Phenotypic plasticity in prostate cancer: role of intrinsically disordered proteins
Bibliographic record
Abstract
A striking characteristic of cancer cells is their remarkable phenotypic plasticity, which is the ability to switch states or phenotypes in response to environmental fluctuations. Phenotypic changes such as a partial or complete epithelial to mesenchymal transition (EMT) that play important roles in their survival and proliferation, and development of resistance to therapeutic treatments, are widely believed to arise due to somatic mutations in the genome. However, there is a growing concern that such a deterministic view is not entirely consistent with multiple lines of evidence, which indicate that stochasticity may also play an important role in driving phenotypic plasticity. Here, we discuss how stochasticity in protein interaction networks (PINs) may play a key role in determining phenotypic plasticity in prostate cancer (PCa). Specifically, we point out that the key players driving transitions among different phenotypes (epithelial, mesenchymal, and hybrid epithelial/mesenchymal), including ZEB1, SNAI1, OVOL1, and OVOL2, are intrinsically disordered proteins (IDPs) and discuss how plasticity at the molecular level may contribute to stochasticity in phenotypic switching by rewiring PINs. We conclude by suggesting that targeting IDPs implicated in EMT in PCa may be a new strategy to gain additional insights and develop novel treatments for this disease, which is the most common form of cancer in adult men.
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".