Decoding methylation patterns in ovarian cancer using publicly available Next-Gen sequencing data

2018 ◽  
Vol 7 (1) ◽  
Author(s):  
Prashant Kumar ◽  
Utkarsh Raj ◽  
Imlimaong Aier ◽  
Pritish Kumar Varadwaj
Keyword(s):  
Ovarian Cancer ◽  
Sequencing Data ◽  
Next Gen Sequencing ◽  
Methylation Patterns

scholarly journals Identification of a Novel Oncogenic Fusion Gene SPON1-TRIM29 in Clinical Ovarian Cancer That Promotes Cell and Tumor Growth and Enhances Chemoresistance in A2780 Cells

2022 ◽  
Vol 23 (2) ◽  
pp. 689
Author(s):  
Saya Nagasawa ◽  
Kazuhiro Ikeda ◽  
Daisuke Shintani ◽  
Chiujung Yang ◽  
Satoru Takeda ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Rna Sequencing ◽  
Anticancer Drugs ◽  
Fusion Gene ◽  
Xenograft Model ◽  
Fusion Transcript ◽  
Serous Carcinoma ◽  
Fusion Genes ◽  
Sequencing Data ◽  
Chromosome 11

Gene structure alterations, such as chromosomal rearrangements that develop fusion genes, often contribute to tumorigenesis. It has been shown that the fusion genes identified in public RNA-sequencing datasets are mainly derived from intrachromosomal rearrangements. In this study, we explored fusion transcripts in clinical ovarian cancer specimens based on our RNA-sequencing data. We successfully identified an in-frame fusion transcript SPON1-TRIM29 in chromosome 11 from a recurrent tumor specimen of high-grade serous carcinoma (HGSC), which was not detected in the corresponding primary carcinoma, and validated the expression of the identical fusion transcript in another tumor from a distinct HGSC patient. Ovarian cancer A2780 cells stably expressing SPON1-TRIM29 exhibited an increase in cell growth, whereas a decrease in apoptosis was observed, even in the presence of anticancer drugs. The siRNA-mediated silencing of SPON1-TRIM29 fusion transcript substantially impaired the enhanced growth of A2780 cells expressing the chimeric gene treated with anticancer drugs. Moreover, a subcutaneous xenograft model using athymic mice indicated that SPON1-TRIM29-expressing A2780 cells rapidly generated tumors in vivo compared to control cells, whose growth was significantly repressed by the fusion-specific siRNA administration. Overall, the SPON1-TRIM29 fusion gene could be involved in carcinogenesis and chemotherapy resistance in ovarian cancer, and offers potential use as a diagnostic and therapeutic target for the disease with the fusion transcript.

scholarly journals Identification of novel mutations of ovarian cancer-related genes from RNA-sequencing data for Japanese epithelial ovarian cancer patients

2020 ◽  
Vol 67 (2) ◽  
pp. 219-229
Author(s):  
Saya Nagasawa ◽  
Kazuhiro Ikeda ◽  
Kuniko Horie-Inoue ◽  
Sho Sato ◽  
Satoru Takeda ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Cancer Patients ◽  
Rna Sequencing ◽  
Sequencing Data ◽  
Novel Mutations

scholarly journals HighSSR: high-throughput SSR characterization and locus development from next-gen sequencing data

2012 ◽  
Vol 28 (21) ◽  
pp. 2797-2803 ◽  
Author(s):  
Alexander Churbanov ◽  
Rachael Ryan ◽  
Nabeeh Hasan ◽  
Donovan Bailey ◽  
Haofeng Chen ◽  
...  
Keyword(s):  
High Throughput ◽  
Sequencing Data ◽  
Next Gen Sequencing

Profiling of Aberrant DNA Methylation In AML Reveals Subclasses of CpG Islands with Epigenetic or Genetic Association

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 2498-2498
Author(s):  
Claudia Gebhard ◽  
Mohammed Sadeh ◽  
Dagmar Glatz ◽  
Lucia Schwarzfischer ◽  
Rainer Spang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
De Novo ◽  
Conflicts Of Interest ◽  
Cpg Island ◽  
Cpg Islands ◽  
Sequencing Data ◽  
Cpg Island Methylator Phenotype ◽  
Chip Sequencing ◽  
Aberrant Dna Methylation ◽  
Methylation Patterns

Abstract Abstract 2498 CpG islands show frequent and often disease-specific epigenetic alterations during malignant transformation, however, the underlying mechanisms are poorly understood. We used methyl-CpG immunoprecipitation (MCIp) to generate comparative DNA methylation profiles of 30 patients with acute myeloid leukemia for human CpG islands across the genome. DNA methylation profiles across 23.000 CpG islands revealed highly heterogeneous methylation patterns in AML with over 6000 CpG islands showing aberrant de novo methylation in AML. Based on these profiles we selected a subset of 380 CpG islands (covering 15.000 individual CpGs) for detailed fine-mapping analyses of aberrant DNA methylation in 185 patients with AML (50% normal karyotype). We found that a proportion of patients (5/185) displayed a concerted hypermethylation at almost all studied loci, representing the rare CpG island methylator phenotype (CIMP) in AML. Meta analysis of methylation profiling and published ChIP sequencing data separated CpG islands in two groups. A highly correlated subgroup of CpG island regions was strongly associated with histone H3 lysine 27 trimethylation in human hematopoietic progenitor cells, suggesting that disease-related de novo DNA methylation at these CpG islands is linked with polycomb group protein (PcG)-mediated repression. The group of mainly non-PcG target CpG islands showed heterogeneous methylation patterns across patients and unsupervised hierarchical clustering revealed a correlation of methylation profiles with genetic disease markers, including oncofusion proteins as well as CEBPA- and NPM1-mutations. Our study suggests that both epigenetic as well as genetic aberrations may underlay AML-related changes in CpG island DNA methylation states. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Identification of Potential Prognostic Competing Triplets in High-Grade Serous Ovarian Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Jian Zhao ◽  
Xiaofeng Song ◽  
Tianyi Xu ◽  
Qichang Yang ◽  
Jingjing Liu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
High Throughput Sequencing ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Sequencing Data ◽  
P53 Signaling ◽  
Oocyte Meiosis ◽  
The Difference ◽  
Division Cell ◽  
The Impact

Increasing lncRNA-associated competing triplets were found to play important roles in cancers. With the accumulation of high-throughput sequencing data in public databases, the size of available tumor samples is becoming larger and larger, which introduces new challenges to identify competing triplets. Here, we developed a novel method, called LncMiM, to detect the lncRNA–miRNA–mRNA competing triplets in ovarian cancer with tumor samples from the TCGA database. In LncMiM, non-linear correlation analysis is used to cover the problem of weak correlations between miRNA–target pairs, which is mainly due to the difference in the magnitude of the expression level. In addition, besides the miRNA, the impact of lncRNA and mRNA on the interactions in triplets is also considered to improve the identification sensitivity of LncMiM without reducing its accuracy. By using LncMiM, a total of 847 lncRNA-associated competing triplets were found. All the competing triplets form a miRNA–lncRNA pair centered regulatory network, in which ZFAS1, SNHG29, GAS5, AC112491.1, and AC099850.4 are the top five lncRNAs with most connections. The results of biological process and KEGG pathway enrichment analysis indicates that the competing triplets are mainly associated with cell division, cell proliferation, cell cycle, oocyte meiosis, oxidative phosphorylation, ribosome, and p53 signaling pathway. Through survival analysis, 107 potential prognostic biomarkers are found in the competing triplets, including FGD5-AS1, HCP5, HMGN4, TACC3, and so on. LncMiM is available at https://github.com/xiaofengsong/LncMiM.

scholarly journals Widespread natural variation of DNA methylation within angiosperms

2016 ◽  
Author(s):  
Chad E. Niederhuth ◽  
Adam J. Bewick ◽  
Lexiang Ji ◽  
Magdy S. Alabady ◽  
Kyung Do Kim ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Life Histories ◽  
Natural Variation ◽  
Gene Body ◽  
Sequencing Data ◽  
Gene Body Methylation ◽  
Genome Bisulfite Sequencing ◽  
Bisulfite Sequencing Data ◽  
Angiosperm Species ◽  
Methylation Patterns

AbstractTo understand the variation in genomic patterning of DNA methylation we compared methylomes of 34 diverse angiosperm species. By analyzing whole-genome bisulfite sequencing data in a phylogenetic context it becomes clear that there is extensive variation throughout angiosperms in gene body DNA methylation, euchromatic silencing of transposons and repeats, as well as silencing of heterochromatic transposons. The Brassicaceae have reduced CHG methylation levels and also reduced or loss of CG gene body methylation. The Poaceae are characterized by a lack or reduction of heterochromatic CHH methylation and enrichment of CHH methylation in genic regions. Reduced CHH methylation levels are found in clonally propagated species, suggesting that these methods of propagation may alter the epigenomic landscape over time. These results show that DNA methylation patterns are broadly a reflection of the evolutionary and life histories of plant species.

scholarly journals IMAGE: high-powered detection of genetic effects on DNA methylation using integrated methylation QTL mapping and allele-specific analysis

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Yue Fan ◽  
Tauras P. Vilgalys ◽  
Shiquan Sun ◽  
Qinke Peng ◽  
Jenny Tung ◽  
...  
Keyword(s):  
Bisulfite Sequencing ◽  
Sequencing Data ◽  
Specific Analysis ◽  
Sequencing Studies ◽  
Bisulfite Sequencing Data ◽  
Allele Specific ◽  
Trait Locus ◽  
Allele Specific Methylation ◽  
Methylation Patterns ◽  
Trait Associations

Abstract Identifying genetic variants that are associated with methylation variation—an analysis commonly referred to as methylation quantitative trait locus (mQTL) mapping—is important for understanding the epigenetic mechanisms underlying genotype-trait associations. Here, we develop a statistical method, IMAGE, for mQTL mapping in sequencing-based methylation studies. IMAGE properly accounts for the count nature of bisulfite sequencing data and incorporates allele-specific methylation patterns from heterozygous individuals to enable more powerful mQTL discovery. We compare IMAGE with existing approaches through extensive simulation. We also apply IMAGE to analyze two bisulfite sequencing studies, in which IMAGE identifies more mQTL than existing approaches.

scholarly journals HMM-Fisher: identifying differential methylation using a hidden Markov model and Fisher’s exact test

2016 ◽  
Vol 15 (1) ◽  
Author(s):  
Shuying Sun ◽  
Xiaoqing Yu
Keyword(s):  
Dna Methylation ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Differential Methylation ◽  
Sequencing Data ◽  
Exact Test ◽  
Fisher's Exact Test ◽  
Fisher’S Exact Test ◽  
Methylation Patterns

AbstractDNA methylation is an epigenetic event that plays an important role in regulating gene expression. It is important to study DNA methylation, especially differential methylation patterns between two groups of samples (e.g. patients vs. normal individuals). With next generation sequencing technologies, it is now possible to identify differential methylation patterns by considering methylation at the single CG site level in an entire genome. However, it is challenging to analyze large and complex NGS data. In order to address this difficult question, we have developed a new statistical method using a hidden Markov model and Fisher’s exact test (HMM-Fisher) to identify differentially methylated cytosines and regions. We first use a hidden Markov chain to model the methylation signals to infer the methylation state as Not methylated (N), Partly methylated (P), and Fully methylated (F) for each individual sample. We then use Fisher’s exact test to identify differentially methylated CG sites. We show the HMM-Fisher method and compare it with commonly cited methods using both simulated data and real sequencing data. The results show that HMM-Fisher outperforms the current available methods to which we have compared. HMM-Fisher is efficient and robust in identifying heterogeneous DM regions.

Sign in / Sign up

Export Citation Format

Share Document