Integrating endocrine regulatory mechanisms in prostate cancer progression
Bibliographic record
Abstract
P cancer (PCa) ranks first in incidence among cancers in American males and second as a cause of death by cancer. Beside age, etiologic and progression factors are still not fully identified. Accumulating evidence point to changes in signalling molecules and pathways as means to confer survival and/or growth advantages to tumor cells as they evolve with time and become resistant to androgen deprivationand chemo-therapies. Our studies on the non-receptor Fak and Fer tyrosine kinases (TKs) indicate that beside their up-regulation in PCa, these kinases are key elements controlling PCa cell motility and survival/growth, respectively. Notably, Fak signaling complexes with Src, paxillin and integrins are involved in PCa cell response to neuroendocrine (NE) products, eliciting selective patterns based on androgen receptor (AR) expression in PCa cells. The Fer TK integrates signals emanating from interleukin (IL)-6 and to a lesser extent growth factors (EGF, IGF-1) and also androgens. This occurs through STAT3 and AR, both identified as Fer substrates and binding partners and accumulating into the nucleus where they regulate transcription. Altogether, these mechanisms favor the adaptation of androgen-sensitive luminal-like tumor cells and the concomitant selection of androgen-independent tumor cell subsets (stem, NE). They concur to cell heterogeneity of tumors and metastases as observed during PCa progression.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".