Effect of AXIN2 expression on prostate cancer recurrence and an invasive, tumorigenic phenotype.
Bibliographic record
Abstract
29 Background: Better biomarkers are needed in prostate cancer (PCa) to predict disease recurrence and guide optimal therapy. We investigated whether genes associated with a highly tumorigenic, drug resistant, progenitor cancer phenotype impact PCa biology and clinical outcomes in localized disease. Methods: Genes associated with self-renewal, drug resistance, and tumorigenicity were analyzed by qRT-PCR on PCa mRNA from radical prostatectomy (RP) specimens (+/- disease recurrence). Wilcoxon rank sum two-sample test, multivariable recursive partitioning, and bootstrap internal validation measured and confirmed associations with recurrence. Further validation was conducted in external cohorts and in-silico, as well as in vitro and in vivo using siRNA knockdown and lentiviral overexpression to determined the effect of gene expression on PCa proliferation, invasion and tumor growth. Results: Four candidate genes were differentially expressed in PCa recurrence, and of these, low AXIN2 expression was internally validated. Validation in external cohorts demonstrated low AXIN2 expression was associated with more aggressive prostate cancer and was independently associated with biochemical recurrence (BCR) and metastasis-free survival (MFS) after RP. In vitro, low AXIN2 expression was associated with a cancer stem-like cell-surface signature, and siRNA knockdown of AXIN2 resulted in significantly greater invasiveness. Conversely, ectopic overexpression of AXIN2 significantly reduced cell proliferation and tumor growth in mice. Conclusions: Low AXIN2 expression was associated with PCa recurrence after RP in our test population as well as in external validation cohorts. AXIN2 expression levels in PCa cells significantly impacted invasiveness, proliferation and tumor growth. AXIN2 represents a putative biomarker and potential therapeutic target in early prostate cancer.
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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 | 0.001 |
| 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) | 0.001 | 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".