Epigenetic Carcinogenesis and Malignancy: The Significance of Migratory Potential
Bibliographic record
Abstract
The essential feature of the malignant phenotype is the ability of the affected cells to transgress the normal territorial limits that delineate tissue boundaries. This brief review outlines the process underlying the acquisition of this property based on evidence consistent with the notion that the mechanism of carcinogenesis involves defective epigenetic transmission. The resulting failure of vertical transmission of the differentiated pattern of gene expression in proliferative stem cells which leads to faulty copying of the epigenetic information at each cell division generates widespread genetic abnormalities; a process which is essentially equivalent to a greatly elevated mutation rate. The outcome from the point of view of the affected cell and its progeny would be expected to interfere negatively with the proliferation rate. To some extent this proliferative disadvantage is offset by the altruistic factor necessary for permitting coexistence of different cell types in multicellular organisms but the crucial property which renders certain cells malignant is their ability to transgress tissue boundaries. Affected cells possessing this malignant phenotype are able to penetrate this barrier and enter microenvironmental zones where they are able to proliferate without competition. The competitive growth process is outlined using a simple microenvironmental model.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".