Project description:Beginning with precursor lesions, aberrant DNA methylation marks the entire spectrum of prostate cancer progression. We mapped the global DNA methylation patterns in prostate tissues (n=17) and cells (n=2) from fifty nanograms of genomic DNA using Methylplex-Next Generation Sequencing (M-NGS). A Hidden Markov Model (HMM)-based algorithm previously used for Chip-Seq data analysis(http://www.sph.umich.edu/csg/qin/HPeak) was used to locate peaks from mapped reads obtained in each sequencing run. The total methylation events in intergenic/intronic regions between benign adjacent and cancer tissues were comparable. While approximately 20% of all CpG islands (CGIs) (68,508) were methylated in tissues, promoter CGI methylation gradually increased from ~12.6% in benign samples to 19.3% and 21.8% in localized and metastatic cancer tissues. We found distinct patterns in promoter methylation around transcription start sites, where methylation occurred directly on the CGIs, flanking regions and on CGI sparse promoters. Among the 6,691 methylated promoters in prostate tissues, 2481 differentially methylated regions (DMRs) are cancer specific and several previously studied targets were among them. A novel cancer specific DMR in WFDC2 promoter showed 77% methylation in cancer (17/22), 100% methylation in transformed prostate cell lines (6/6), none in the benign tissues (0/10) and normal PrEC cells. Integration of LNCaP DNA methylation and H3K4me3 data suggested a role for DNA methylation in alternate transcription start site utilization. While methylated promoters containing CGIs had mutually exclusive H3K4me3 modification, the histone mark was absent in CGI sparse promoters. Finally, we observed difference in methylation of LINE-1 elements between transcription factor ERG positive and negative cancers. The comprehensive methylome map presented here will further our understanding of epigenetic regulation of the prostate cancer genome. We mapped the global DNA methylation patterns in prostate tissues (n=17; data not available in GEO - being deposited in dbGaP for controlled access) and cells (n=2) from fifty nanograms of genomic DNA using Methylplex-Next Generation Sequencing (M-NGS). For replicate analysis in cell lines, a total of 4 runs were completed for PrEC prostate normal cell line, and 5 runs were completed for LNCaP prostate cancer cell line. For tissue samples, 2 benign prostate samples were ran twice on illumina next generation sequencing platform to access overall repeatability of M-NGS.

Project description:<p>Aberrant DNA methylation changes are known to occur during prostate cancer progression beginning with precursor lesions. Utilizing fifty nanograms of genomic DNA in Methylplex-Next Generation Sequencing (M-NGS) we mapped the global DNA methylation patterns in prostate tissues (n=17) and cells (n=2). Peaks were located from mapped reads obtained in each sequencing run using a Hidden Markov Model (HMM)-based algorithm previously used for Chip-Seq data analysis(<a href="http://www.sph.umich.edu/csg/qin/HPeak">http://www.sph.umich.edu/csg/qin/HPeak</a>). The total methylation events in intergenic/intronic regions between benign adjacent and cancer tissues were comparable. Promoter CGI methylation gradually increased from -12.6% in benign samples to 19.3% and 21.8% in localized and metastatic cancer tissues and approximately 20% of all CpG islands (CGIs) (68,508) were methylated in tissues. We observed distinct patterns in promoter methylation around transcription start sites, where methylation occurred directly on the CGIs, flanking regions and on CGI sparse promoters. Among the 6,691 methylated promoters in prostate tissues, 2481 differentially methylated regions (DMRs) are cancer specific and several previously studied targets were among them. A novel cancer specific DMR in WFDC2 promoter showed 77% methylation in cancer (17/22), 100% methylation in transformed prostate cell lines (6/6), none in the benign tissues (0/10) and normal PrEC cells. Integration of LNCaP DNA methylation and H3K4me3 data suggested a role for DNA methylation in alternate transcription start site utilization. While methylated promoters containing CGIs had mutually exclusive H3K4me3 modification, the histone mark was absent in CGI sparse promoters. Finally, we observed difference in methylation of LINE-1 elements between transcription factor ERG positive and negative cancers. The comprehensive methylome map presented here will further our understanding of epigenetic regulation of the prostate cancer genome. Overall Design: We mapped the global DNA methylation patterns in prostate tissues (n=17) and cells (n=2) from fifty nanograms of genomic DNA using Methylplex-Next Generation Sequencing (M-NGS). For replicate analysis in cell lines, a total of 4 runs were completed for PrEC prostate normal cell line, and 5 runs were completed for LNCaP prostate cancer cell line. For tissue samples, 2 benign prostate samples were sequenced twice on Illumina next generation sequencing platform to access overall repeatability of M-NGS.</p>

Project description:Purpose: The aim of this study is to identify known and novel small RNAs (Piwi-interacting RNAs and microRNAs) in normal ovary and epithelial ovarian malignancies by adopting high-throughput RNA sequencing (RNA-Seq) and unveil their possible functions in neoplastic pathways of the two most frequently observed and highly lethal subtypes of epithelial ovarian cancers, endometrioid ovarian cancer (ENOCa) and serous ovarian cancer (SOCa). The study has been performed in normal ovarian tissues as well as malignant tissues of these cancer subtypes. Methods: 1 µg of total RNA was used as the starting material for library preparation as prescribed by Illumina Truseq small RNA library protocol. Small RNA sequencing was carried out by Genotypic Technology, Bangalore, India on Illumina Next-Seq 500 platform. Library preparation was done by performing 3' and 5' adapter ligation followed by reverse transcription of cDNA and amplification of the library. The construct of the library was fractionated to select 16-40 nucleotide insert of small RNAs using PAGE. The quality check of the library was done in Agilent Technologies 2100 Bioanalyzer using a DNA-specific chip, such as High Sensitivity DNA. The sequence reads that passed through Quality Check by FastQC and aligned to the human genome (hg19) were further analysed using in-house pipelines and set of known and novel piRNAs and miRNAs were identified. The sets of known piRNAs and miRNAs identified were assessed for their involvement in neoplastic processes of ovarian cancer subtypes by performing target analysis and GO enrichment studies. Results: Using an in-house prediction pipeline, we mapped about 10-15 million sequence reads per sample to the human genome (hg19) and detected 256, 234 and 219 annotated piRNAs in ENOCa, SOCa, and normal ovary respectively; whereas the average number of known miRNAs present in each sample was estimated to be 480. The annotated piRNAs obtained from each sample exhibited varied length distribution between 26-32 nts. Conclusions: For the first time, our study reported the presence of piRNAs in ENOCa, SOCa and normal ovarian tissue from the next-gen sequencing of small RNAs of 16-40 nts length. The extensive catalogue of human EOCa small RNAs (both piRNAs and miRNAs) detected in this study provides a useful resource to dissect complex neoplastic events that are possibly mediated by these ncRNAs, especially by piRNAs. Moreover, these piRNAs could be used as probable small RNA biomarkers for the EOCa.

Dataset Information

Next Generation Sequencing of piRNAs in 3 prostate cancer tissues and 3 paired prostate tissues

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