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. 2023 Dec 20;15:196. doi: 10.1186/s13148-023-01598-3

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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 — A–B Volcano plots showing the effect size (b) of differential gene expression and its statistical significance for the comparisons Cluster 1A versus non-Cluster 1A and metastatic versus non-metastatic tumours from the RNA-seq data. C Correlation plot of differentially methylated promoters and differentially expressed genes in metastatic tumours shows an inverse relationship between the more differentially methylated (higher Δβ) and the level of downregulation. Pearson’s correlation coefficient ρ = −0.3587, p = 3.7 × 10^–13. D Over-representation analysis of upregulated differentially expressed genes between metastatic and non-metastatic tumours for molecular function ontology revealed enriched terms were mainly related to motor proteins, the Wnt-pathway and the extracellular matrix