (C) Cell infiltration profile of patients in GSE63156 by heatmap. (D) The importance score of the tumor-infiltrating immune cells varied with the running times of the Boruta feature selection; the x-axis represents the running times of the Boruta feature selection, and the y-axis represents the importance score of the variables. (E) Importance score of the tumor-infiltrating immune cells screened by the Boruta feature selection; the x-axis represents the tumor-infiltrating immune cells screened by the Boruta feature selection; the y-axis represents the importance score calculated using the Boruta feature selection. Th2 cells, T helper 2 cells; NKT, natural killer T cells; CD4 Tem, CD4+ T effector memory; CD8 Tcm, CD8+ T central memory cells.</p

Identification of hub genes from common DEGs between healthy subjects and patients with ES.

(A) Volcano plot of the DEGs (absolute log2FoldChange >1 and adjusted p-value B) Volcano plot of the DEGs (absolute log2FoldChange >1 and adjusted p-value C) Venn plot for the DEGs (absolute log2FoldChange >2 and adjusted p-value D) LASSO coefficient profiles for the genes screened by univariate and multivariate Cox regression analyses in 10-fold cross-validations. (E) Partial likelihood deviance with alteration of the log(λ) plotted by LASSO regression in 10-fold cross-validations. (F) The importance score of the genes varied with the running times of the Boruta feature selection; the x-axis represents running times of the Boruta feature selection, and the y-axis represents the importance score. (G) Importance score of the genes screened by LASSO regression; the x-axis represents the genes screened by LASSO regression, and the y-axis represents the importance score calculated by the Boruta feature selection. (H) PPI network analysis for the common DEGs: red and yellow nodes indicate the top 50 genes by Cytohubba (color intensity indicates greater maximum clique centrality [MCC] value). DEGs, differentially expressed genes; ES, Ewing sarcoma; LASSO, least absolute shrinkage and selection operator; PPI, protein–protein interaction.</p

Univariate and multivariate Cox analyses of tumor-infiltrating immune cells.

Univariate and multivariate Cox analyses of tumor-infiltrating immune cells.</p

The flow chart of this study.

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.</div

Differential analysis between the high- and low-risk score groups and functional analyses of DEGs.

(A) Dot plot for GO clustering analysis of DEGs in GSE17679. (B) Dot plot for KEGG clustering analysis of DEGs in GSE17679. (C) GSEA analysis for DEGs in GSE17679. (D) Chord plot for the top seven clustered GO terms. (E) Chord plot for the top seven clustered KEGG pathways. (F) Lollipop plot for the common DEGs correlated with PRR11 (absolute correlation value ≥0.7 and p-value G) PPI network analysis for proteins encoded by the top DEGs correlated with PRR11 (p-value PRR11, proline rich 11.</p

Expression of PRR11 in ES and normal bone tissues.

Immunohistochemical staining of PRR11 in ES tissue. Magnification: ×100 (A), ×200 (B) and ×400 (C): PRR11 was stained brown in the cytoplasm of ES cells. Immunohistochemical staining of PRR11 in the normal bone tissue. Magnification: ×100 (D), ×200 (E) and ×400 (F): there was no significant brown staining of areas in the normal bone tissue. (G) The barplot illustrates the positive rate of PRR11 in immunohistochemical staining, analyzed using the Mann-Whitney test (non-normal distribution), with the presentation of Mean ± Standard Error of the Mean. Significance level: no significance (ns), p ≥ 0.05; *, p < 0.05; **, p < 0.01; ***, p < 0.001, ****, p < 0.0001. ES, Ewing sarcoma; PRR11, proline rich 11.</p

Validation of the model in the GSE63156 dataset.

(A) Scatter plots for the survival characteristics of patients with increasing PRR11 expression in GSE63156; the left side of the vertical dashed line represents patients in the low-expression group, and the right side represents patients in the high-expression group. (B) KM plot for the high- and low-PRR11 expression groups in GSE63156. (C) Nomogram for the Cox proportional hazards regression model in GSE63156. (D) Calibration analysis of the model in GSE63156; the x-axis represents predicted overall survival rate by the model, and the y-axis represents observed overall survival rate; the diagonal (dashed line) refers to the ideal line. (E) Time-dependent ROC analysis of the model in GSE63155; the x-axis represents the 1-specificity of the model, and the y-axis represents the sensitivity of the model. (F) 1-year DCA in GSE63156; the x-axis represents the threshold probability for treatment or intervention, and the y-axis represents net benefit. DCA, decision curve analysis; KM, Kaplan–Meier; PRR11, proline rich 11; ROC, receiver operating characteristic.</p

Clinicopathological characteristics in the training and validation sets.

Clinicopathological characteristics in the training and validation sets.</p