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. 2023 Aug 4;23(3):263. doi: 10.1007/s10142-023-01158-1

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© The Author(s) 2023

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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Fig. 6 — Construction and assessment of ubiquitination-related prognostic signature based on the train cohort. A, B The processing of LASSO algorithm. (A the trajectory changes of the independent variables; B the confidence interval of each lambda). C Sectionalization based on the median risk score. D Distribution characteristics of survival status in low-risk and high-risk populations. E Heatmap showed the molecular expression levels of 9 genes involved in the prognostic panel. F–I The discrepancies in clinical outcomes between low-risk and high-risk patients, including OS, PFI, DSS, and DFI. J Multi-index combined with ROC curve highlighted the superiority and great clinical application value of ubiquitination panel. K ROC curves of our URPS and other four prognostic signatures of PAAD