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. 2023 Jun 2;25:15. doi: 10.1186/s12575-023-00207-0

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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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 4 — Unsupervised clustering of deep learning features in TCGA-UVM cohort. A Comprehensive heatmap with unsupervised clustering showed distinctive characteristics of deep learning features and clinical characteristics. B Kaplan–Meier curve of survival probability between Cluster1 and Cluster2 subtype. C Boxplot of the 14 deep learning features between alive and dead status. D Heatmap of 50 cancer Hallmark pathways between Cluster1 and Cluster2 subtypes. E Comprehensive OncoPrint plots of mutated characteristics in Cluster1 and Cluster2 subtypes. F Boxplot of TMB between Cluster1 and Cluster2 subtypes. G Boxplot of MSI between Cluster1 and Cluster2 subtypes. H The distributions of mutational signatures in Cluster1 and Cluster2 subtypes. I Forest plots of the top mutated genes between Cluster1 and Cluster2 subtypes