ERKs in Cancer: Friends or Foes?
Bibliographic record
Abstract
The extracellular signal-regulated kinase ERK1 and ERK2 (ERK1/2) cascade regulates a variety of cellular processes by phosphorylating multiple target proteins. The outcome of its activation ranges from stimulation of cell survival and proliferation to triggering tumor suppressor responses such as cell differentiation, cell senescence, and apoptosis. This pathway is intimately linked to cancer as several of its upstream activators are frequently mutated in human disease and are shown to accelerate tumorigenesis when engineered in the mouse genome. However, measurement of activated ERKs in human cancers or mouse models does not always support a role in tumorigenesis, and data consistent with a role in tumor suppression have been reported as well. The intensity of ERK signaling, negative feedback loops that regulate the pathway, and cross-talks with other signaling pathways, seem to be of primary importance in determining the final cellular outcome. Cell senescence, a putative tumor-suppression mechanism, depends on high-intensity ERK signals that trigger phosphorylation-dependent protein degradation of multiple proteins required for cell-cycle progression. This response may be circumvented during carcinogenesis by a variety of mechanisms, some of them yet to be discovered, which in essence turn ERK functions from tumor suppression to tumor promotion. The use of pharmacologic inhibitors targeting this pathway must be carefully evaluated so they are applied to cases in which ERKs are mainly oncogenic.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".