Key regulators of apoptosis execution as biomarker candidates in melanoma
Bibliographic record
Abstract
Resistance to apoptosis is frequently detected in malignant melanoma, a skin cancer with rapidly growing incidence rates. Apoptosis resistance may develop with disease progression and may be associated with the poor responsiveness of metastatic melanoma to apoptosis-inducing treatments, such as genotoxic chemotherapy and radiotherapy. Likewise, the efficacy of novel treatment options (targeted kinase inhibitors and immunotherapeutics) that indirectly lead to cell death may depend on the susceptibility of melanoma to apoptosis. At its core, apoptosis execution is regulated by the interplay between a comparatively small number of pro- and anti-apoptotic proteins, and consequently numerous studies have investigated the potential of these players as biomarker candidates. Here, we provide a comprehensive overview of biomarker discovery studies focusing on key regulators of apoptosis execution, critically review the findings of these studies, and outline strategies that address current limitations and challenges in exploiting regulators of apoptosis execution as prognostic or predictive biomarkers in melanoma.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 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 | 0.002 |
| 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".