Abstract 158: The landscape of DNA hypomethylation in liver cancer, its correlation with other cancers, and its potential role in cancer
Bibliographic record
Abstract
Abstract DNA demethylation, although a common hallmark of cancer, has been relatively ignored. We used hepatic cellular carcinoma, which is one of the most common human cancers, as a model to delineate the landscape of hypomethylation in cancer, unravel the driving forces of hypomethylation and determine its potential role in cancer progression, metastasis and invasion. Using liver cancer cell lines as a model system, we tested whether the demethylation observed in liver cancer is driven by MBD2, a protein previously implicated in demethylation, which we found to be overexpressed in liver cancer. We used genome-wide promoter arrays and methylated DNA immunoprecipitation to map the sites in the genome that lost methyl marks in liver cancer patients. Affymetrix arrays were used for gene expression studies. The array data were validated by pyrosequencing and QPCR. For functional analyses, we used siRNA transfection, soft agar and invasion assays. We discovered that about 3700 transcript-encoding genes were demethylated in liver cancer samples compared to adjacent normal tissue. At least 350 of these 3700 genes were significantly induced in tumors. In the group of demethylated and induced genes, we identified a distinct subgroup of over 100 genes with CpG dense promoters that were highly methylated in normal liver, but demethylated in the tumors. Gene ontology analyses revealed that these genes are involved in cell growth, cell adhesion, signal transduction and invasion. Examination of the Ensemble methylome data suggests that these promoters are differentially methylated in a tissue specific manner but are always methylated in liver. All of the demethylated genes in liver cancer were found to be demethylated in other cancers suggesting that the demethylation of these genes is a basic requirement in cancer. The experiments in HepG2 and SkHep1 cells showed that knock-down of MBD2 with siRNA results in suppression of the majority of the hypomethylated genes and in a relevant decrease in cancer cell growth and invasion. Furthermore, the down-regulation of these genes was accompanied by an increase in promoter methylation and a decrease in MBD2 binding. It suggests that MBD2 is required for demethylation and up-regulation of these genes, which appear to be putative targets for MBD2. Our results demonstrated that DNA hypomethylation drives the coordinated expression of several gene circuitries involved in cancer growth, survival and metastasis. We showed that MBD2 is involved in driving this processes by affecting methylation and expression of important players in liver cancer growth and metastasis. Our studies established for the first time the rules governing hypomethylation in liver cancer and defined the potential functional role of hypomethylation. This study was supported by a grant from the MDEEI program of the government of Quebec and the National Cancer Institute of Canada to MS and MOST to ZGH Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 158.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 0.000 |
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".