Cell-surface and mitotic-spindle RHAMM: moonlighting or dual oncogenic functions?
Bibliographic record
Abstract
Tumor cells use a wide variety of post-translational mechanisms to modify the functional repertoire of their transcriptome. One emerging but still understudied mechanism involves the export of cytoplasmic proteins that then partner with cell-surface receptors and modify both the surface-display kinetics and signaling properties of these receptors. Recent investigations demonstrate moonlighting roles for the proteins epimorphin, FGF1, FGF2, PLK1 and Ku80, to name a few, during oncogenesis and inflammation. Here, we review the molecular mechanisms of unconventional cytoplasmic-protein export by focusing on the mitotic-spindle/hyaluronan-binding protein RHAMM, which is hyper-expressed in many human tumors. Intracellular RHAMM associates with BRCA1 and BARD1; this association attenuates the mitotic-spindle-promoting activity of RHAMM that might contribute to tumor progression by promoting genomic instability. Extracellular RHAMM-CD44 partnering sustains CD44 surface display and enhances CD44-mediated signaling through ERK1 and ERK2 (ERK1/2); it might also contribute to tumor progression by enhancing and/or activating the latent tumor-promoting properties of CD44. The unconventional export of proteins such as RHAMM is a novel process that modifies the roles of tumor suppressors and promoters, such as BRCA1 and CD44, and might provide new targets for therapeutic intervention.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".