Insights of RKIP-Derived Suppression of Prostate Cancer
Bibliographic record
Abstract
Prostate cancer (PC) is a major cause of cancer death in men. The disease has a great disparity in prognosis. Although low grade PCs with Gleason scores ≤ 6 are indolent, high-risk PCs are likely to relapse and metastasize. The standard of care for metastatic PC (mPC) remains androgen deprivation therapy (ADT). Resistance commonly occurs in the form of castration resistant PC (CRPC). Despite decades of research efforts, CRPC remains lethal. Understanding of mechanisms underpinning metastatic progression represents the overarching challenge in PC research. This progression is regulated by complex mechanisms, including those regulating PC cell proliferation, epithelial-mesenchymal transition (EMT), and androgen receptor (AR) signaling. Among this PC metastatic network lies an intriguing suppressor of PC metastasis: the Raf kinase inhibitory protein (RKIP). Clinically, the RKIP protein is downregulated in PC, and showed further reduction in mPC. In xenograft mouse models for PC, RKIP inhibits metastasis. In vitro, RKIP reduces PC cell invasion and sensitizes PC cells to therapeutic treatments. Mechanistically, RKIP suppresses Raf-MEK-ERK activation and EMT, and modulates extracellular matrix. In return, Snail, NFκB, and the polycomb protein EZH2 contribute to inhibition of RKIP expression. In this review, we will thoroughly analyze RKIP's tumor suppression actions in PC.
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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 | 0.000 |
| 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".