EPIGENETIC REGULATION OF A GENE, MS-1, IN CELLS OF DIFFERENT METASTATIC POTENTIAL
Bibliographic record
Abstract
Breast cancer is the most common malignancy and a major cause of cancerrelated death among Canadian women.Although treatment of primary breast tumours is highly successful through surgery, metastatic breast cancer is difficult to treat.Cancer progression and metastasis require the accumulation of numerous genetic and epigenetic alterations.Normal cells that acquire such alterations can transform into cancer cells, resulting in primary tumour formation.Primary tumours are a heterogeneous population, containing cells of various metastatic potentials.Cells that acquire a high potential for metastasis can spread to secondary locations.Our model system consists of two subpopulations, with different metastatic potential, derived from the same rat mammary adenocarcinoma.Using this model, a differentially expressed novel gene, termed MS-1, aberrant methylation patterns of this CpG island between the cell lines of different metastatic potential in our model.Also, MS-1 expression was partially induced by both DNA methylation and histone deacetylation inhibitors.Following a screen of several cancer cell lines of varying metastatic potential, it appears that the presence of DNA methylation in the CpG island of MS-1 correlates with the lack of MS-1 expression.Therefore, these results suggest that MS-1 may be silenced in cells of high metastatic potential through epigenetic mechanisms.
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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) | 0.001 | 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".