The DOI matches its Crossref record; 60 of the 61 references it cites were found in a registry or an index.
Deep sequencing reveals distinct patterns of DNA methylation in prostate cancer
Open accessAbstract
Beginning with precursor lesions, aberrant DNA methylation marks the entire spectrum of prostate cancer progression. We mapped the global DNA methylation patterns in select prostate tissues and cell lines using MethylPlex–next-generation sequencing (M-NGS). Hidden Markov model–based next-generation sequence analysis identified ∼68,000 methylated regions per sample. While global CpG island (CGI) methylation was not differential between benign adjacent and cancer samples, overall promoter CGI methylation significantly increased from ∼12.6% in benign samples to 19.3% and 21.8% in localized and metastatic cancer tissues, respectively ( P -value < 2 × 10 −16 ). We found distinct patterns of promoter methylation around transcription start sites, where methylation occurred not only on the CGIs, but also on flanking regions and CGI sparse promoters. Among the 6691 methylated promoters in prostate tissues, 2481 differentially methylated regions (DMRs) are cancer-specific, including numerous novel DMRs. A novel cancer-specific DMR in the WFDC2 promoter showed frequent methylation in cancer (17/22 tissues, 6/6 cell lines), but not in the benign tissues (0/10) and normal PrEC cells. Integration of LNCaP DNA methylation and H3K4me3 data suggested an epigenetic mechanism for alternate transcription start site utilization, and these modifications segregated into distinct regions when present on the same promoter. Finally, we observed differences in repeat element methylation, particularly LINE-1, between ERG gene fusion-positive and -negative cancers, and we confirmed this observation using pyrosequencing on a tissue panel. This comprehensive methylome map will further our understanding of epigenetic regulation in prostate cancer progression.
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“The statistically significant correlations that were observed, however, accounted for relatively small effects.”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [138]
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“Pairwise DNA-methylation and RNA-seq integration, for the most part, has consisted of the analysis of the correlation between DEGs and methylation patterns [138–140].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [138]
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Vandesompele, J., De Preter, K., Pattyn, F., Poppe, B., Van Roy, N., De Paepe, A., & Speleman, F. (2002) · Genome Biology · doi:10.1186/gb-2002-3-7-research0034 · Cited by 20,038
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Souiai, O., Guerfali, F. Z., Miled, S. B., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,785
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Bird, A. (2002) · Genes & Development · doi:10.1101/gad.947102 · Cited by 7,124
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Bernstein, B. E., Mikkelsen, T. S., Xie, X., Kamal, M., Huebert, D. J., Cuff, J., Fry, B., Meissner, A., Wernig, M., Plath, K., et al. (2006) · Cell · doi:10.1016/j.cell.2006.02.041 · Cited by 5,361
Jones, P. A., & Baylin, S. B. (2007) · Cell · doi:10.1016/j.cell.2007.01.029 · Cited by 4,515
MethPrimer: designing primers for methylation PCRs
Li, L.-C., & Dahiya, R. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.11.1427 · Cited by 2,741
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Meissner, A., Mikkelsen, T. S., Gu, H., Wernig, M., Hanna, J., Sivachenko, A., Zhang, X., Bernstein, B. E., Nusbaum, C., Jaffe, D. B., et al. (2008) · Nature · doi:10.1038/nature07107 · Cited by 2,536
Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning
Cokus, S. J., Feng, S.-H., Zhang, X.-Y., Chen, Z., Merriman, B., Haudenschild, C., Pradhan, S., Nelson, S., Pellegrini, M., & Jacobsen, S. (2008) · Nature · doi:10.1038/nature06745 · Cited by 2,278
Irizarry, R. A., Ladd-Acosta, C., Wen, B., Wu, Z., Montano, C., Onyango, P., Cui, H., Gabo, K., Rongione, M., Webster, M., et al. (2009) · Nature Genetics · doi:10.1038/ng.298 · Cited by 2,177
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Metadata
- Authors
- Kim, J. H., Dhanasekaran, S. M., Prensner, J. R., Cao, X., Robinson, D., Kalyana-Sundaram, S., Huang, C., Shankar, S., Jing, X., Iyer, M., Hu, M., Sam, L., Grasso, C., Maher, C. A., Palanisamy, N., Mehra, R., Kominsky, H. D., Siddiqui, J., Yu, J., Qin, Z. S., & Chinnaiyan, A. M.
- Affiliations
- University of Michigan (US)
- Harvard University (US)
- Northwestern University (US)
- Emory University (US)
- ORCID
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2011-07
- Volume
- 21
- Issue
- 7
- Pages
- 1028-1041
- ISSN
- 1088-9051 (ISSN-L)
- PMC
- PMC3129246
- OpenAlex
- W2114884175
- Cited by
- 188
- References
- 61
- Citations by year
- 2026: 7 · 2025: 2 · 2024: 4 · 2023: 4 · 2022: 7 · 2021: 9 · 2020: 11 · 2019: 12 · 2018: 8 · 2017: 15 · 2016: 14 · 2015: 20 · 2014: 20 · 2013: 25 · 2012: 27
- Type
- posted content
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genome Research (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- U.S. Department of Defense (W81XWH, BC075023)
- Howard Hughes Medical Institute
- Prostate Cancer Foundation
- Amgen
- University of Michigan (P50CA69568)
- Canary Foundation
- National Institutes of Health (W81XWH, CA009676)
- National Institute on Drug Abuse (U54DA021519)
- National Human Genome Research Institute (R01HG005119)
- National Cancer Institute (P50-CA-69568, R01CA132874, U01CA111275, CA009676-18, R00CA129565, K99CA129565, P50CA069568, T32CA009676)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Prostate Cancer Treatment and Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Cancer-related gene regulation
- Keywords
- prostate cancer, DNA methylation, methylome, promoter methylation, CpG island methylation, differentially methylated regions, HE4, H3K4me3, tumor progression, metastatic prostate cancer, epigenetic regulation
- MeSH terms
- DNA, Neoplasm, Epithelial Cells, Humans, Male, Markov Chains, Neoplasm Metastasis, Promoter Regions, Genetic, Prostate, Prostatic Neoplasms, Gene Library, Polymerase Chain Reaction, Sequence Analysis, RNA, CpG Islands, DNA Methylation, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Transcription Initiation Site, Cell Line, Tumor, Epigenomics, High-Throughput Nucleotide Sequencing
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