Comparative and integrative analysis of transcriptomic and epigenomic-wide DNA methylation changes in African American prostate cancer

Figures & data

Table 1. Clinicopathological data of the 113 AA prostate tissues. Number and percentage (in parentheses) of all cancer cases are shown. The age range and mean age, median, standard error (Std. Err), standard deviation (Std. dev) as well as the 95% confidence interval for mean are shown. Independent samples t-test shows that means of age are not significantly different for Grade groups 1, 2 and 3. Independent samples median test reveals that the medians of age are not significantly different for Gleason grade 4 and 5 (Fisher Exact test, case sizes are too small for Group 4 and 5 to make comparison with Group 1–3).

	Number (Percent)	Age Range (years)
Gleason score
5 and 6	16 (25)	45–70 (57.62 + 6.04)
7–10	47 (75)	45–73 (57/78 + 7.08)

Figure 1. Genomic distribution of 5623 top differentially methylated CpG probes in prostate cancer compared to normal prostate tissues is expressed as percentage landscape distribution in a pie chart. a. Analysis in HUBCM dataset. b. Analysis in TCGA dataset. The methylation levels of CpG probes are shown by functional locations including TSS1500, TSS200, 5ʹUTR, 1st Exon, Body, and 3ʹUTR and intergenic (INT) regions.

Table 2. GO term search results showing the functional categories for the genes associated with differential methylation and expression. A. Genes with high methylation and low expression and B. Genes with low methylation and high expression. For each result set, there is GO functional category, name of enrichment term, number of genes counted in term, then the number of counts in total population and the biological genes involved as well as the degree of statistical enrichment.

Functional Category	Name of Enrichment Term	Count of selected Genes	Count in Total Population	Involved gene list	p-value
Table 2A
GO	hemidesmosone assembly	6	11	COL17A1, ITGB4, KRT5, KRT4, LAMA3, LAMB3	5.29E-06
GO	mammary gland development	8	26	B4GALT1, NRG1, IRS1, NOTCH4, TGFB3, CAV1, WNT3A, NTN1	2.4E-05
GO	epidermis development	14	81	COL7A1, COL17A1, ALDH3A2, GJB5, KRT5, KRT13, KRT14, KRT15, KRT17, LAMA3, LAMB3, ATP2A2, NTF3, GRHL3	3.8E-05
GO	hormone biosynthetic process	5	10	HFE, DUOX2, DUOX1, TG, CHST9	5.91E-05
GO	cell communication	14	89	NOXO1, GJB4, SNX31, GJB3, GJB5, NRG1, ITGB4, JAG2, SLC8A2, SNX1, KREMEN1, PPFIBP2, SNX21, TRIP10	0.00011
GO	response to hypoxia	18	140	CHRNA7, EPAS1, PTK2B, ALDOC, HSD11B2, JAG2, SMAD3, ANGPLT4, PSEN2, PYGM, BCL2, RYR1, SCNN1B, TGFB3, VEGFA, ACTN4, CAV1, PDLIM1	0.000183
GO	positive regulation of stress fibre assembly	5	13	SORBS3, AMOT, SMAD3, NF2, SDC4	0.000267
GO	basal plasma membrane	6	21	MUC20, ITGB4, MET, DST, TF, CAV1	0.000406
GO	Wnt receptor signalling pathway, calcium modulating pathway	6	21	ROR2, WNT4, WNT6, WNT10A, WNT5B, WNT3A	0.000406
GO	endothelial cell morphogenesis	3	4	HOXA13, ID1, NOTCH4	0.000461
GO	pyruvate dehydrogenase (acetyl-transferring) kinase activity	3	4	PDK2, PDK3, PDK4	0.000461
GO	cytoskeletal protein binding	8	39	ALDOA, ALDOC, B4GALT1, ANXA2, NF2, SDC4, FRMD5, DLG5	0.000534
GO	apical plasma membrane	18	153	PDPN, SLC46A1, PROM2, CTSB, SLC5A8, MUC20, NRG1, ABCC6, SLC22A18, P2RY2, DUOX2, ATP6VOA4, DUOX1, VANGL2, SCNN1A, SCNN1B, TF, CAV1	0.000551
GO	aldehyde dehydrogenase (NAD) activity	4	9	ALDH2, ALDH3A1, ALDH3A2, ALDH4A1	0.000607
GO	focal adhesion	12	84	ARPC2, SORBS3, PTK2B, FHL2, TES, SDC4, TNS1, TRIP6, TNS4, JUB, CAV1, BCAR1	0.000823
GO	structural constituent of cytoskeleton	11	73	ARPC2, SORBS3, ADD3, DES, SYNM, KRT5, KRT14, KRT15, KRT17, KRT19, VILL	0.000853
GO	lung alveolus development	6	24	PDPN, PSEN2, SFTPD, SOX2, TGFB3, VEGFA	0.000887
GO	positive regulation of cell migration	10	63	PDPN, AKT2, PTK2B, IRS1, SMAD3, ROR2, TRIP6, VEGFA, WNT5B, BCAR1 PDPN	0.000961
Table 2B
GO	negative regulation of macrophage differentiation	2	4	INHBA, C1QC	0.000314
GO	cAMP-dependent protein kinase inhibitor activity	2	5	PRKAG2, PKIB	0.000521
GO	protein destabilization	2	8	MYLIP, PRKDC	0.001439
GO	clathrin coated vesicle membrane	2	11	DAB2, DENND1A	0.002787
GO	transcription corepressor activity	5	140	TFEC, HOXC6, APEX1, YY1, NRIP1	0.003649
GO	fatty acid biosynthetic process	3	46	PTGES3, TMEM195, PRKAG2	0.004591
GO	phosphatidylinositol-3,4,5-trisphosphate binding	2	16	ADAP2, BTK	0.005937
GO	positive regulation of neutrophil differentiation	1	1	IK2F1	0.007302
GO	tRNA export from nucleus	1	1	XPOT	0.007302
GO	2-oxoglutarate-dependent dioxygenase activity	1	1	ALKBH2	0.007302
GO	oxidative DNA demethylation	1	1	ALKBH2	0.007302
GO	collagen type VIII	1	1	COL8A1	0.007302
GO	dUTP diphosphatase activity	1	1	DUT	0.007302
GO	dUTP metabolic process	1	1	DUT	0.007302
GO	alpha-methylacyl-CoA racemase activity	1	1	AMACR	0.007302
GO	phosphoribosylamine-glycine ligase activity	1	1	GART	0.007302
GO	phosphoribosylformylglycinamidine cyclo-ligase activity	1	1	GART	0.007302
GO	phosphoribosylglycinamide formyltransferase activity	1	1	GART	0.007302

Figure 2. Heat map generated from hierarchical clustering analysis of the top 1263 CGI probes most variable CpG islands across all samples in the genome-wide methylation analysis and correlated with RNA expression profiles. A. The AA dataset consists of 63 PCa and 50 normal adjacent (adj.) prostate tissue DNA used in the methylation analysis and 96 RNA samples (matched PCa and normal adj. tissues) used in the transcription analysis. B. The TCGA methylation dataset consists of 504 prostate tissues used in the genome-wide DNA methylation analysis. This is made up of 58 AA PCa and 3 AA normal prostate tissues, and 412 EUR PCa and 31 EUR normal prostate tissues. The TCGA RNA expression consists of 521 prostate tissue RNA samples. This is made up of from 58 AA PCa and 7 AA normal prostate tissues, and 412 EUR PCa and 44 EUR normal tissues.

Figure 3. Hierarchical clustering analysis of 1655 CGI probes across all AA prostate tissue samples categorized by Gleason score 7–10 (grade 2–5) tumours, Gleason score 5–6 (grade 1) tumours and normal (benign) prostate tissues. a. Clustering analysis of the top 1655 CGI differential methylation that are significant (p < 0.05) by t-test and methylation change greater than 5%) and separated into Gleason 7–10; Gleason 5–6; and benign prostate tissues. b. Comparison of the significant differential DNA methylation and mRNA expression as categorized by Gleason score and normal tissues.

Figure 4. Predicted gene regulatory network of the overlapping genes associated with increasing Gleason score in AA prostate cancer. Ingenuity Pathway Analysis software was used to predict putative sub-networks containing five candidate genes: IER3, TFAP2C, ESR2, MIXL1, AMIGO3 (RNF123) that play a role in several mechanisms associated steroid signalling and tumour microenvironment in prostate cancer disease progression. MIXLI, TFAP2C, IER3 and ESR2 are highlighted in red.

Table 3. Statistical analysis of differential methylated probes and associated gene symbol for PCa in the Howard/BCM (HUBCM) datasets and the TCGA (EUR and AA) datasets. Highlight in pink show direct correlation of differential methylation in high versus low Gleason grade PCa. Highlights in green shows inverse correlation of differential methylation in high versus low grade PCa. P-values for all probes are shown.

Data availability statement

Any data that support the finding of this study are available from the corresponding author, BKA upon reasonable non-commercial request.