Bibliographic record
Abstract
The 90 kDa ribosomal S6 kinase (RSK) family is a group of highly conserved Ser/Thr kinases that promote cell proliferation, growth, motility and survival. Deregulated RSK expression or activity has been associated with several human diseases, including cancer. RSK lies downstream of the Ras/mitogen-activated protein kinase (MAPK) signalling pathway and is directly phosphorylated by the extracellular signal-regulated kinases 1 and 2 (ERK1/2). Significant advances in the field of RSK signalling have occurred in the past few years, unravelling novel RSK cellular substrates and biological functions as well as new RSK regulatory mechanisms. Together, these findings suggest that RSK may be a promising therapeutic target for the treatment of cancer, particularly those characterized by oncogenic mutations in components of the Ras signalling pathway. This article briefly describes our current knowledge on the impact of RSK on cell growth and proliferation, as well as RSK-dependent mechanisms associated with tumourigenesis. The potential of targeting RSK in cancer is discussed in light of available data on the biological functions of each RSK family members. Targeting RSK with small molecule inhibitors appears to be a promising path for cancer therapy, but several considerations need to be evaluated and will be discussed in detail.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".