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, was discovered.Due to significant expression of this gene in the poorly metastatic subpopulation and lack of expression in the highly metastatic subpopulation, MS-1 may have involvement in metastasis suppression.Several breast cancer metastasis suppressor genes have been identified on the basis that they are down-regulated during the progression of metastasis.Epigenetic mechanisms, such as DNA methylation, account for loss of expression in several of these genes.Hypermethylation of CpG islands within gene promoters results in deacetylation of histone proteins and produces a compact chromatin structure that is unfavourable for transcription.A CpG island spans the 5' untranslated region, exon 1 and part of intron 1 of the MS-1 gene.Our data reveal Several changes have occurred in my life throughout the duration of this research endeavor.My mother Merrilee and her courageous struggle with cancer gave me the strength to fight my own battles and the realization of what truly matters.The new-found love between my father Jerald and step-mother Shelley inspired me to believe that your dreams will come true if you face life with open arms.I also dedicate this work to my long-time friend Danielle, who is the sister I never had, and my brother Jeremy, who is so much more than that.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".