Skin Cancers and the Contribution of Rho GTPase Signaling Networks to Their Progression
Bibliographic record
Abstract
Skin cancers are the most common cancers worldwide. Among them, melanoma, basal cell carcinoma of the skin and cutaneous squamous cell carcinoma are the three major subtypes. These cancers are characterized by different genetic perturbations even though they are similarly caused by a lifelong exposure to the sun. The main oncogenic drivers of skin cancer initiation have been known for a while, yet it remains unclear what are the molecular events that mediate their oncogenic functions and that contribute to their progression. Moreover, patients with aggressive skin cancers have been known to develop resistance to currently available treatment, which is urging us to identify new therapeutic opportunities based on a better understanding of skin cancer biology. More recently, the contribution of cytoskeletal dynamics and Rho GTPase signaling networks to the progression of skin cancers has been highlighted by several studies. In this review, we underline the various perturbations in the activity and regulation of Rho GTPase network components that contribute to skin cancer development, and we explore the emerging therapeutic opportunities that are surfacing from these studies.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".