Developmental pathways associated with cancer metastasis: Notch, Wnt, and Hedgehog
Bibliographic record
Abstract
Master developmental pathways, such as Notch, Wnt, and Hedgehog, are signaling systems that control proliferation, cell death, motility, migration, and stemness. These systems are not only commonly activated in many solid tumors, where they drive or contribute to cancer initiation, but also in primary and metastatic tumor development. The reactivation of developmental pathways in cancer stroma favors the development of cancer stem cells and allows their maintenance, indicating these signaling pathways as particularly attractive targets for efficient anticancer therapies, especially in advanced primary tumors and metastatic cancers. Metastasis is the worst feature of cancer development. This feature results from a cascade of events emerging from the hijacking of epithelial-mesenchymal transition, angiogenesis, migration, and invasion by transforming cells and is associated with poor survival, drug resistance, and tumor relapse. In the present review, we summarize and discuss experimental data suggesting pivotal roles for developmental pathways in cancer development and metastasis, considering the therapeutic potential. Emerging targeted antimetastatic therapies based on Notch, Wnt, and Hedgehog pathways are also discussed.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".