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. 2022 Jul 21;20:324. doi: 10.1186/s12967-022-03522-4

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© The Author(s) 2022

Open AccessThis 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. 1 — Flow chart of data analysis. The WES data from each patient is subjected to genome mapping and variant calling (Variant calling). Then the data was divided into 2 groups. UCKT; N = 7 and UCHD; N = 5. The mutations were then pooled together in each group and known dbSNPs were excluded (Novel variations). The COSMIC-annotated cancer driver genes were selected (Retain cancer driver genes), but next, bladder cancer-related genes were removed from the list (Exclude bladder cancer genes). Genes that were unique in each group were identified (Overlap). Final list of genes contains those occurred in two or more UCKT patients (Multiple occurrences in UCKT group). Numbers represent gene counts, except those in parentheses, which are nucleotide mutations