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. 2021 Dec 8;12(12):1140. doi: 10.1038/s41419-021-04427-8

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 4 — A The risk score distribution (top) and survival status distribution (bottom) for glioma patients. B Kaplan–Meier survival curves for patients with glioma, classified into four groups based on grade and risk scores. C Kaplan–Meier survival curves for patients with glioma, classified by molecular pathological characteristic and risk scores. D ROC curves verified the prognostic performance of the risk score. E Univariate and multivariate Cox regression analyses of the association between clinic pathological factors and OS of patients. F Construction of a nomogram for survival prediction. G The calibration curve for the nomogram model. Three colored lines (blue, red and black) represent the performance of the nomogram. A closer fit to the diagonal gray line indicates a better estimation. ****p < 0.0001; ***p < 0.001; **p < 0.01.