Computational protocol: Genome-Wide DNA Methylation Indicates Silencing of Tumor Suppressor Genes in Uterine Leiomyoma

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[…] Genomic DNA was isolated from 20 mg frozen tissues using the DNeasy Blood & Tissue (Qiagen, Valencia, CA). One microgram of genomic DNA from each sample was bisulfite-modified using the EZ DNA Methylation kit (Zymo Research, Orange, CA), according to the manufacturer's protocol along with the technical validation of the assay . Bisulfite-modified DNA was hybridized to the HumanMethylation27 Beadchip (Illumina Inc., San Diego, CA).Total RNA was isolated from 20 mg of frozen tissues using the RNeasy Fibrous Tissue kit (Qiagen) according to manufacturer protocols with minor modifications. After elution, RNA samples were quantified using a ND-1000 spectrophotometer (NanoDrop Wilmington, DE) and evaluated for degradation using a 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA). For use in hybridization, samples were required to have a RIN>9, an OD260/280 of 1.9–2.0, and OD260/230>1.5, and a 28S∶18S ribosomal band ratio of >1.5. The samples were hybridized into the HumanHT-12 v3 genome-wide gene expression BeadChips according to the manufacturer's protocol (Illumina, Inc.).We used the Bioconductor lumi package, which was developed by our collaborator and is widely used as one of the standard tools to process both Illumina DNA methylation and mRNA expression data. The data first went through a QA/QC step. For Illumina expression data, the data passing QA step was preprocessed using a variance stabilization transformation method followed by quantile normalization. For methylation data, we first performed a color balance adjustment of methylated and unmethylated probe intensities between two color channels using a smooth quantile normalization method. The methylated and unmethylated probe intensities were then normalized using the SSN (Scale and Shift Normalization) method. The methylation M-value (log 2 ratio of methylated and unmethylated probes) was calculated to estimate the methylation level of the measured CpG sites . The follow-up analysis was then based on the M-value. We used a shift and scaling normalization (SSN) method, which includes global background shift during normalization instead of more aggresive quantile normalization as described in reference 45. We made this decision primarily because we produced high quality and consistent data evident by the principal component analysis that we are now incorporating in the supplemental section.After preprocessing, the differential analysis of methylation data was similar to that used for expression microarray data. Probes (for expression data) or CpG-sites (for methylation data) with all samples “Absent” (lower or around background levels) were removed from further analysis to reduce false positives. To compare the differences in both methylation and expression between leiomyoma and myometrial tissues, we performed differential analyses using routines implemented in the limma package . To ensure both high statistical significance and strong biological effects, we require that the differentially methylated CpG sites had an FDR<0.01 and fold-change (based on M-value) of >2; using this process 1031 CpG sites (585 up, 446 down) were identified. For mRNA expression data, we required that the differentially expressed genes had an FDR<0.01 and a fold-change of>1.5; with these parameters, we identified 525 genes (218 up, 307 down). We mapped the differentially methylated CpG sites to the closest downstream gene, and found there are 55 overlapping genes between the lists of genes with changes in DNA methylation and mRNA expression data. The microarray data is MIAME compliant and is available at the Gene Expression Omnibus Web site (http://www.ncbi.nlm.nih.gov/geo) under accession No.GSE31699. […]

Pipeline specifications

Software tools lumi, limma
Databases GEO
Application Gene expression microarray analysis
Organisms Homo sapiens
Diseases Leiomyoma, Neoplasms, Cardiovascular Abnormalities