Abstract 96: The functional role of DNA hypomethylation in liver cancer
Bibliographic record
Abstract
Abstract DNA hypomethylation, a process of losing methyl marks, may play an important role in cancer, especially through activating genes that promote cancer metastasis, the deadly aspect of cancer. We previously delineated using whole genome promoter methylation microarrays the landscape of hypomethylation in hepatocellular carcinoma which is one of the most common cancers worldwide. Our studies revealed that 50% of differentially methylated promoters in tumors are hypomethylated compared to adjacent normal tissue. In the present studies, using liver cancer cell lines as a model system, we evaluated the importance of the identified hypomethylated genes in liver cancer development and progression. We tested whether the hypomethylation observed in liver cancer is driven by MBD2, a protein reported previously to be implicated in DNA demethylation, which we found to be overexpressed in liver cancer patients. Following siRNA MBD2 depletion in HepG2 hepatocellular carcinoma cell line, we used methylated DNA immunoprecipitation, chromatin immunoprecipitation (ChIP) and 60K custom microarrays covering 12K bp upstream and 10K bp downstream of transcription start site to determine methylation and MBD2 binding in 300 top genes hypomethylated and/or induced in liver cancer patients and/or HepG2 cells. The array data were validated by quantitative ChIP and/or pyrosequencing. For functional analyses, we used siRNA transfection, soft agar and invasion assays. We discovered MBD2 binding peaks in 172 genes out of 300, mainly within their promoter regions. Using bioinformatic tools, we identified MBD2 binding motif and established putative transcription factors recognizing this sequence which may play a key role in recruiting MBD2 to target genes. 41% of those genes were hypermethylated in HepG2 cells after MBD2 depletion in a fragment corresponding to MBD2 binding site. In addition, expression analysis for some of the genes showed attenuation. In HepG2 cells, we depleted expression of several of the genes hypomethylated in cancer and regulated by MBD2 using siRNA and demonstrated a decrease in cell growth and invasive capacities. Our results establish a role for MBD2 in coordinating inhibition of a panel of genes involved in cancer growth and metastasis. This study was supported by a grant from the MDEIE program of the government of Quebec and the National Cancer Institute of Canada to MS. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 96. doi:10.1158/1538-7445.AM2011-96
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".