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. 2023 Apr 29;15:73. doi: 10.1186/s13148-023-01482-0

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© The Author(s) 2023

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 — A 12-tumor suppressor gene panel from TCGA hepatocellular carcinoma patients shows gene silencing through promoter DNA methylation within subsets of liver cancer samples. A RNA transcript abundance (logTPM, z-score normalized), methylation of promoter-associated probes (β-value, z-score normalized), and copy number variation for the indicated genes (left to right), together with tumor sample “integrated-data cluster” (iClust) annotations. RNA abundance and DNA methylation are also shown for matched normal liver tissue samples where available. B Scatter plots showing the association between RNA transcript abundance (y-axis; logTPM) and associated probe-level of promoter DNAme (x-axis; β-value). Scatter markers colors correspond to adjacent normal liver (black), iClust1 (blue), iClust1-inf (light blue), iClust2 (orange), iClust2-inf (light orange), iClust3 (green), or iClust3-inf (light green) tumor samples. Further information on inferred iClusters (iClust-inf) is given in “Methods”