Ontology highlight
ABSTRACT:
PROVIDER: PRJNA784747 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR17071349_1.fastq.gz | Fastqsanger.gz | |||
| SRR17071349_2.fastq.gz | Fastqsanger.gz | |||
| SRR17071350_1.fastq.gz | Fastqsanger.gz | |||
| SRR17071350_2.fastq.gz | Fastqsanger.gz | |||
| SRR17071351_1.fastq.gz | Fastqsanger.gz |
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Project description:Transcriptomic changes in human liver cancer cell lines caused by the demethylating drug zebularine. Epigenomic changes such as aberrant hypermethylation and subsequent atypical gene silencing are characteristic features of human cancer. Here, we report a comprehensive characterization of epigenomic modulation caused by zebularine, an effective DNA methylation inhibitor, in human liver cancer. Using transcriptomic and epigenomic profiling, we identified a zebularine signature that classified liver cancer cell lines into two major subtypes with different drug-responses. In drug-sensitive cell lines, zebularine caused inhibition of proliferation coupled with increased apoptosis, whereas drug-resistant cell lines were associated with upregulation of oncogenic networks (e.g. E2F1, MYC, and TNF) driving liver cancer growth in vitro and in mice. Assessment of zebularine-based therapy in xenograft mouse models demonstrated potent therapeutic effects against tumors established from zebularine-sensitive but not zebularine-resistant liver cancer cells leading to increased survival and decreased pulmonary metastasis. Integration of zebularine gene expression and demethylation response signatures differentiated patients with HCC according to their survival and disease recurrence and identified a subclass of patients within the poor survivors likely to benefit from therapeutic agents that target the cancer epigenome. Each cell line was mock treated or treated with 100uM and 200uM zebularine for 7 days, respectively *** This Series represents the gene expression component of the study.
2011-07-30 | E-GEOD-23991 | biostudies-arrayexpress
Project description:Transcriptomic changes in human liver cancer cell lines caused by the demethylating drug zebularine. Epigenomic changes such as aberrant hypermethylation and subsequent atypical gene silencing are characteristic features of human cancer. Here, we report a comprehensive characterization of epigenomic modulation caused by zebularine, an effective DNA methylation inhibitor, in human liver cancer. Using transcriptomic and epigenomic profiling, we identified a zebularine signature that classified liver cancer cell lines into two major subtypes with different drug-responses. In drug-sensitive cell lines, zebularine caused inhibition of proliferation coupled with increased apoptosis, whereas drug-resistant cell lines were associated with upregulation of oncogenic networks (e.g. E2F1, MYC, and TNF) driving liver cancer growth in vitro and in mice. Assessment of zebularine-based therapy in xenograft mouse models demonstrated potent therapeutic effects against tumors established from zebularine-sensitive but not zebularine-resistant liver cancer cells leading to increased survival and decreased pulmonary metastasis. Integration of zebularine gene expression and demethylation response signatures differentiated patients with HCC according to their survival and disease recurrence and identified a subclass of patients within the poor survivors likely to benefit from therapeutic agents that target the cancer epigenome.
2011-07-31 | GSE23991 | GEO
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Project description:Bordel2018 - GSMM for Human Metabolic
Reactions (HMR database)
This model is described in the article:
Constraint based modeling of
metabolism allows finding metabolic cancer hallmarks and
identifying personalized therapeutic windows
Sergio Bordel
Oncotarget. 2018; 9:19716-19729
Abstract:
In order to choose optimal personalized anticancer
treatments, transcriptomic data should be analyzed within the
frame of biological networks. The best known human biological
network (in terms of the interactions between its different
components) is metabolism. Cancer cells have been known to have
specific metabolic features for a long time and currently there
is a growing interest in characterizing new cancer specific
metabolic hallmarks. In this article it is presented a method
to find personalized therapeutic windows using RNA-seq data and
Genome Scale Metabolic Models. This method is implemented in
the python library, pyTARG. Our predictions showed that the
most anticancer selective (affecting 27 out of 34 considered
cancer cell lines and only 1 out of 6 healthy mesenchymal stem
cell lines) single metabolic reactions are those involved in
cholesterol biosynthesis. Excluding cholesterol biosynthesis,
all the considered cell lines can be selectively affected by
targeting different combinations (from 1 to 5 reactions) of
only 18 metabolic reactions, which suggests that a small subset
of drugs or siRNAs combined in patient specific manners could
be at the core of metabolism based personalized treatments.
This model is hosted on
BioModels Database
and identified by:
MODEL1707250000.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
| MODEL1707250000 | BioModels
Project description:Metastasis is the major cause of cancer-related death in patients with colorectal cancer. Although inducible nitric oxide synthase (iNOS) is a crucial regulator of cancer development and progression, its roles in epithelial-mesenchymal transition (EMT) and the pathogenesis of metastatic colorectal cancer have not been fully investigated. Primary colorectal cancer and liver metastatic tissue specimens were analyzed showing 90% of liver metastatic colorectal cancer with reduced expressions of iNOS compared with 6% of primary colorectal cancer. The Cancer Genome Atlas database analyses via cBioPortal reveal that mRNA expression of iNOS negatively correlated with selected EMT markers in colorectal cancer in a cancer type-dependent manner. The transcriptomic profiling (RNA sequencing data) indicates that iNOS knockdown in SW480 colorectal cancer cells induced an EMT program with upregulated expression of selected stem-cell markers. iNOS knockdown did not alter E-cadherin mRNA expression but re-localized it from membrane to cytoplasm through iNOS-GATA4-Crb2-E-cadherin pathway. iNOS knockdown induced a change in cell morphology, and promoted cell invasion and migration in vitro, and metastasis in vivo.
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