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Fig. 5 | Molecular Medicine

Fig. 5

From: Leveraging a disulfidptosis-related signature to predict the prognosis and immunotherapy effectiveness of cutaneous melanoma based on machine learning

Fig. 5

Validation of the risk model. A, C Survival curves of the high-risk group and low-risk group in the validation set (GSE65904, GSE54467). B, D AUC values of the ROC curves for risk scores in the validation set (GSE65904, GSE54467). Difference in the immunotherapy efficacy between the high-risk and low-risk groups in E Melanoma-PR-JEB23709 and H STAD-PRJEB25780. Comparison of risk scores after immunotherapy in different CM patients with different remission statuses in F Melanoma-PRJEB23709 and I STAD-PRJEB25780. Comparison of risk scores after immunotherapy in different reaction statuses of CM patients in G Melanoma-PRJEB23709 and J STAD-PRJEB25780. K Nomogram depicting risk scores and clinical indicators. The red line represents an example of how prognosis is predicted. L Survival differences between the high and low score groups in the column line plots. M ROC curves demonstrate the predictive efficiency of the nomogram.*P < 0.05; **P < 0.01; ***P < 0.001

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Molecular Medicine

ISSN: 1528-3658