RSK1 Activation Promotes Invasion in Nodular Melanoma
Bibliographic record
Abstract
The two major melanoma histologic subtypes, superficial spreading and nodular melanomas, differ in their speed of dermal invasion but converge biologically once they invade and metastasize. Herein, we tested the hypothesis that distinct molecular alterations arising in primary melanoma cells might persist as these tumors progress to invasion and metastasis. Ribosomal protein S6 kinase, 90 kDa, polypeptide 1 (RSK1; official name RPS6KA1) was significantly hyperactivated in human melanoma lines and metastatic tissues derived from nodular compared with superficial spreading melanoma. RSK1 was constitutively phosphorylated at Ser-380 in nodular but not superficial spreading melanoma and did not directly correlate with BRAF or MEK activation. Nodular melanoma cells were more sensitive to RSK1 inhibition using siRNA and the pharmacological inhibitor BI-D1870 compared with superficial spreading cells. Gene expression microarray analyses revealed that RSK1 orchestrated a program of gene expression that promoted cell motility and invasion. Differential overexpression of the prometastatic matrix metalloproteinase 8 and tissue inhibitor of metalloproteinases 1 in metastatic nodular compared with metastatic superficial spreading melanoma was observed. Finally, using an in vivo zebrafish model, constitutive RSK1 activation increased melanoma invasion. Together, these data reveal a novel role for activated RSK1 in the progression of nodular melanoma and suggest that melanoma originating from different histologic subtypes may be biologically distinct and that these differences are maintained as the tumors invade and metastasize.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".