Abstract 4103: Hepatocellular carcinoma-specific isoforms of methylated DNA-binding protein 2 and their therapeutic and diagnostic potential
Bibliographic record
Abstract
Abstract Liver cancer is the fifth most common cancer in the world. It is particularly prevalent in developing countries in Asia and Africa. In the US alone, it is estimated that 26,190 adults will be diagnosed with primary liver cancer this year, and 19,590 people will die of the disease. Hepatocellular carcinoma has no efficient cure, has a 5-year survival rate of 14% and is highly metastatic. In the last couple of decades, it has been established that many types of cancer involve major changes in the DNA methylation machinery and in turn, widespread yet highly specific changes in cytosine methylation. DNA methylation is a chemical modification on the DNA associated with gene inactivation, and in contrast to genetic mutation, is dynamic and reversible, making it a preferable target for therapeutics. Methylated-DNA Binding Proteins (MBD) have been shown to play a critical role in the progression and metastasis of cancers in general and specifically in hepatocellular carcinoma. We have identified two isoforms of the MBD2 protein (hcMBD2) that have not been previously reported in any somatic cells: these isoforms are totally silenced in normal liver cells and active in hepatocellular carcinoma. Specific inhibition of these isoforms of MBD2 in hepatocellular carcinoma (HePG2 cell line) has a dramatic effect on cell growth and cellular transformation, but no effect in normal cells, where the isoforms are not expressed. Knockdown experiments show dramatic reduction in cell invasiveness, reduced cell growth rate and increased cell death in HepG2 cells, while primary liver hepatocytes remain largely unaffected. A survey of several types of cancers and their surrounding tissue of origin shows the expression of hcMBD2 in several other types of cancer, such as breast cancer. These results position hcMBD as a promising candidate for cancer diagnostics and therapeutics. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4103. doi:1538-7445.AM2012-4103
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".