Aberrant DNA methylation of uveal melanoma drivers as an excellent prognostic tool

doi:10.21203/rs.3.rs-2502537/v1

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Aberrant DNA methylation of uveal melanoma drivers as an excellent prognostic tool

https://doi.org/10.21203/rs.3.rs-2502537/v1

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Background: Despite outstanding advances in understanding the genetic background of uveal melanoma (UM) development and prognosis, the role of DNA methylation reprogramming remains elusive. This study aims to clarify the extent of DNA methylation deregulation in the context of gene expression changes and its utility as a reliable prognostic biomarker.

Methods: Transcriptomic and DNA methylation landscapes in 25 high- and low-risk UMs were interrogated by Agilent SurePrint G3 Human Gene Expression 8×60K v2 Microarray and Human Infinium Methylation EPIC Bead Chip array, respectively. DNA methylation and gene expression of the nine top discriminatory genes, selected by the integrative analysis, were validated by pyrosequencing and qPCR in 58 tissues.

Results: Among 2,262 differentially expressed genes discovered in UM samples differing in metastatic risk, 60 were epigenetic regulators, mostly histone modifiers and chromatin remodelers. 44,398 CpGs were differentially methylated, 27,810 hypomethylated, and 16,588 hypermethylated in high-risk tumors, with Δβ values ranging between -0.78 and 0.79. By integrative analysis, 944 differentially expressed DNA methylation-regulated genes were revealed, 635 hypomethylated/upregulated, and 309 hypermethylated/downregulated. Aberrant DNA methylation in high-risk tumors was associated with the deregulation of key oncogenic pathways such as EGFR tyrosine kinase inhibitor resistance, focal adhesion, proteoglycans in cancer, PI3K-Akt signaling, or ECM-receptor interaction. Notably, DNA methylation values of nine genes, HTR2B, AHNAK2, CALHM2, SLC25A38, EDNRB, TLR1, RNF43, IL12RB2, and MEGF10, validated by pyrosequencing, demonstrated excellent risk group prediction accuracies (AUC ranging between 0.870 and 0.956). Moreover, CALHM2 hypomethylation and MEGF10, TLR1 hypermethylation, as well as two three-gene methylation signatures, Signature 1 combining AHNAK2, CALHM2, and IL12RB and Signature 2 AHNAK2, CALHM2, and SLC25A38genes, correlated with shorter overall survival (HR = 4.38, 95% CI 1.30-16.41, HR = 5.59, 95% CI 1.30-16.41; HR = 3.43, 95% CI 1.30-16.41, HR = 4.61, 95% CI 1.30-16.41 and HR = 4.95, 95% CI 1.39-17.58, respectively).

Conclusions: Our results demonstrate a significant role of DNA methylation aberrancy in UM progression. The advantages of DNA as a biological material and excellent prediction accuracies of methylation markers open the perspective for their more extensive clinical use.

uveal melanoma

DNA methylation

gene expression

integrative analysis

pyrosequencing

metastasis

Uveal melanoma (UM) is rare ocular cancer metastasizing in 50% of cases with no curative treatment for advanced disease. Consequently, despite the clinical availability of reliable prognostic tools, the outcome for high-risk patients remains dismal. Therefore, understanding the metastatic process and its drivers is of utmost importance.

UM metastases predominantly occur in the liver but can also affect other organs such as the lungs, lymph nodes, skin, bone, and brain. Metastatic risk estimation is based on the American Joint Committee on Cancer (AJCC) staging system, where tumors are categorized into four stages according to basal diameter and thickness, ciliary body involvement, and extraocular overgrowth (1). While AJCC stage I tumors have a 10-year survival probability of 90% and stage II 75%, in stage III, it decreases to less than 60%. However, this system does not consider tumor histology, represented by epithelioid cell type, a high number of mitoses, immune cell infiltration, and extravascular matrix pattern (2). The combination of AJCC with cytogenetic analysis significantly improves prognostication. Chromosomal aberrations include several UM-specific rearrangements, the most significant monosomy 3 (M3), and additional copy number changes (gains and losses) on chromosomes 1, 6, and 8. Recurrent mutations in EIF1AX, SF3B1, and BAP1, occurring later in tumorigenesis, represent a second hit with a significant impact on prognosis (3, 4).

Besides aberrant histological profiles and genetic abnormalities, several other factors may influence metastatic risk. The role of DNA methylation, one of the critical regulators of gene transcription (5), has been intensely studied in cancer initiation and progression. While global hypomethylation has been associated with chromosomal instability, hypermethylation of promoter regions was related to silencing tumor suppressor genes (TSGs) (6). In parallel, frequently mutated and aberrantly expressed regulatory proteins and enzymes controlling the maintenance and removal of epigenetic marks, referred to as epigenetic regulators, have been identified in cancer (7, 8).

Since its first evidence in 2018, DNA methylation reprogramming has been confirmed as one of the main attributes of poor prognosis UM, defined by the presence of M3, amplification of the long arm of chromosome 8, BAP1, and SF3B1 mutations, reflected in the Class 2 gene expression profile (9–12). However, understanding the epigenetic landscape aberrancy in UM pathogenesis is still in its infancy. DNA methylation repatterning was initially attributed to BAP1 loss (10). BAP1 is a deubiquitinating hydrolase with various functions, including cell cycle control, DNA damage repair, chromatin remodeling, and gene expression regulation. The discovery of a hypermethylated region within the BAP1 locus suggested epigenetic regulation of BAP1 itself (10). Recently, BAP1 was shown to negatively control the polycomb machinery responsible for maintaining cellular integrity (13). BAP1 loss was also correlated with the upregulation of genes involved in the immunosuppressive UM tumor microenvironment (TME) (14). Infiltration of tumor-associated macrophages (TAMs) and tumor-infiltrating lymphocytes (TILs) were demonstrated to play an adverse role in UM prognosis (15). Immunohistochemical (IHC) analysis of liver metastases revealed TAMs and TILs entrapment in peritumoral fibrotic areas, suggesting immune exclusion (14). The significance of DNA methylation and other epigenetic mechanisms in regulating the activity of immune cells within TME and thus determining tumor growth or suppression has been demonstrated in various cancer types (16).

To decipher the role of DNA methylation in UM progression, we performed a whole-genome analysis of 25 tumor samples using Illumina's EPIC platform, allowing us to analyze 850 000 CpG dinucleotides differing in methylation level between low- and high-risk patients. The results were integrated with the gene expression data obtained from Agilent SurePrint G3 Human Gene Expression 8x60K v3 Microarray to assess the functional consequences of aberrant DNA methylation. The top findings were validated by pyrosequencing and quantitative RT-PCR (qPCR) in an expanded set of 58 UM tissues.

Patient samples

This study involved 58 UM patients treated by enucleation (7 of them were enucleated after stereotactic radiosurgery in the past) between August 2018 and January 2022 at the Department of Ophthalmology, Faculty of Medicine, Comenius University in Bratislava. The median age was 66 years (ranging from 32 to 87), the right and left eyes were affected approximately equally (51.7%, n = 30 vs. 48.3%, n = 28), and 58.6% (n = 34) of patients were males. Choroidal melanoma (C69.3) was diagnosed in 82.8% (n = 48), while ciliary body melanoma (C69.4) in 17.2% (n = 10) of cases. All UM tissues underwent routine histological examination at the Department of Pathology, Faculty of Medicine, Comenius University, Bratislava. Ten patients were diagnosed with stage IV, and one developed metastasis after primary UM treatment. Metastases were located in the liver (n = 4), lungs (n = 3), spine (n = 1), skin (n = 2), and pelvis (n = 1).

Genomic profiling

Tumor samples (approx. 0.5 cm²) snap-frozen in liquid nitrogen after surgery and stored at -80°C were mechanically homogenized, and DNA was extracted by QIAamp DNA Mini Kit (Qiagen, Hilden, Germany). Isolated DNA was used for genomic and methylome profiling. DNA quantity and quality were evaluated by Nanodrop 2000 spectrophotometer (NanoDrop Technologies, USA).

Chromosomal rearrangements

Chromosomal aberrations were assessed in 100 ng of tumor DNA by multiplex ligation-dependent probe amplification (MLPA), using SALSA MLPA Probemix P027 Uveal melanoma kit (MRC Holland, the Netherlands) (17). MLPA products were separated by capillary electrophoresis on Genetic Analyzer 3130XL (Applied Biosystems, MA, USA) and analyzed by Coffalyser software (MRC Holland, the Netherlands).

Hotspot mutations

Nine hotspot mutations in four genes GNAQ (p.Q209L, p.Q209R, p.Q209P, p.R183Q), GNA11 (p.Q209L, p.Q209P, p.R183C), PLCB4 (p.D630Y) and CYSLTR2 (p.L129Q) were determined in tumor tissues by digital droplet PCR (ddPCR) and validated by Sanger sequencing as described previously (17).

BAP1 mutations

All 17 coding exons of the BAP1 gene (ENSG00000163930) were amplified using HOT FIREPol® DNA Polymerase (Solis BioDyne, Estonia) with 10 µM specific primers (Additional file 1: Table S1) and following PCR steps: denaturation at 95°C for 15 min, 30 cycles at 95°C for 40 s, 60°C for 30 s, and 72°C for 60 s, ended by 20 min at 72°C. PCR products were purified with illustra™ ExoProStar 1-Step enzyme (Sigma Aldrich, USA), amplified using the BigDye™ Terminator v3.1 Cycle Sequencing Kit, and purified according to the manufacturer's protocol. Sequencing products were analyzed on Genetic Analyzer 3130XL (Applied Biosystems, MA, USA). ChromasPro Software v1.6 (Technelysium Pty Ltd, Australia) was applied to identify variants by alignment to the reference sequence (ENST00000460680.6).

Transcriptome profiling and Validation

Total RNA was isolated from snap-frozen tumor tissues by RNeasy Mini Kit (Qiagen, Hilden, Germany). The RNA quantity and quality were evaluated on the Agilent 2100 Bioanalyzer instrument using Agilent RNA 6000 Nano Kit (Agilent Technologies, USA). Only samples with RIN above 8.5 were selected for transcriptome analysis, while samples with RIN above 7.5 were rated suitable for qPCR.

Agilent SurePrint G3 Human Gene Expression 8x60K v3 Microarray was used to obtain transcriptomic data from 24 tumor samples (one sample was excluded due to insufficient RNA quality). After the integrity check, 100ng of total RNA was labeled with Low Input Quick Amp Labeling Kit (Agilent, Santa Clara, CA, USA). Labeled targets were re-purified using GeneJET™ RNA Purification Kit (ThermoScientific, USA). Samples with specific activity above eight were fragmented and prepared for hybridization using Gene Expression Hybridization Kit (Agilent Technologies, USA), applied onto the array, and hybridized for 17 hours at 65°C. Microarray slides were washed in two steps using Gene Expression Wash Buffer Kit (Agilent Technologies, USA) and scanned by SureScan Microarray Scanner (Agilent Technologies, USA) at a resolution of 2 µm.

Automatic Image processing was performed with Feature Extraction Software 12.0.3.2 (Agilent Technologies, USA). The resulting files with spot intensities were imported into GeneSpring 14.9 GX software (Agilent Technologies, USA). Differential gene expression between high- vs. low-risk samples was calculated using moderate T-test and Benjamini-Hochberg correction with absolute fold change value (abs(FC)) ≥ 2 and FDR adjusted p-value ˂ 0.05 set as cut-offs. The g:profiler toolkit was used for Gene Set Enrichment Analysis (18).

Quantitative RT-PCR

The top nine selected genes were validated by qPCR using primers listed in Additional file 1: Table S2. Total RNA was reverse transcribed with RevertAid First Strand cDNA Synthesis Kit (Thermo Fisher Scientific, USA). The 20 µl qPCR reactions performed in triplicates contained 0.3 µM primers, 50 ng of RNA, and GoTaq® qPCR Master Mix (Promega, USA). qPCR was carried out on Stratagene Mx3005P Real-time PCR System (Agilent Technologies, USA) at the following thermal cycling conditions: 95°C for 10 min, followed by 36 cycles of 95°C for 20 s, 59°C for 20 s, and 72°C for 20 s. Relative gene expression was quantified using the 2^–ΔΔCT method. The results were reported as FC in the high-risk group normalized to endogenous control HPRT1 and relative to the low-risk group.

Methylome profiling and Validation

DNA isolated from 25 UM tissues was bisulfite-treated using EZ DNA Methylation™ Kit (Zymo Research, USA) according to Illumina's recommended deamination protocol. A genome-wide methylation screening using Illumina Infinium Methylation EPIC array BeadChip (850K) (Illumina, San Diego, CA, USA) was carried out by the Epigenomic Services from Diagenode. The raw data were checked for bisulfite conversion efficiency, staining, extension, hybridization, target removal, and dye specificity. Arrays that passed quality control were processed via a pipeline included in the R ChAMP package (19) with default settings excepting XY filtering, as males and females were evenly distributed. The batch effect was corrected through the ComBat algorithm (20). For each CpG site, individual methylation beta value (β) was acquired, ranging from 0 for unmethylated to 1 for fully methylated. Differentially methylated CpGs were obtained based on FDR adjusted p-value < 0.05 with no threshold set for β values.

Integration of whole-genome methylation and gene expression data

The integrative analysis of transcriptomic and epigenomic data was done to identify the methylation-driven transcriptomic changes. Pearson or Spearman correlation coefficients were calculated based on data distribution, and genes with a significant negative correlation between gene expression and individual β values were considered DNA methylation-regulated. Epigenetic regulators, their complexes, targets, and products were identified based on the EpiFactors database (21), the TSGs, and oncogenes by TSGene 2.0 and ONGene databases (22, 23).

Pyrosequencing

DNA methylation of prioritized loci, located within regulatory regions of nine selected genes, was validated by pyrosequencing. Primer design was guided by the whole-genome data, focusing on the largest Δβ values (difference between high- and low-risk tumors). Primers for PCR amplification and pyrosequencing were designed using PyroMark Assay Design Software 2.0 (Qiagen, Hilden, Germany), and their sequences are listed in Additional file 1: Table S3. The PyroMark PCR Kit® (Qiagen, Hilden, Germany) was used for PCR amplification of bisulfite-converted DNA (EpiTect Bisulfite Kit® (Qiagen, Hilden, Germany)). The concentrations of PCR primers were 0.32 µM. The annealing temperature was 57°C for all genes except for CALHM2 and AHNAK2, where an annealing temperature was 60°C. Pyrosequencing was carried out on the PyroMark Q96 ID device (Qiagen, Hilden, Germany) with the PyroGold Reagent Kit ® (Qiagen, Hilden, Germany). The pyrosequencing results were analyzed by PyroMark Q96 Software version 2.5.8 (Qiagen, Hilden, Germany).

Statistical analysis

IBM SPSS Statistics for Windows, Version 23.0 (Armonk, NY: IBM Corp.) was used for statistical analysis. The normality of data distribution was assessed by the Sapiro-Wilk test, based on which the Mann-Whitney U test or Student's t-test was applied to analyze differences between studied groups. All tests were two-tailed, performed at significance level α = 0.05. The categorical variables were tested using χ2 or Fisher's exact test. The Pearson or Spearman correlation coefficients were calculated based on data distribution. DNA methylation values were evaluated for their performance in predicting the risk groups (defined by chromosome 3 or BAP1 mutation status) by calculation of the area under the Receiver operating characteristic (ROC) curve (AUC). Based on this analysis, the optimal DNA methylation cut-off values for individual genes were estimated from Youden Index (24). The Kaplan–Meier survival analysis with the log-rank test was used to assess the differences in overall survival (OS) based on these cut-offs. The variables with univariate Cox proportional hazard model p-values < 0.1 were considered in a multivariate model with backward selection. Formula by Gass was applied to calculate tumor volume "TV = π/6 × (largest basal diameter × width × prominence)" (25).

Baseline characteristics

The study design is presented in Fig. 1A. The materials and methods section provides clinicopathological characteristics of enrolled patients. Their individual clinical and molecular features are extended in Fig. 1B, C. Based on MLPA results, 32 tissues were classified as M3, 22 as disomy 3 (D3), and four samples, three of them treated by stereotactic radiotherapy in the past, did not carry UM-specific rearrangements. The classification of two samples, UM37, with only partial loss of chromosome 3 (3q-6p + 8q+), and UM56, with loss of the short arm of chromosome 1 (1p-8q+) without chromosome 3 aberrations, was refined by gene expression profiling (GEP). Based on the expression of 12 discriminatory genes (Fig. 2A), UM37 was classified as low-risk and UM56 as high-risk UM. In addition, UM59, the only D3 sample carrying BAP1 mutation, was considered high-risk. This classification yielded 25 low-risk and 33 high-risk tumors, of which 21 (63.6%) were BAP1 positive.

Figure 1. Baseline characteristics. A) Overview of the methodological approaches. Twenty-five samples were subjected to whole-genome analyses. The most significant findings were validated in an extended cohort of 58 UM samples by qPCR and pyrosequencing. B) Clinico-pathological characteristics of included patients. On the left side are samples analyzed by whole-genome approaches. C) Mutational and chromosomal aberration status. Four samples marked with black triangles did not carry UM-specific chromosomal aberrations. A sample with partial loss of chromosome 3 is highlighted by an asterisk. The high-risk group was determined based on the presence of M3 or BAP1 mutation in tumor tissue and refined by gene expression profiling. Insertions, deletions, and one insertion/deletion mutation in the BAP1 gene were combined as INDEL. Abbreviations: SRT, – stereotactic radiotherapy in the past

Gene expression changes in high-risk tumors

High-risk UMs were characterized by extensive gene expression reprogramming with 2,262 differentially expressed genes (DEGs), 1,171 downregulated and 1,091 upregulated compared to low-risk tumors (Fig. 2B, C). The top upregulated and downregulated genes with the highest p-values are displayed in the Volcano plot (Fig. 2D). The extent of differential expression ranged from FC 191 in the HTR2B gene to FC -112 in PCDH20. DEGs included 60 epigenetic regulators (Fig. 2E, F), mostly histone modifiers and chromatin remodelers, 16 located on chromosome 3 (SATB1, NEK11, SETMAR, BAP1, KAT2B, UBE2E1, HDAC11, PRKCD, MINA, SFMBT1, BRPF1, HLTF, ACTR8, CTNNB1, PIK3CA, and RYBP). Notably, 87 DEGs were transcriptional factors (TFs), the top of upregulated being OLIG1 (FC 22.9), IRX1 (FC 14.9), and FOXD1 (FC 14.7), downregulated RORC (FC -25.1), DMRT2 (FC -19.1), and MSC (FC -8.5). Among 136 differentially expressed TSGs, 60 were downregulated, with DLC1 (FC -8.7), HPGD (FC -8.2), DCDC2 (FC -7.7), NEURL1 (FC -6.9), and MTUS1 (FC -6.0) having the highest FC values. Finally, 57 of 93 oncogenes were upregulated, with CTNND2 (FC 9.3), CDH1 (FC 7.8), S100A4 (FC 6.6), IGF2 (FC 6.3), RUNX1T1 (FC 6.0) or KIT (FC 5.1) between those with highest FC. The complete list of DEGs, including differentially expressed epigenetic modifiers, TFs, TSGs, and oncogenes, is provided in Additional file 2: Tables 1, 2. Numerous signaling pathways were deregulated in high-risk tumors. The two most significantly enriched were coronavirus disease - COVID-19 and ribosome pathways, with the majority of genes involved in genetic information processing (Fig. 2G). The other enriched pathways were proteoglycans in cancer, hepatocellular carcinoma, or EGFR tyrosine kinase inhibitor resistance. A complete list of significantly enriched pathways is available in Additional file 2: Table 3.

Whole-genome DNA methylation changes

The main goal of this work was to decipher differences in DNA methylation between risk groups. We found 44,398 differentially methylated CpGs, 27,810 hypomethylated, and 16,588 hypermethylated in high-risk tumors (Fig. 3A, Additional file 2: Table 4). The heatmap illustrates the clustering of the top 5,000 differentially methylated CpGs showing high β value differences between risk groups (Fig. 3B). Δβ values ranged between − 0.78 and 0.79 with HTR2B and IL12RB genes among those with top differentially methylated CpGs (Δβ beta values of -0.73 and 0.76, respectively). The distribution across the genomic features and CpG islands differed, with hypermethylation more frequent in TSS1500 and CpG shores (Fig. 3C, D). Hypomethylated CpGs were also more frequent on individual chromosomes (Fig. 3E). The list of the top 70 genes enriched by significant CpGs is provided in Fig. 3F. The highest number of differentially methylated CpGs was found for HDAC4, with 117/0 hypomethylated/hypermethylated CpGs, followed by DIP2C 84/2 and SEPT9 61/1. Most of the CpGs in particular genes were hypomethylated, except for a few exceptions, such as genes PDE4D, IQSEC1, PDE4B, and RGS3, where the majority of the CpGs were hypermethylated (5/23, 7/15, 5/14 and 2/18, respectively).

Integration of gene expression and DNA methylation data

To assess DNA methylation-driven transcriptomic changes, we integrated DNA methylation and gene expression data based on the correlation between FC and methylation β values. Only CpGs with significant inverse correlation were further considered. By this approach, we found 635 hypomethylated/upregulated and 309 hypermethylated/downregulated genes in high-risk tumors (Fig. 4A). The heatmap of the top 30 hypomethylated/upregulated and hypermethylated/downregulated genes is depicted in Fig. 4B. The top hypomethylated/upregulated genes were HTR2B (FC 191.4), CCL18 (FC 28.1), and CHAC1 (FC 18.1), while hypermethylated/downregulated were PCDH20 (FC -111.9), HHATL (FC -48.1), SYNPR (FC -47.4), MEGF10 (FC -25.2), RORC (FC -25.1), MPPED2 (FC -25.0), GSTA3 (FC -18.7), IL12RB2 (FC -18.1), CPS1 (FC -17.1). Venn diagram shows the number of DEGs belonging to individual functional groups, epigenetic modifiers, TFs, TSGs, and oncogenes (Fig. 4C). β values were lower in the high-risk UMs when differentially methylated CpGs were considered (Fig. 4D). The differences between high- and low-risk groups in BAP1 gene DNA methylation are demonstrated in Fig. 4E. The correlation coefficient for BAP1 gene expression and cg01493712 β values was – 0.496 (p = 0.014). The extent and distribution of differences between high- and low-risk UMs in DNA methylation and gene expression across the genome are displayed in the circos graph (Fig. 4F). Aberrant DNA methylation in high-risk tumors was associated with the deregulation of key oncogenic pathways such as EGFR tyrosine kinase inhibitor resistance, focal adhesion, proteoglycans in cancer, PI3K-Akt signaling or ECM-receptor interaction (Fig. 4G). The complete list of enriched pathways is provided in Additional file 2: Table 5.

Validation of whole-genome data

Based on the findings from the integrative analysis, three hypomethylated/upregulated genes, HTR2B (FC 191.4), AHNAK2 (FC 12.6), CALHM2 (FC 7.8), and six hypermethylated/downregulated genes, SLC25A38 (FC -4.6), EDNRB (FC -4.7), TLR1 (FC -8.6), RNF43 (FC -10.8), IL12RB2 (FC -18.1) and MEGF10 (FC -25.2) were selected for validation. The selection was based on Δβ and FC values, and the number of CpGs inversely correlated with gene expression. Pyrosequencing primers were designed to cover the regulatory gene regions with the biggest Δβ values.

The Gene Expression Profiling Interactive Analysis (GEPIA) tool, available online: http://gepia.cancer-pku.cn/ (accessed on 16 December 2022), was applied to verify the prognostic value of selected genes in UM with median gene expression values used as cut-off (26). The expression of all selected genes significantly correlated with OS data available for 80 UM tissues in The Cancer Genome Atlas (TCGA) dataset (Additional file 1: Fig. S1).

Validation of gene expression by qPCR confirmed findings from the whole-genome approach (Additional file 1: Fig. S2). The percentage of DNA methylation obtained by pyrosequencing correlated significantly with β values (Additional file 1: Table S4). DNA methylation of all studied genes differed significantly between high- and low-risk tissues (Fig. 5A) and showed an excellent diagnostic ability to predict risk group (AUC ranging between 0.870 and 0.956, p < 0.001) (Fig. 5B, Additional file 1: Table S5). These values were even higher (AUC = 0.999 and 0.945, respectively; p < 0.001) for the combination of AHNAK2 and CALHM2 hypomethylation, with either IL12RB2 (Signature 1) or SLC25A38 hypermethylation (Signature 2). Kaplan-Meier survival curves with log-rank test and univariate Cox regression analysis confirmed that DNA methylation of MEGF10, SLC25A38, and CALHM2 genes is sufficient to stratify patients reasonably well as the standard risk groups based on chromosomal rearrangements and mutation profiling (Fig. 5C, Table 1). Increasing tumor volume, ciliary body melanoma, Ki-67 proliferation over 10%, MEGF10 hypermethylation, and CALHM2 hypomethylation, and both methylation signatures were confirmed as independent OS risk factors by multivariate Cox regression analysis.

Table 1

Univariate and multivariate Cox regression models for clinical variables and DNA methylation of all selected genes.
	Univariate			Multivariate*
Variables	p-value	HR	95.0% CI	p-value	HR	95.0% CI
Age	0.264	0.98	0.94–1.02
Volume [cm3]	0.002	2.28	1.36–3.85	0.008	2.00	1.20–3.35
Dg. C69.4	0.001	3.77	1.67–8.52	0.026	2.67	1.13–6.31
Epithelial cell type	0.023	3.695	1.20-11.38
Ki-67 (> 10%)	0.030	3.01	1.11–8.16	0.033	3.07	1.10–8.60
Metastasis	0.021	3.24	1.19–8.78
Secondary malignancy	0.395	0.53	0.12–2.31
Risk group	0.014	4,83	1.38–16.89	0.019	4.57	1.28–16.29
HTR2B	0.058	2.97	0.97–9.14
AHNAK	0.086	2.40	0.89–6.49
CALHM2	0.025	3.62	1.18–11.15	0.018	4.38	1.29–14.88
SLC25A38	0.016	3.99	1.30-12.25
EDNRB	0.338	1.60	0.61–4.22
TLR1	0.174	1.96	0.74–5.16	0.036	3.43	1.08–10.87
RNF43	0.382	1.56	0.58–4.22
IL12RB2	0.063	2.90	0.95–8.92
MEGF10	0.019	5.85	1.34–25.67	0.024	5.59	1.26–24.82
Signature 1	0.012	4.95	1.42–17.31	0.018	4.61	1.30-16.41
Signature 2	0.009	7.27	1.66–31.92	0.011	7.03	1.58–31.33
* Clinical variables with p < 0.1 from univariate analysis were included in multivariate models, where only values for significant variables are presented. Signature 1: AHNAK2 and CALHM2 hypomethylation combined with IL12RB hypermethylation; Signature 2: AHNAK2 and CALHM2 hypomethylation combined with SLC25A38 hypermethylation

Despite the unprecedented understanding of molecular drivers and prognostic markers, UM survival rates have not changed over decades. While the entire spectrum of genes deregulated in poor prognosis M3 tumors was already published in 2004 (27), epigenome reprogramming in high-risk patients was discovered relatively recently (9, 10). Here we show the extent of DNA methylation-regulated reprograming in poor-prognosis UMs. Moreover, we demonstrate the prognostic potential of DNA methylation and epigenetic regulation of several genes critically involved in UM pathogenesis.

A low degree of genetic complexity and genomic instability are typical features of UMs (28, 29). However, two seminal studies demonstrated the significance of DNA methylation in UM (9, 10). Recently, comprehensive CpG methylation landscapes of the different layers of the human eye were published, unveiling the gene networks involved in DNA methylation-mediated human eye development and associated with different visual disorders, including UM (30). The authors demonstrated frequent hypermethylation of TSGs in ocular tumors compared to control non-tumor tissues. From the same database, a methylation signature of 11 genes was discovered by Li et al. (31), separating UM patients into two groups with shorter and longer metastasis-free survival. Recently, a 10-gene methylation signature was retrieved from TCGA and GEO datasets using machine learning-based integrative analysis, stratifying patients into two risk groups (32). The authors identified 40 methylation-driven genes, including three, EDNRB, SLC25A38, and IL12RB2, validated herein. This evidence supports the significance of DNA methylation in the transcriptional regulation of genes involved in UM progression.

BAP1 gene, located on chromosome 3, encodes a nuclear ubiquitin carboxylase with deubiquitinase activity (33). It is associated with BRCA1 and is needed for the regulation of DNA damage response and controls transcription and chromatin structure via histone 2A deubiquitination (34). Depletion of BAP1 determines cell dedifferentiation and acquisition of stem-like phenotype (35). It has been proposed that loss of BAP1 may also favor inflammatory TME (36), although UM is considered poorly immunogenic, primarily due to its immuno-privileged site of origin. However, opposing the other tumors, a high density of TILs was associated with a worse prognosis in UM (37, 38). The immune infiltration approach showed that neutrophils and CD8⁺ T cells had prognostic value. Four of the top DNA methylation-regulated genes in our study, EDNRB, RNF43, IL12RB2, and CALHM2, were identified within the panel of 21 UM prognostic genes that closely interacted with immune and stromal cells present in TME. While EDNRB and RNF43 expression negatively correlated with CD8⁺ T cells and positively with neutrophil infiltration, and IL12RB2 was related to neutrophils only, CALHM2 expression was positively correlated with CD8⁺ and negatively with neutrophil infiltration (15). In UM, a high TAM infiltration was strongly associated with epithelioid cells, heavy pigmentation, high microvessel density, and, consequently, UM-specific mortality (39). Decreased EDNRB expression was associated with poor prognosis in UM and other neural crest cell cancers by Smith et al. already in 2002 (40). Tumor suppressor RNF43 is a stem-cell E3 ligase that induces endocytosis of Wnt receptors (41). Its regulation by DNA methylation has not been reported to date.

IL12RB gene product IL-12Rβ2 is a subunit of the IL-12 receptor. Together with IL-12Rβ1 and IL-12β2 subunits generate high-affinity binding sites for IL-12, one of the most potent antitumor cytokines (42). Epigenetic silencing of IL12RB is a recurrent event in lung cancer (43). RNF43, SLC25A38, and CALHM2 are listed among the most differential UM survival genes based on transcriptome data from 80 UM cases from the TCGA dataset (26). The downregulation of SLC25A38, located on chromosome 3, was associated with enhanced migration of UM cells and promoted metastasis in mice models (44). SLC25A38 is a mitochondrial glycine transporter inducing caspase-dependent apoptosis. TLR1, related to immune response activation, belongs among several immune genes that significantly correlate with M3 status but do not correlate with BAP1 expression (14). Its regulation by DNA methylation was reported in glioma (45).

MEGF10 is a type I transmembrane protein highly expressed in the central nervous system, myoblasts, retina, and muscle satellite cells. It was shown to play an inhibitory effect on the progression of UM by regulating malignant cell behaviors (46). MEGF10 was one of the top hypermethylated genes in BAP1 mutated tumors associated with disease-free survival in UM (11). It has been assigned an epigenetically repressed candidate tumor suppressor in neuroblastoma due to its growth-repressive properties in their precursor normal human neural crest cells, common with melanoma precursor cells (47). Similarly to our findings, the authors reported a preponderance of DNA hypomethylation in neuroblastoma, suggesting that epigenetic gene activation can be more common than repression. Besides DNA hypermethylation, MEGF10 expression was also suppressed by repressive histone modifications H3K27me3 and H3K9me2.

As is well known, activating mutations in GNAQ and GNA11, coding for Gα subunits of G proteins, are major drivers of UM tumorigenesis (48). Gαq pathway is generally activated by ligand binding to the G protein-coupled receptors, which also include serotonin (5-hydroxytryptamine, 5-HT) receptors 2A (HTR2A) and 2B (HTR2B). HTR2B expression represents one of the most reliable discriminants of GEP molecular signature markers (27) and the highest upregulated gene in high-risk UMs. Aside from its role as a neurotransmitter and vascular active molecule, HTR2B ligand serotonin (5-HT) is also a mitogen for hepatocytes that promotes liver regeneration (49, 50). Stimulation of HTR2B on hepatic stellate cells was shown to activate the expression of TGFβ1, which is involved in various aspects of cell proliferation, differentiation, migration, apoptosis, angiogenesis, and immune surveillance (51, 52). Although it was recently shown that HTR2B is under the regulatory influence of transcription factors NF1 and RUNX1, and the role of the proteasome in HTR2B upregulation was assessed, these factors did not sufficiently explain the elevated level of HTR2B protein in high-risk UMs (53, 54). The same group also investigated the effect of the selective serotonin antagonist PRX-08066 on UM cells and confirmed the impact of HTR2B on cell viability, proliferation, and migration (55). The 5-HT2 receptors, coupled with an intracellular G protein, stimulate intracellular calcium signaling by activating phospholipase C.

AHNAK2, upregulated in UM, was recently characterized as an oncoprotein. Its knockdown in UM cell lines dramatically suppressed cell proliferation, migratory and invasive abilities, and activation of the PI3K signaling pathway. Importantly, AHNAK2 levels have been shown to correlate to immune-cell subpopulations, including CD8+, CD4+, T, and B cells, macrophages, and dendritic cells. It was hypothesized that AHNAK2 overexpression causes immune system dysregulation via calcium signaling, which deserves further studies.

Our study has two main limitations. First is a relatively small sample size, which is not surprising given the rare character of UM and is comparable to similar, recently published studies (11, 12, 56). The second one is that all samples were obtained from large tumors treated by enucleation. Therefore, these data could have some selection bias and not entirely represent a standard clinical population. However, despite these weaknesses, we provide convincing data demonstrating extensive deregulation of the DNA methylation landscape in high-risk UMs and supporting the clinical potential of DNA methylation as a reliable and methodically accessible prognostic marker in UM.

Here we demonstrated DNA methylation-mediated deregulation of numerous genes, some of them critically involved in UM progression. Although only nine validated genes were discussed, the findings from the integrative analysis and the extent of delta beta values suggest the significant contribution of DNA methylation aberrancy in the development of high-risk phenotype. However, given the proposed functions of deregulated epigenetic regulators, we expect broader epigenomic reprogramming at histone modifications and chromatin remodeling levels, the study of which is beyond the scope of this work.

AJCC: American Joint Committee on Cancer

abs(FC): Absolute fold change

AUC: Area under the curve

Bnd: Boundaries

CI: Confidence interval

D3: Chromosome 3 disomy

ddPCR: Digital droplet PCR

DEGs: Differentially expressed genes

FC: Fold change

FDR: False discovery rate

GEP: Gene expression profiling

GEPIA: Gene Expression Profiling Interactive Analysis

HR: hazard ratio

IGR: Intergenic region

IHC: Immunohistochemical

M3: Chromosome 3 monosomy

MLPA: Multiplex ligation-dependent probe amplification

OS: Overall survival

qPCR: Quantitative real-time PCR

ROC: Receiver operating characteristics curve

SRT: Stereotactic radiotherapy

TAMs: Tumor-associated macrophages

TCGA: The Cancer Genome Atlas

TFs: Transcriptional factors

TILs: Tumor-infiltrating lymphocytes

TME: Tumor microenvironment

TSGs: Tumor suppressor genes

TSS: Transcription start site

UM: Uveal melanoma

Ethics approval and consent to participate

The Ethics Committee of the Ruzinov Hospital Bratislava approved the study (EK/250/2018), and written informed consent was obtained from all patients.

Consent for publication

Not applicable.

Availability of data and materials

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Competing interests

The authors declare that they have no competing interests.

Funding

This research was funded by the Slovak Research and Development Agency grant number APVV-17-0369, The Ministry of Education, Science, Research and Sport of the Slovak Republic, grant number VEGA 1/0395/21 and LISPER (ITMS 313011V446: Integrative strategy in the development of personalized medicine of selected malignant tumors and its impact on quality of life. Operational program integrated infrastructure 2014–2020) projects.

Authors' contributions

The experiments were conceptualized and designed by BS, AF and ZD. BS, ZD, IJ and AF acquired funding, BS, AF and ZD were responsible for projects administration and supervision. AF and DL participated in the patient recruitment, enucleations, and sampling. PB performed the immunohistochemical analysis and contributed to data interpretation. VHK, DD, LD, and VB processed the samples and performed the experiments and subsequent data analyses. DD, KJ, IK, MS, and DB participated in genomic profiling. AS, AF, MP, and KC performed transcriptomic and methylome analyses and bioinformatic interpretations. DD, VHK, and AS significantly contributed to validation experiments. BS and AS performed the statistical analyses and interpreted and discussed data. BS, AS and VHK drafted the manuscript, DD, IJ, and ZD edited the manuscript, and VB prepared the figures and graphs. All authors have read, reviewed, and approved the final version of the manuscript.

Acknowledgments

The authors would like to thank Dr. Andrea Štanclova (Department of Molecular Biology and Genomics, Jessenius Faculty of Medicine in Martin, Comenius University, Bratislava, Slovakia) for her technical assistance and valuable contributions.

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No competing interests reported.

Additionalfile1Soltysovaetal.2022.docx
Additional file 1.docxcontains: Table S1.Sequencing primers used for BAP1 mutation detection Table S2.Sequences of qPCR primers Table S3.Sequences of pyrosequencing primers Figure S1.Kaplan Meier survival plots constructed based on gene expression of selected genes from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases using The online Gene Expression Profiling Interactive Analysis (GEPIA) tool. Figure S2.Validation of mRNA expression by qPCR. Table S4.Correlation between methylation beta values for individual probes and % of DNA methylation obtained by analysis of identical CpGs by pyrosequencing. Table S5. Individual AUC values.
Additionalfile2Soltysovaetal.2022.xlsx
Additional file 2.xlsxcontains: Table 1. Differentially expressed genes in high-risk vs. low-risk tumors Table 2. The complete list of differentially expressed genes, epigenetic modifiers, transcription factors, tumor suppressor genes, and oncogenes are classified based on the EpiFactors database (ref. 21), TSGene 2.0, and ONGene databases (refs. 22, 23) Table 3. A complete list of deregulated signaling pathways in high-risk uveal melanoma tumors Table 4. A complete list of differentially methylated probes in high-risk tissues Table 5. The complete list of enriched pathways from integrated analysis

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Aberrant DNA methylation of uveal melanoma drivers as an excellent prognostic tool

Status:

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Abstract

Figures

Background

Materials And Methods

Patient samples

Genomic profiling

Chromosomal rearrangements

Hotspot mutations

BAP1 mutations

Transcriptome profiling and Validation

Quantitative RT-PCR

Methylome profiling and Validation

Integration of whole-genome methylation and gene expression data

Pyrosequencing

Statistical analysis

Results

Baseline characteristics

Gene expression changes in high-risk tumors

Whole-genome DNA methylation changes

Integration of gene expression and DNA methylation data

Validation of whole-genome data

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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