Cell adhesion molecules and their relation to (cancer) cell stemness
Bibliographic record
Abstract
Despite decades of search for anticancer drugs targeting solid tumors, this group of diseases remains largely incurable, especially if in advanced, metastatic stage. In this review, we draw comparison between reprogramming and carcinogenesis, as well as between stem cells (SCs) and cancer stem cells (CSCs), focusing on changing garniture of adhesion molecules. Furthermore, we elaborate on the role of adhesion molecules in the regulation of (cancer) SCs division (symmetric or asymmetric), and in evolving interactions between CSCs and extracellular matrix. Among other aspects, we analyze the role and changes of expression of key adhesion molecules as cancer progresses and metastases develop. Here, the role of cadherins, integrins, as well as selected transcription factors like Twist and Snail is highlighted, not only in the regulation of epithelial-to-mesenchymal transition but also in the avoidance of anoikis. Finally, we briefly discuss recent developments and new strategies targeting CSCs, which focus on adhesion molecules or targeting tumor vasculature.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".