The Role of Apoptosis in Tumor Progression and Metastasis
Bibliographic record
Abstract
Metastasis, the process by which cancer spreads from a primary to a secondary site, is responsible for the majority of cancer related deaths. Yet despite the detrimental effects of metastasis, it is an extremely inefficient process by which very few of the cells that leave the primary tumor give rise to secondary tumors. Metastasis can be considered as a series of sequential steps that begins with a cell leaving a primary tumor, and concludes with the formation of a metastatic tumor in a distant site. During the process of metastasis cells are subjected to various apoptotic stimuli. Thus, in addition to genetic changes that promote unregulated proliferation, successful metastatic cells must have a decreased sensitivity to apoptotic stimuli. As many cancer cells exhibit aberrations in the level and function of key apoptotic regulators, exploiting these alterations to induce tumor cell apoptosis offers a promising therapeutic target. This review will examine the apoptotic regulators that are often aberrantly expressed in metastatic cells; the role that these regulators may play in metastasis; the steps of metastasis and their susceptibility to apoptosis; and finally, current and future cancer prognostics and treatment targets based on apoptotic regulators.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".