Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Comparative transcriptomic analysis of prostate cancer and matched normal tissue using RNA-seq

ABSTRACT: We used RNA-seq to interrogate prostate cancer specific gene fusions, alternative splicings, somatic mutations and novel transcripts. We sequenced the transcriptome (polyA+) of 20 prostate cancer tumors and 10 matched normal tissues using Illumina GAII platform. Then we used bioinformatic approaches to identify prostate cancer specific aberrations which include gene fusion, alternative splicing, somatic mutation, etc.

ORGANISM(S): Homo sapiens

SUBMITTER: LIGUO WANG

PROVIDER: E-GEOD-22260 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Recurrent chimeric RNAs enriched in human prostate cancer identified by deep sequencing.

Kannan Kalpana K Wang Liguo L Wang Jianghua J Ittmann Michael M MM Li Wei W Yen Laising L

Proceedings of the National Academy of Sciences of the United States of America 20110512 22

Transcription-induced chimeric RNAs, possessing sequences from different genes, are expected to increase the proteomic diversity through chimeric proteins or altered regulation. Despite their importance, few studies have focused on chimeric RNAs especially regarding their presence/roles in human cancers. By deep sequencing the transcriptome of 20 human prostate cancer and 10 matched benign prostate tissues, we obtained 1.3 billion sequence reads, which led to the identification of 2,369 chimeric ...[more]

PMID: 21571633

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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:A major task in analyzing alternative splicing (AS) by RNA-Seq is to explore and quantify the differential representation of various splice classes and the protein-coding potential of the mapped reads. To this end, we have generated a streamlined bioinformatic pipeline available to the scientific community, which maps RNA-Seq reads to a complex combinatorial database of exon-exon junctions for the unique junction discovery, PTC detection and identification of splice isoforms. Hence, we have incorporated a splice isoform inference and PTC detection algorithm, which facilitates a highly accurate mapping and prediction of splice isoform junctions and nonsense-mediated mRNA decay (NMD)-susceptibility. We used this pipeline to investigate the complexity of the transcriptome and global role of AS-NMD in vivo in mammalian cells, by taking advantage of our Upf2 conditional knock-out mouse. Using tissue-specific Cre deleter strains, we have previously demonstrated in vivo that NMD acts to degrade many transcripts that results from AS. Also, ablating UPF2 (a core NMD factor) in the mouse leads to rapid mortality and collapse of most organs tested (Thoren et al., 2010; Weischenfeldt et al., 2008), suggesting an essential function for NMD in AS. To explore the effect of NMD on global splicing and to generate and validate an attractive bioinformatic pipeline for the scientific community to study AS and NMD, we chose to analyze two different mammalian organ systems with distinct phenotypes upon UPF2 ablation to test the robustness and dynamic range of the analysis. In one end of the spectrum, we chose to analyze the liver, wherein removal of UPF2 results in failure in liver metabolism and a high mortality rate (Thoren et al., 2010). To profile a less affected tissue, we chose to analyze bone marrow-derived macrophages (BMMs). These are macrophages that differentiate in vitro and Upf2 deleted BMMs are completely devoid of NMD activity but nevertheless show no morphological or functional phenotype compared to wild-type controls (Weischenfeldt et al., 2008). Thus, we were able to make a direct comparison between these two tissues to demonstrate the potency of our Isoform and PTC detection pipeline described below, which will allow the scientific community to analyze transcriptome data for global AS and NMD-susceptibility. Examination of UPF2 WT vs. KO in two different murine tissues (liver, bone marrow-derived macrophages).

Dataset Information

Comparative transcriptomic analysis of prostate cancer and matched normal tissue using RNA-seq

Publications

Recurrent chimeric RNAs enriched in human prostate cancer identified by deep sequencing.

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