DNA Methylation-Specific Analysis of G Protein-Coupled Receptor-Related Genes in Pan-Cancer.
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ABSTRACT:
SUBMITTER: Zhang M
PROVIDER: S-EPMC9320183 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Researchers hypothesized that telomere shortening facilitates carcinogenesis. Previous studies found inconsistent associations between blood leukocyte telomere length (LTL) and cancer. Epigenetic reprogramming of telomere maintenance mechanisms may help explain this inconsistency. We examined associations between DNA methylation in telomere-related genes (TRG) and cancer. We analyzed 475 participants providing 889 samples 1 to 3 times (median follow-up, 10.1 years) from 1999 to 2013 in the Normative Aging Study. All participants were cancer-free at each visit and blood leukocytes profiled using the Illumina 450K array. Of 121 participants who developed cancer, 34 had prostate cancer, 10 melanoma, 34 unknown skin malignancies, and 43 another cancer. We examined 2,651 CpGs from 80 TRGs and applied a combination of Cox and mixed models to identify CpGs prospectively associated with cancer (at FDR < 0.05). We also explored trajectories of DNA methylation, logistic regression stratified by time to diagnosis/censoring, and cross-sectional models of LTL at first blood draw. We identified 30 CpGs on 23 TRGs whose methylation was positively associated with cancer incidence (β = 1.0-6.93) and one protective CpG in MAD1L1 (β = -0.65), of which 87% were located in TRG promoters. Methylation trajectories of 21 CpGs increased in cancer cases relative to controls; at 4 to 8 years prediagnosis/censoring, 17 CpGs were positively associated with cancer. Three CpGs were cross-sectionally associated with LTL. TRG methylation may be a mechanism through which LTL dynamics reflect cancer risk. Future research should confirm these findings and explore potential mechanisms underlying these findings, including telomere maintenance and DNA repair dysfunction. Cancer Prev Res; 11(8); 511-22. ©2018 AACR.
| S-EPMC6800137 | biostudies-literature
Project description:Neuroendocrine tumors (NETs) of the small intestine undergo large chromosomal and methylation changes. The objective of this study was to identify methylation differences in NETs and consider how the differentially methylated genes may impact patient survival. Genome-wide methylation and chromosomal copy number variation (CNV) of NETs from the small intestine and appendix were measured. Tumors were divided into three molecular subtypes according to CNV results: chromosome 18 loss (18LOH), Multiple CNV, and No CNV. Comparison of 18LOH tumors with MultiCNV and NoCNV tumors identified 901 differentially methylated genes. Genes from the G-protein coupled receptor (GPCR) pathways are statistically overrepresented in the differentially methylated genes. One of the highlighted genes from the GPCR pathway is somatostatin (SST), a clinical target for NETs. Patient survival based on low versus high methylation in all samples identified four significant genes (p < 0.05) OR2S2, SMILR, RNU6-653P, and AC010543.1. Within the 18LOH molecular subtype tumors, survival differences were identified in high versus low methylation of 24 genes. The most significant is TRHR (p < 0.01), a GPCR with multiple FDA-approved drugs. By separating NETs into different molecular subtypes based on chromosomal changes, we find that multiple GPCRs and their ligands appear to be regulated through methylation and correlated with survival. These results suggest opportunities for better treatment strategies for NETs based on molecular features.
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Project description:BackgroundDNA methylation is a key epigenetic regulator contributing to cancer development. To understand the role of DNA methylation in tumorigenesis, it is important to investigate and compare differential methylation (DM) patterns between normal and case samples across different cancer types. However, current pan-cancer analyses call DM separately for each cancer, which suffers from lower statistical power and fails to provide a comprehensive view for patterns across cancers.MethodsIn this work, we propose a rigorous statistical model, PanDM, to jointly characterize DM patterns across diverse cancer types. PanDM uses the hidden correlations in the combined dataset to improve statistical power through joint modeling. PanDM takes summary statistics from separate analyses as input and performs methylation site clustering, differential methylation detection, and pan-cancer pattern discovery. We demonstrate the favorable performance of PanDM using simulation data. We apply our model to 12 cancer methylome data collected from The Cancer Genome Atlas (TCGA) project. We further conduct ontology- and pathway-enrichment analyses to gain new biological insights into the pan-cancer DM patterns learned by PanDM.ResultsPanDM outperforms two types of separate analyses in the power of DM calling in the simulation study. Application of PanDM to TCGA data reveals 37 pan-cancer DM patterns in the 12 cancer methylomes, including both common and cancer-type-specific patterns. These 37 patterns are in turn used to group cancer types. Functional ontology and biological pathways enriched in the non-common patterns not only underpin the cancer-type-specific etiology and pathogenesis but also unveil the common environmental risk factors shared by multiple cancer types. Moreover, we also identify PanDM-specific DM CpG sites that the common strategy fails to detect.ConclusionsPanDM is a powerful tool that provides a systematic way to investigate aberrant methylation patterns across multiple cancer types. Results from real data analyses suggest a novel angle for us to understand the common and specific DM patterns in different cancers. Moreover, as PanDM works on the summary statistics for each cancer type, the same framework can in principle be applied to pan-cancer analyses of other functional genomic profiles. We implement PanDM as an R package, which is freely available at http://www.sta.cuhk.edu.hk/YWei/PanDM.html .
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Project description:DNA methylation alterations have already been linked to cancer, and their usefulness for therapy and diagnosis has encouraged research into the human epigenome. Several biomarker studies have focused on identifying cancer types individually, yet common cancer and multicancer markers are still underexplored. We used The Cancer Genome Atlas (TCGA) to investigate genome-wide methylation profiles of 14 different cancer types and developed a three-step computational approach to select candidate biomarker CpG sites. In total, 1991 pan-cancer and between 75 and 1803 cancer-specific differentially methylated CpG sites were discovered. Differentially methylated blocks and regions were also discovered for the first time on such a large scale. Through a three-step computational approach, a combination of four pan-cancer CpG markers was identified from these sites and externally validated (AUC = 0.90), maintaining comparable performance across tumor stages. Additionally, 20 tumor-specific CpG markers were identified and made up the final type-specific prediction model, which could accurately differentiate tumor types (AUC = 0.87-0.99). Our study highlights the power of the methylome as a rich source of cancer biomarkers, and the signatures we identified provide a new resource for understanding cancer mechanisms on the wider genomic scale with strong applicability in the context of new minimally invasive cancer detection assays.
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Project description:Calcitriol (1?, 25(OH)(2)-Vitamin D3) binds to the vitamin D receptor (VDR) and regulates differentiation of the normal mammary gland, and may therefore be useful in breast cancer treatment or prevention. Many breast cancer cells are, however, resistant to Calcitriol. In this study, we investigated the resistance mechanism and the role of epigenetic silencing of VDR by promoter hypermethylation. Bisulfite sequencing of the VDR promoter region revealed methylated CpG islands at -700 base pairs (bp) upstream and near the transcription start site. VDR CpG islands were demethylated by 5'deoxy-azacytidine treatment, and this was accompanied by a parallel increase in VDR mRNA levels in breast cancer cell lines. Quantitative methylation-specific PCR analyses confirmed hypermethylation of these CpG islands in primary tumors, and its absence in normal breast tissue. VDR transcripts detected in breast cancers were predominantly 5'-truncated, while normal breast tissue expressed full-length transcripts. Consistent with this observation, genes containing the VDR-responsive element (VDRE), such as cytochrome p450 hydroxylases, p21 or C/EBP were underexpressed in breast cancers compared to normal breast samples. Expression of the active longer transcripts of VDR was restored with 5'deoxy-Azacytidine (AZA) treatment, with a concurrent increase in expression of VDRE-containing genes. Thus, promoter methylation-mediated silencing of expression of the functional variants of VDR may contribute to reduced expression of downstream effectors of the VDR pathway and subsequent Calcitriol insensitivity in breast cancer. These data suggest that pharmacological reversal of VDR methylation may re-establish breast cancer cell susceptibility to differentiation therapy using Calcitriol.
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Project description:The nature and extent of immune cell infiltration into solid tumours are key determinants of therapeutic response. Here, using a DNA methylation-based approach to tumour cell fraction deconvolution, we report the integrated analysis of tumour composition and genomics across a wide spectrum of solid cancers. Initially studying head and neck squamous cell carcinoma, we identify two distinct tumour subgroups: 'immune hot' and 'immune cold', which display differing prognosis, mutation burden, cytokine signalling, cytolytic activity and oncogenic driver events. We demonstrate the existence of such tumour subgroups pan-cancer, link clonal-neoantigen burden to cytotoxic T-lymphocyte infiltration, and show that transcriptional signatures of hot tumours are selectively engaged in immunotherapy responders. We also find that treatment-naive hot tumours are markedly enriched for known immune-resistance genomic alterations, potentially explaining the heterogeneity of immunotherapy response and prognosis seen within this group. Finally, we define a catalogue of mediators of active antitumour immunity, deriving candidate biomarkers and potential targets for precision immunotherapy.
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Project description:Pan-cancer analysis can identify cell- and tissue-specific genomic loci and regions with underlying biological functions. Here we present an online curated DNA Methylation Annotation Knowledgebase, DMAK, which includes the pan-cancer analysis results for differentially-methylated loci and regions by the Reduced Representation Bisulfite Sequencing profiling technology. DMAK contains 3 modules of curated information and analysis results on 688,445 CpG sites across 19 cancer and embryonic stem cell lines from ENCODE, and further analysis of survival associations with clinical sources retrieved from TCGA. The knowledgebase covers all identified differentially-methylated CpG sites and regions of interest, further annotated genomic information, together with tumor suppressor genes information and calculated methylation level. DMAK provides meaningful clues for deriving functional association network and related clinical association results based on protein-coding genes, including tumor suppressor genes, identified from differentially methylated regions of interest. Thus DMAK constitutes a comprehensive reference source for the current epigenetic research and clinical study.
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Project description:BACKGROUND:In cancer, mutations of DNA methylation modification genes have crucial roles for epigenetic modifications genome-wide, which lead to the activation or suppression of important genes including tumor suppressor genes. Mutations on the epigenetic modifiers could affect the enzyme activity, which would result in the difference in genome-wide methylation profiles and, activation of downstream genes. Therefore, we investigated the effect of mutations on DNA methylation modification genes such as DNMT1, DNMT3A, MBD1, MBD4, TET1, TET2 and TET3 through a pan-cancer analysis. METHODS:First, we investigated the effect of mutations in DNA methylation modification genes on genome-wide methylation profiles. We collected 3,644 samples that have both of mRNA and methylation data from 12 major cancer types in The Cancer Genome Atlas (TCGA). The samples were divided into two groups according to the mutational signature. Differentially methylated regions (DMR) that overlapped with the promoter region were selected using minfi and differentially expressed genes (DEG) were identified using EBSeq. By integrating the DMR and DEG results, we constructed a comprehensive DNA methylome profiles on a pan-cancer scale. Second, we investigated the effect of DNA methylations in the promoter regions on downstream genes by comparing the two groups of samples in 11 cancer types. To investigate the effects of promoter methylation on downstream gene activations, we performed clustering analysis of DEGs. Among the DEGs, we selected highly correlated gene set that had differentially methylated promoter regions using graph based sub-network clustering methods. RESULTS:We chose an up-regulated DEGs cluster where had hypomethylated promoter in acute myeloid leukemia (LAML) and another down-regulated DEGs cluster where had hypermethylated promoter in colon adenocarcinoma (COAD). To rule out effects of gene regulation by transcription factor (TF), if differentially expressed TFs bound to the promoter of DEGs, that DEGs did not included to the gene set that effected by DNA methylation modifiers. Consequently, we identified 54 hypomethylated promoter DMR up-regulated DEGs in LAML and 45 hypermethylated promoter DMR down-regulated DEGs in COAD. CONCLUSIONS:Our study on DNA methylation modification genes in mutated vs. non-mutated groups could provide useful insight into the epigenetic regulation of DEGs in cancer.
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Project description:Many studies have demonstrated that epigenetic mechanisms are important in the regulation of gene expression during embryogenesis, gametogenesis, and other forms of tissue-specific gene regulation. We sought to explore the possible role of epigenetics, specifically DNA methylation, in the establishment and maintenance of cell type-restricted gene expression in the retina. To assess the relationship between DNA methylation status and expression level of retinal genes, bisulfite sequence analysis of the 1000 bp region around the transcription start sites (TSS) of representative rod and cone photoreceptor-specific genes and gene expression analysis were performed in the WERI and Y79 human retinoblastoma cell lines. Next, the homologous genes in mouse were bisulfite sequenced in the retina and in non-expressing tissues. Finally, bisulfite sequencing was performed on isolated photoreceptor and non-photoreceptor retinal cells isolated by laser capture microdissection. Differential methylation of rhodopsin (RHO), retinal binding protein 3 (RBP3, IRBP) cone opsin, short-wave-sensitive (OPN1SW), cone opsin, middle-wave-sensitive (OPN1MW), and cone opsin, long-wave-sensitive (OPN1LW) was found in the retinoblastoma cell lines that inversely correlated with gene expression levels. Similarly, we found tissue-specific hypomethylation of the promoter region of Rho and Rbp3 in mouse retina as compared to non-expressing tissues, and also observed hypomethylation of retinal-expressed microRNAs. The Rho and Rbp3 promoter regions were unmethylated in expressing photoreceptor cells and methylated in non-expressing, non-photoreceptor cells from the inner nuclear layer. A third regional hypomethylation pattern of photoreceptor-specific genes was seen in a subpopulation of non-expressing photoreceptors (Rho in cones from the Nrl -/- mouse and Opn1sw in rods). These results demonstrate that a number of photoreceptor-specific genes have cell-specific differential DNA methylation that correlates inversely with their expression level. Furthermore, these cell-specific patterns suggest that DNA methylation may play an important role in modulating photoreceptor gene expression in the developing mammalian retina.
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Project description:For cancer diagnosis, many DNA methylation markers have been identified. However, few studies have tried to identify DNA methylation markers to diagnose diverse cancer types simultaneously, i.e., pan-cancers. In this study, we tried to identify DNA methylation markers to differentiate cancer samples from the respective normal samples in pan-cancers. We collected whole genome methylation data of 27 cancer types containing 10,140 cancer samples and 3386 normal samples, and divided all samples into five data sets, including one training data set, one validation data set and three test data sets. We applied machine learning to identify DNA methylation markers, and specifically, we constructed diagnostic prediction models by deep learning. We identified two categories of markers: 12 CpG markers and 13 promoter markers. Three of 12 CpG markers and four of 13 promoter markers locate at cancer-related genes. With the CpG markers, our model achieved an average sensitivity and specificity on test data sets as 92.8% and 90.1%, respectively. For promoter markers, the average sensitivity and specificity on test data sets were 89.8% and 81.1%, respectively. Furthermore, in cell-free DNA methylation data of 163 prostate cancer samples, the CpG markers achieved the sensitivity as 100%, and the promoter markers achieved 92%. For both marker types, the specificity of normal whole blood was 100%. To conclude, we identified methylation markers to diagnose pan-cancers, which might be applied to liquid biopsy of cancers.
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