The Idea and Evidence for the Tumor Stemness Switch
Bibliographic record
Abstract
The maintenance of stemness of normal stem cell is a complex process, where transcription factors like Oct-4, Bmi-1, and signaling pathways such as Wnt/β-catenin play important roles. This molecular set of mechanisms not only expands the population (self-renewal) but also keeps stem cells in a state of “de-differentiation.” Thus, stemness and differentiation are mutually exclusive and tightly regulated, where the idea of variation of stemness over time has not been incorporated. However, unlike normal stem cell stemness, tumor stemness may not be tightly regulated, where complexity of tumor microenvironment, especially hypoxic stress may allow for variation in stemness. In this review, we discuss the stem cell model of tumor growth and the emerging concept of tumor stemness. We also discuss our findings on the expansion of tumor stem cell–like side-population cells following hypoxic and drug-induced stress. We then propose a model of stemness switch, where quiescent TSCs (tumor stem cells) switch to a state of active and self-renewing TSC following stress.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".