Humanmethylation450 beadchip

We used binomial tests to measure the enrichment of differentially methylated CpGs in gene bodies, promoters, and erythroid enhancers as compared to the rest of the genome surveyed by the Illumina HumanMethylation450 BeadChip. To compare the enrichment of differentially methylated CpGs in erythroid enhancers versus other enhancers present in different cell types, we obtained enhancer coordinates from nine cell lines analyzed by the ENCODE Project [22 (link)]. We used DAVID to measure the enrichment of gene ontology (GO) terms and biological pathways among genes with a mean β-value difference >0.1 and a combined P <9 × 10⁻⁷ in their body or promoter, taking into account the coverage of the Illumina HumanMethylation450 BeadChip [23 (link),24 (link)]. To identify transcription factor binding motifs that are enriched at differentially methylated loci, we used the HOMER software and a list of 495 pre-defined transcription factor motifs [25 (link)], limiting the search to 200 base pairs on both sides of differentially methylated CpGs. For this analysis, we compared the 5,000 most hypomethylated regions in fetal erythroblasts to the 5,000 most hypomethylated regions in adult erythroblasts, as recommended. HOMER results were stable using different thresholds to select hypomethylated regions.

All DNA samples were assessed for integrity, quantity and purity by electrophoresis in a 1.3% agarose gel, picogreen quantification and nanodrop measurements. All samples were randomly distributed into 96-well plates. Bisulfite-converted DNA (200 ng) were used for hybridization on the HumanMethylation450 BeadChip (Illumina), comprising 25 PCa and 5 MNPT.
HumanMethylation450 BeadChip data were processed using Bioconductor minfi package [11 (link)]. The “Ilumina” procedure, which mimics the method of GenomeStudio (Illumina), was performed comprising background correction and normalization taking the first array of the plate as reference. Probes with one or more single-nucleotide polymorphisms (SNPs) with a minor allele frequency (MAF) >1% (1000 Genomes) in the first 10 bp of the interrogated CpG were removed. The methylation level (β) for each of the 485,577 CpG sites was calculated as the ratio of methylated signal divided by the sum of methylated and unmethylated signals, multiplied by 100. After normalization step, probes related to X and Y chromosomes were removed. All analyses were performed in human genome version 19 (hg19), and data was deposited in GEO repository under accession number GSE52955.

A total of 483 renal samples in the TCGA Kidney Clear Cell Carcinoma (TCGA-KIRC) cohort obtained at the time of nephrectomy were subjected to the Illumina Infinium 450k Human DNA methylation Beadchip to obtain genome wide DNA methylation status in approximately 485,000 CpGs in renal carcinoma and adjacent normal kidney tissue samples from ccRCC patients (Cancer Genome Atlas Research Network, 2013 (link)). We obtained the beta value matrix of the TCGA-KIRC methylation profile from UCSC Xena¹. Meanwhile, we obtained associated copy number data, mRNA expression data, somatic mutation data and clinical data of TCGA-KIRC from GDC data portal². GSE113501, measured by Illumina HumanMethylation450 BeadChip and included 115 ccRCC patients and the associated methylation profile, were downloaded for Gene Expression Omnibus (GEO)³ (Evelonn et al., 2019 (link)). We used GSE113501 as an independent validation set to verify the relationship between methylation status and disease metastasis. GSE61441, measured by Illumina HumanMethylation450 BeadChip, included 46 ccRCC tissue and 46 matched normal kidney tissues, were used to validate the relationship between the methylation status and ccRCC and normal kidney tissue (Wei et al., 2015 (link)).

The methylation (Illumina HumanMethylation450 BeadChip) and transcriptome (RNA-Seq) profiles of 80 UM tissues were obtained from The Cancer Genome Atlas (TCGA) database (TCGA-UVM, https://portal.gdc.cancer.gov/). The corresponding clinicopathological information of UM patients from TCGA was obtained from the cBioPortal website (22 (link)) (https://www.cbioportal.org/). Considering that there were no normal tissues in TCGA-UVM, we downloaded another dataset(GSE57362 (23 (link))) containing only methylation (Illumina HumanMethylation450 BeadChip) profiles of 10 uvea tissues and 15 UM samples from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/). Other 57 UM cases from GSE44295 (Zhang’s cohort, platform: Illumina HumanRef-8 v3.0 expression beadchip) were employed for validation. GSE143952 (Mikkelsen’s cohort, platform: NanoString Custom Panel) and GSE148387 (24 (link)) (platform: Illumina NextSeq 500) both consisting of gene expression profiles of 12 CM samples and 8 healthy conjunctiva tissues were also included in this study. Fragments per kilobase million (FPKM) values of UM samples downloaded from TCGA were normalized as transcripts per kilobase million (TPM) and subsequently transformed as log2(TPM+1). The gene expression level of samples from GEO was transformed by log2(x+1).

Methylation profiling was done using the Illumina Infinium methylation assay and the HumanMethylation450 BeadChip. First, the cord blood DNA samples were pre-screened for the suitability of the Methylation450 assay using Nanodrop to check the purity and the PicoGreen assay to check the dsDNA concentration. The EZ DNA Methylation kit was used for the bisulfite conversion (Zymo Research), and 100 to 250 ng of bisulfite-converted DNA was hybridized onto the Infinium Human Methylation450 BeadChip following the Illumina Infinium HD Methylation protocol.