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 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) | 0.000 | 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) | 0.000 | 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".