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A Peripheral Blood DNA Methylation Signature of Hepatic Fat Reveals a Potential Causal Pathway for Nonalcoholic Fatty Liver Disease.
Ma J, Nano J, Ding J, Zheng Y, Hennein R, Liu C, Speliotes EK, Huan T, Song C, Mendelson MM, Joehanes R, Long MT, Liang L, Smith JA, Reynolds LM, Ghanbari M, Muka T, van Meurs JBJ, Alferink LJM, Franco OH, Dehghan A, Ratliff S, Zhao W, Bielak L, Kardia SLR, Peyser PA, Ning H, VanWagner LB, Lloyd-Jones DM, Carr JJ, Greenland P, Lichtenstein AH, Hu FB, Liu Y, Hou L, Darwish Murad S, Levy D
(2019) Diabetes 68: 1073-1083
MeSH Terms: Biomarkers, DNA Methylation, Diabetes Mellitus, Type 2, Fats, Female, Humans, Lipopolysaccharide Receptors, Liver, Male, Middle Aged, Non-alcoholic Fatty Liver Disease, Risk Factors
Show Abstract · Added January 10, 2020
Nonalcoholic fatty liver disease (NAFLD) is a risk factor for type 2 diabetes (T2D). We aimed to identify the peripheral blood DNA methylation signature of hepatic fat. We conducted epigenome-wide association studies of hepatic fat in 3,400 European ancestry (EA) participants and in 401 Hispanic ancestry and 724 African ancestry participants from four population-based cohort studies. Hepatic fat was measured using computed tomography or ultrasound imaging and DNA methylation was assessed at >400,000 cytosine-guanine dinucleotides (CpGs) in whole blood or CD14+ monocytes using a commercial array. We identified 22 CpGs associated with hepatic fat in EA participants at a false discovery rate <0.05 (corresponding = 6.9 × 10) with replication at Bonferroni-corrected < 8.6 × 10 Mendelian randomization analyses supported the association of hypomethylation of cg08309687 () with NAFLD ( = 2.5 × 10). Hypomethylation of the same CpG was also associated with risk for new-onset T2D ( = 0.005). Our study demonstrates that a peripheral blood-derived DNA methylation signature is robustly associated with hepatic fat accumulation. The hepatic fat-associated CpGs may represent attractive biomarkers for T2D. Future studies are warranted to explore mechanisms and to examine DNA methylation signatures of NAFLD across racial/ethnic groups.
© 2019 by the American Diabetes Association.
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Genetic Data from Nearly 63,000 Women of European Descent Predicts DNA Methylation Biomarkers and Epithelial Ovarian Cancer Risk.
Yang Y, Wu L, Shu X, Lu Y, Shu XO, Cai Q, Beeghly-Fadiel A, Li B, Ye F, Berchuck A, Anton-Culver H, Banerjee S, Benitez J, Bjørge L, Brenton JD, Butzow R, Campbell IG, Chang-Claude J, Chen K, Cook LS, Cramer DW, deFazio A, Dennis J, Doherty JA, Dörk T, Eccles DM, Edwards DV, Fasching PA, Fortner RT, Gayther SA, Giles GG, Glasspool RM, Goode EL, Goodman MT, Gronwald J, Harris HR, Heitz F, Hildebrandt MA, Høgdall E, Høgdall CK, Huntsman DG, Kar SP, Karlan BY, Kelemen LE, Kiemeney LA, Kjaer SK, Koushik A, Lambrechts D, Le ND, Levine DA, Massuger LF, Matsuo K, May T, McNeish IA, Menon U, Modugno F, Monteiro AN, Moorman PG, Moysich KB, Ness RB, Nevanlinna H, Olsson H, Onland-Moret NC, Park SK, Paul J, Pearce CL, Pejovic T, Phelan CM, Pike MC, Ramus SJ, Riboli E, Rodriguez-Antona C, Romieu I, Sandler DP, Schildkraut JM, Setiawan VW, Shan K, Siddiqui N, Sieh W, Stampfer MJ, Sutphen R, Swerdlow AJ, Szafron LM, Teo SH, Tworoger SS, Tyrer JP, Webb PM, Wentzensen N, White E, Willett WC, Wolk A, Woo YL, Wu AH, Yan L, Yannoukakos D, Chenevix-Trench G, Sellers TA, Pharoah PDP, Zheng W, Long J
(2019) Cancer Res 79: 505-517
MeSH Terms: Biomarkers, Tumor, Carcinoma, Ovarian Epithelial, Cohort Studies, DNA Methylation, European Continental Ancestry Group, Female, Genetic Predisposition to Disease, Humans, Models, Genetic, Ovarian Neoplasms, Predictive Value of Tests, Risk, Women's Health
Show Abstract · Added March 26, 2019
DNA methylation is instrumental for gene regulation. Global changes in the epigenetic landscape have been recognized as a hallmark of cancer. However, the role of DNA methylation in epithelial ovarian cancer (EOC) remains unclear. In this study, high-density genetic and DNA methylation data in white blood cells from the Framingham Heart Study ( = 1,595) were used to build genetic models to predict DNA methylation levels. These prediction models were then applied to the summary statistics of a genome-wide association study (GWAS) of ovarian cancer including 22,406 EOC cases and 40,941 controls to investigate genetically predicted DNA methylation levels in association with EOC risk. Among 62,938 CpG sites investigated, genetically predicted methylation levels at 89 CpG were significantly associated with EOC risk at a Bonferroni-corrected threshold of < 7.94 × 10. Of them, 87 were located at GWAS-identified EOC susceptibility regions and two resided in a genomic region not previously reported to be associated with EOC risk. Integrative analyses of genetic, methylation, and gene expression data identified consistent directions of associations across 12 CpG, five genes, and EOC risk, suggesting that methylation at these 12 CpG may influence EOC risk by regulating expression of these five genes, namely , and . We identified novel DNA methylation markers associated with EOC risk and propose that methylation at multiple CpG may affect EOC risk via regulation of gene expression. SIGNIFICANCE: Identification of novel DNA methylation markers associated with EOC risk suggests that methylation at multiple CpG may affect EOC risk through regulation of gene expression.
©2018 American Association for Cancer Research.
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13 MeSH Terms
Machine learning selected smoking-associated DNA methylation signatures that predict HIV prognosis and mortality.
Zhang X, Hu Y, Aouizerat BE, Peng G, Marconi VC, Corley MJ, Hulgan T, Bryant KJ, Zhao H, Krystal JH, Justice AC, Xu K
(2018) Clin Epigenetics 10: 155
MeSH Terms: Adult, CpG Islands, DNA Methylation, Epigenesis, Genetic, Female, Frailty, Genome-Wide Association Study, HIV Infections, Humans, Machine Learning, Male, Middle Aged, Mortality, Prognosis, Signal Transduction, Smoking
Show Abstract · Added December 11, 2019
BACKGROUND - The effects of tobacco smoking on epigenome-wide methylation signatures in white blood cells (WBCs) collected from persons living with HIV may have important implications for their immune-related outcomes, including frailty and mortality. The application of a machine learning approach to the analysis of CpG methylation in the epigenome enables the selection of phenotypically relevant features from high-dimensional data. Using this approach, we now report that a set of smoking-associated DNA-methylated CpGs predicts HIV prognosis and mortality in an HIV-positive veteran population.
RESULTS - We first identified 137 epigenome-wide significant CpGs for smoking in WBCs from 1137 HIV-positive individuals (p < 1.70E-07). To examine whether smoking-associated CpGs were predictive of HIV frailty and mortality, we applied ensemble-based machine learning to build a model in a training sample employing 408,583 CpGs. A set of 698 CpGs was selected and predictive of high HIV frailty in a testing sample [(area under curve (AUC) = 0.73, 95%CI 0.63~0.83)] and was replicated in an independent sample [(AUC = 0.78, 95%CI 0.73~0.83)]. We further found an association of a DNA methylation index constructed from the 698 CpGs that were associated with a 5-year survival rate [HR = 1.46; 95%CI 1.06~2.02, p = 0.02]. Interestingly, the 698 CpGs located on 445 genes were enriched on the integrin signaling pathway (p = 9.55E-05, false discovery rate = 0.036), which is responsible for the regulation of the cell cycle, differentiation, and adhesion.
CONCLUSION - We demonstrated that smoking-associated DNA methylation features in white blood cells predict HIV infection-related clinical outcomes in a population living with HIV.
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Examining How the MAFB Transcription Factor Affects Islet β-Cell Function Postnatally.
Cyphert HA, Walker EM, Hang Y, Dhawan S, Haliyur R, Bonatakis L, Avrahami D, Brissova M, Kaestner KH, Bhushan A, Powers AC, Stein R
(2019) Diabetes 68: 337-348
MeSH Terms: Animals, Cells, Cultured, Chromatin Immunoprecipitation, Chromosomes, Artificial, Bacterial, DNA Methylation, Female, Humans, In Vitro Techniques, Insulin-Secreting Cells, Maf Transcription Factors, Large, MafB Transcription Factor, Mice, Mice, Transgenic, Pregnancy, Tryptophan Hydroxylase
Show Abstract · Added January 8, 2019
The sustained expression of the MAFB transcription factor in human islet β-cells represents a distinct difference in mice. Moreover, mRNA expression of closely related and islet β-cell-enriched MAFA does not peak in humans until after 9 years of age. We show that the MAFA protein also is weakly produced within the juvenile human islet β-cell population and that expression is postnatally restricted in mouse β-cells by de novo DNA methylation. To gain insight into how MAFB affects human β-cells, we developed a mouse model to ectopically express in adult mouse β-cells using transcriptional control sequences. Coexpression of MafB with MafA had no overt impact on mouse β-cells, suggesting that the human adult β-cell MAFA/MAFB heterodimer is functionally equivalent to the mouse MafA homodimer. However, MafB alone was unable to rescue the islet β-cell defects in a mouse mutant lacking MafA in β-cells. Of note, transgenic production of MafB in β-cells elevated tryptophan hydroxylase 1 mRNA production during pregnancy, which drives the serotonin biosynthesis critical for adaptive maternal β-cell responses. Together, these studies provide novel insight into the role of MAFB in human islet β-cells.
© 2018 by the American Diabetes Association.
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15 MeSH Terms
A Pan-Cancer Analysis Reveals High-Frequency Genetic Alterations in Mediators of Signaling by the TGF-β Superfamily.
Korkut A, Zaidi S, Kanchi RS, Rao S, Gough NR, Schultz A, Li X, Lorenzi PL, Berger AC, Robertson G, Kwong LN, Datto M, Roszik J, Ling S, Ravikumar V, Manyam G, Rao A, Shelley S, Liu Y, Ju Z, Hansel D, de Velasco G, Pennathur A, Andersen JB, O'Rourke CJ, Ohshiro K, Jogunoori W, Nguyen BN, Li S, Osmanbeyoglu HU, Ajani JA, Mani SA, Houseman A, Wiznerowicz M, Chen J, Gu S, Ma W, Zhang J, Tong P, Cherniack AD, Deng C, Resar L, Cancer Genome Atlas Research Network, Weinstein JN, Mishra L, Akbani R
(2018) Cell Syst 7: 422-437.e7
MeSH Terms: Bone Morphogenetic Protein 5, DNA Methylation, Humans, MicroRNAs, Mutation Rate, Neoplasms, Receptor, Transforming Growth Factor-beta Type I, Signal Transduction, Smad Proteins, Transforming Growth Factor beta
Show Abstract · Added October 30, 2019
We present an integromic analysis of gene alterations that modulate transforming growth factor β (TGF-β)-Smad-mediated signaling in 9,125 tumor samples across 33 cancer types in The Cancer Genome Atlas (TCGA). Focusing on genes that encode mediators and regulators of TGF-β signaling, we found at least one genomic alteration (mutation, homozygous deletion, or amplification) in 39% of samples, with highest frequencies in gastrointestinal cancers. We identified mutation hotspots in genes that encode TGF-β ligands (BMP5), receptors (TGFBR2, AVCR2A, and BMPR2), and Smads (SMAD2 and SMAD4). Alterations in the TGF-β superfamily correlated positively with expression of metastasis-associated genes and with decreased survival. Correlation analyses showed the contributions of mutation, amplification, deletion, DNA methylation, and miRNA expression to transcriptional activity of TGF-β signaling in each cancer type. This study provides a broad molecular perspective relevant for future functional and therapeutic studies of the diverse cancer pathways mediated by the TGF-β superfamily.
Copyright © 2018 Elsevier Inc. All rights reserved.
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Integrated Molecular Characterization of Testicular Germ Cell Tumors.
Shen H, Shih J, Hollern DP, Wang L, Bowlby R, Tickoo SK, Thorsson V, Mungall AJ, Newton Y, Hegde AM, Armenia J, Sánchez-Vega F, Pluta J, Pyle LC, Mehra R, Reuter VE, Godoy G, Jones J, Shelley CS, Feldman DR, Vidal DO, Lessel D, Kulis T, Cárcano FM, Leraas KM, Lichtenberg TM, Brooks D, Cherniack AD, Cho J, Heiman DI, Kasaian K, Liu M, Noble MS, Xi L, Zhang H, Zhou W, ZenKlusen JC, Hutter CM, Felau I, Zhang J, Schultz N, Getz G, Meyerson M, Stuart JM, Cancer Genome Atlas Research Network, Akbani R, Wheeler DA, Laird PW, Nathanson KL, Cortessis VK, Hoadley KA
(2018) Cell Rep 23: 3392-3406
MeSH Terms: DNA Copy Number Variations, DNA Methylation, Gene Expression Regulation, Neoplastic, Humans, Male, MicroRNAs, Neoplasms, Germ Cell and Embryonal, Proto-Oncogene Proteins c-kit, Seminoma, Testicular Neoplasms, ras Proteins
Show Abstract · Added October 30, 2019
We studied 137 primary testicular germ cell tumors (TGCTs) using high-dimensional assays of genomic, epigenomic, transcriptomic, and proteomic features. These tumors exhibited high aneuploidy and a paucity of somatic mutations. Somatic mutation of only three genes achieved significance-KIT, KRAS, and NRAS-exclusively in samples with seminoma components. Integrated analyses identified distinct molecular patterns that characterized the major recognized histologic subtypes of TGCT: seminoma, embryonal carcinoma, yolk sac tumor, and teratoma. Striking differences in global DNA methylation and microRNA expression between histology subtypes highlight a likely role of epigenomic processes in determining histologic fates in TGCTs. We also identified a subset of pure seminomas defined by KIT mutations, increased immune infiltration, globally demethylated DNA, and decreased KRAS copy number. We report potential biomarkers for risk stratification, such as miRNA specifically expressed in teratoma, and others with molecular diagnostic potential, such as CpH (CpA/CpC/CpT) methylation identifying embryonal carcinomas.
Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.
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11 MeSH Terms
Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.
Malta TM, Sokolov A, Gentles AJ, Burzykowski T, Poisson L, Weinstein JN, Kamińska B, Huelsken J, Omberg L, Gevaert O, Colaprico A, Czerwińska P, Mazurek S, Mishra L, Heyn H, Krasnitz A, Godwin AK, Lazar AJ, Cancer Genome Atlas Research Network, Stuart JM, Hoadley KA, Laird PW, Noushmehr H, Wiznerowicz M
(2018) Cell 173: 338-354.e15
MeSH Terms: Carcinogenesis, Cell Dedifferentiation, DNA Methylation, Databases, Genetic, Epigenesis, Genetic, Humans, Machine Learning, MicroRNAs, Neoplasm Metastasis, Neoplasms, Stem Cells, Transcriptome, Tumor Microenvironment
Show Abstract · Added October 30, 2019
Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation.
Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.
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Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer.
Hoadley KA, Yau C, Hinoue T, Wolf DM, Lazar AJ, Drill E, Shen R, Taylor AM, Cherniack AD, Thorsson V, Akbani R, Bowlby R, Wong CK, Wiznerowicz M, Sanchez-Vega F, Robertson AG, Schneider BG, Lawrence MS, Noushmehr H, Malta TM, Cancer Genome Atlas Network, Stuart JM, Benz CC, Laird PW
(2018) Cell 173: 291-304.e6
MeSH Terms: Aneuploidy, Chromosomes, Cluster Analysis, CpG Islands, DNA Methylation, Databases, Factual, Humans, MicroRNAs, Mutation, Neoplasm Proteins, Neoplasms, RNA, Messenger
Show Abstract · Added October 30, 2019
We conducted comprehensive integrative molecular analyses of the complete set of tumors in The Cancer Genome Atlas (TCGA), consisting of approximately 10,000 specimens and representing 33 types of cancer. We performed molecular clustering using data on chromosome-arm-level aneuploidy, DNA hypermethylation, mRNA, and miRNA expression levels and reverse-phase protein arrays, of which all, except for aneuploidy, revealed clustering primarily organized by histology, tissue type, or anatomic origin. The influence of cell type was evident in DNA-methylation-based clustering, even after excluding sites with known preexisting tissue-type-specific methylation. Integrative clustering further emphasized the dominant role of cell-of-origin patterns. Molecular similarities among histologically or anatomically related cancer types provide a basis for focused pan-cancer analyses, such as pan-gastrointestinal, pan-gynecological, pan-kidney, and pan-squamous cancers, and those related by stemness features, which in turn may inform strategies for future therapeutic development.
Copyright © 2018 Elsevier Inc. All rights reserved.
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Comparative Molecular Analysis of Gastrointestinal Adenocarcinomas.
Liu Y, Sethi NS, Hinoue T, Schneider BG, Cherniack AD, Sanchez-Vega F, Seoane JA, Farshidfar F, Bowlby R, Islam M, Kim J, Chatila W, Akbani R, Kanchi RS, Rabkin CS, Willis JE, Wang KK, McCall SJ, Mishra L, Ojesina AI, Bullman S, Pedamallu CS, Lazar AJ, Sakai R, Cancer Genome Atlas Research Network, Thorsson V, Bass AJ, Laird PW
(2018) Cancer Cell 33: 721-735.e8
MeSH Terms: Adenocarcinoma, Aneuploidy, Chromosomal Instability, DNA Methylation, DNA Polymerase II, Epigenesis, Genetic, Female, Gastrointestinal Neoplasms, Gene Regulatory Networks, Heterogeneous-Nuclear Ribonucleoproteins, Humans, Male, Microsatellite Instability, MutL Protein Homolog 1, Mutation, Poly-ADP-Ribose Binding Proteins, Polymorphism, Single Nucleotide, Proto-Oncogene Proteins p21(ras), SOX9 Transcription Factor
Show Abstract · Added October 30, 2019
We analyzed 921 adenocarcinomas of the esophagus, stomach, colon, and rectum to examine shared and distinguishing molecular characteristics of gastrointestinal tract adenocarcinomas (GIACs). Hypermutated tumors were distinct regardless of cancer type and comprised those enriched for insertions/deletions, representing microsatellite instability cases with epigenetic silencing of MLH1 in the context of CpG island methylator phenotype, plus tumors with elevated single-nucleotide variants associated with mutations in POLE. Tumors with chromosomal instability were diverse, with gastroesophageal adenocarcinomas harboring fragmented genomes associated with genomic doubling and distinct mutational signatures. We identified a group of tumors in the colon and rectum lacking hypermutation and aneuploidy termed genome stable and enriched in DNA hypermethylation and mutations in KRAS, SOX9, and PCBP1.
Copyright © 2018 Elsevier Inc. All rights reserved.
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lncRNA Epigenetic Landscape Analysis Identifies EPIC1 as an Oncogenic lncRNA that Interacts with MYC and Promotes Cell-Cycle Progression in Cancer.
Wang Z, Yang B, Zhang M, Guo W, Wu Z, Wang Y, Jia L, Li S, Cancer Genome Atlas Research Network, Xie W, Yang D
(2018) Cancer Cell 33: 706-720.e9
MeSH Terms: Animals, Binding Sites, Breast Neoplasms, Cell Cycle, Cell Line, Tumor, CpG Islands, DNA Methylation, Epigenesis, Genetic, Female, Gene Expression Regulation, Neoplastic, Humans, Mice, Neoplasm Transplantation, Prognosis, Promoter Regions, Genetic, Proto-Oncogene Proteins c-myc, RNA, Long Noncoding, Up-Regulation
Show Abstract · Added October 30, 2019
We characterized the epigenetic landscape of genes encoding long noncoding RNAs (lncRNAs) across 6,475 tumors and 455 cancer cell lines. In stark contrast to the CpG island hypermethylation phenotype in cancer, we observed a recurrent hypomethylation of 1,006 lncRNA genes in cancer, including EPIC1 (epigenetically-induced lncRNA1). Overexpression of EPIC1 is associated with poor prognosis in luminal B breast cancer patients and enhances tumor growth in vitro and in vivo. Mechanistically, EPIC1 promotes cell-cycle progression by interacting with MYC through EPIC1's 129-283 nt region. EPIC1 knockdown reduces the occupancy of MYC to its target genes (e.g., CDKN1A, CCNA2, CDC20, and CDC45). MYC depletion abolishes EPIC1's regulation of MYC target and luminal breast cancer tumorigenesis in vitro and in vivo.
Copyright © 2018 Elsevier Inc. All rights reserved.
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