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. 2020 Oct 22;10:18028. doi: 10.1038/s41598-020-74482-2

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© The Author(s) 2020

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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Logistic regression classifier performance. For each receiver operative characteristic curve, black represents a model containing both modules while blue and turquoise correspond to their respective individual modules (SUZ12-enriched and E2F4/FOXM1-enriched, respectively). The top row (including all WHO grades) includes a red curve which represents the predictive accuracy of WHO grade alone. Note that with the exception of the black curves, each model contains a single predictive variable (metagene or WHO grade). Models in rows 2–4 include only individual WHO grades as labeled. Columns (left to right) represent performance on the discovery cohort (tenfold cross validation), merged microarray validation cohort (Validation 1), and RNA-seq validation cohort (Validation 2). For the models including all WHO grades, the red curves are statistically less predictive (DeLong p < 0.05) than all other curves in the Discovery and Validation 2 cohorts, while all curves in Validation 1 are similar to one another (DeLong’s p < 0.05).