Increased expression and differential phosphorylation of stathmin may promote prostate cancer progression
Bibliographic record
Abstract
BACKGROUND: Proteins which regulate normal development may promote tumorigenesis, tumor progression, or metastasis through dysregulation of these functions. We postulate that proteins, which regulate prostate growth also promote prostate cancer (PCa) progression. METHODS: Two Dimensional Gel Electrophoresis was utilized to compare patterns of protein expression in 12T-7f prostates (LPB-Tag mouse model for PCa) during tumor development and progression with those of normal developing and adult wild type CD-1 prostates. Stathmin expression and phosphorylation patterns were analyzed in mouse and human PCa cell lines as well as in human PCa tissue arrays. RESULTS: Stathmin was identified by two-dimensional gel electrophoresis and mass spectrometry. Stathmin levels increase early during normal mouse prostate development and again during prostate tumor development and progression. In human prostate adenocarcinoma, stathmin increases in Gleason pattern 5. Further, stathmin is differentially phosphorylated in androgen-dependent LNCaP cells compared to androgen-independent PC-3 and DU145 cells. This differential phosphorylation is modulated by androgen and anti-androgen treatment. CONCLUSION: Stathmin expression is highest when the prostate is undergoing morphogenesis or tumorigenesis and these processes may be regulated through differential phosphorylation. Furthermore, modulation of stathmin phosphorylation may correlate with the development of androgen-independent PCa.
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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 | 0.000 | 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 | 0.000 |
| 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".