Role of nodal signaling and the microenvironment underlying melanoma plasticity
Bibliographic record
Abstract
The incidence of melanoma has increased dramatically over the last 50 yr, and although melanoma accounts for only 10% of all skin cancers, it is responsible for over 80% of skin cancer deaths. Recent studies have uncovered critical molecular events underlying melanocytic transformation and melanomagenesis. Among these noteworthy observations are the acquisition of stem cell-associated proteins, such as the Notch receptors and Nodal, which have also been implicated in melanoma progression. For example, we have demonstrated that Nodal expression is limited to invasive vertical growth phase and metastatic melanoma lesions, and that inhibition of Nodal signaling promotes the reversion of metastatic melanoma cells toward a more differentiated, less invasive non-tumorigenic phenotype. In addition, molecular cross-talk exists between the Notch and Nodal signaling pathways. Interestingly, the acquisition of stem cell-associated plasticity is often acquired via epigenetic mechanisms, and is therefore receptive to reprogramming in response to embryonic microenvironments. Here, we review the concept of melanoma plasticity, with an emphasis on the emerging role of Nodal as a regulator of melanoma tumorigenesis and progression, and present findings related to epigenetic reprogramming.
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 | 0.001 | 0.001 |
| 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".