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. 2022 Jan 5;54(1):12–22. doi: 10.1038/s12276-021-00718-4

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© The Author(s) 2021

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — a Overview of our genome-wide methylation analysis: (i) Study design, (ii) Analysis of differentially methylated regions (DMRs), risk-related single nucleotide polymorphisms (SNPs) and immune infiltration, and (iii) Gene set enrichment analysis of functional DMRs. b–e Stacked bar plots for the count of promoter DMRs (pDMRs) and enhancer DMRs (eDMRs) (b), the count of target genes of pDMRs and eDMRs (c), the count of functional pDMRs (F-pDMRs) and functional eDMRs (F-eDMRs) (d), and the count of target genes of F-pDMRs and F-eDMRs (e). f–g Dimension reduction of methylation profiles for tumor and normal samples using t-stochastic neighbor embedding (tSNE) (f) and uniform manifold approximation and projection (UMAP) (g).