Fig. 3: Integration of DNA methylation, gene expression and histone modification data.
From: Intraindividual epigenetic heterogeneity underlying phenotypic subtypes of advanced prostate cancer
a Analytical workflow for identifying methylation-driven gene regulation. Figure 3/panel a was created in BioRender. Mizuno, K. (https://BioRender.com/703sb5t). b Distribution of correlations between DNA methylation and gene expression by genomic context (H3K27ac regions, H3K27me3 regions, promoters, gene bodies). Blue text indicates negative correlation between DNA methylation and gene expression, while red text indicates positive correlation. SD, standard deviation. c Example loci showing differential regulation. Left panels: Genomic regions (highlighted in red boxes) showing significant correlation between histone modifications and gene expression. Upper track shows region with positive correlation between H3K27ac signal and ASCL1 expression; lower track shows region with negative correlation between H3K27me3 signal and SEZ6L expression. Right panels: Scatter plots demonstrating these correlations between histone modification signals and gene expression. Upper panel: Scatter plot showing correlation between H3K27ac signal and ASCL1 expression (AC: adenocarcinoma, pale pink, n = 16; NE: neuroendocrine, burgundy, n = 9). Lower panel: Scatter plot showing correlation between H3K27me3 signal and SEZ6L expression (AC, pale pink, n = 17; NE, burgundy, n = 8). Statistical significance was assessed using two-sided Pearson correlation tests. d Representative examples of methylation-expression relationships for key prostate cancer genes across samples (n = 98). Bar plots show sample-level gene expression values, with DNA methylation levels indicated by color. False discovery rate (FDR) values for correlations between gene expression and DNA methylation are shown.
