Microenvironmental Regulation of Cancer Stem Cell Phenotypes
Bibliographic record
Abstract
Cancer is a complex set of diseases, driven by genomic instability overlaid with epigenetic modifications. Two prevailing concepts, the stochastic theory and the hierarchical theory, are traditionally used to understand tumor progression. These seemingly contradictory theories can be reconciled with the concept of cellular plasticity, such that certain genetic mutations enable epigenetic alterations in cell fate. A growing body of evidence suggests that cancer cells co-opt embryonic stem cell-associated regulatory networks in order to sustain tumor cell plasticity concomitant with growth and progression. The expression of these stem cell associated factors is regulated by dynamic niches, characterized by cellderived proteins as well as biophysical features such low oxygen tensions. In this review we describe specific embryo-associated proteins such as NODAL, NOTCH, and canonical WNT, which cooperate to maintain stem cell phenotypes in cancer. We also illustrate how biophysical factors, in particular oxygen, can orchestrate plasticity by modulating the expression of stem cell-associated proteins. As the microenvironment is known to play a key role in cellular regulation, it is essential to understand its role in cancer progression in order to improve and create new therapies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".