<i>ASAP1</i>, a Gene at 8q24, Is Associated with Prostate Cancer Metastasis
Bibliographic record
Abstract
Metastatic prostate cancer is a terminal disease, and the development of reliable prognostic tools and more effective therapy is critically important for improved disease survival and management. This study was aimed at identifying genes that are differentially expressed in metastatic and nonmetastatic prostate cancer cells and, as such, could be critical in the development of metastasis. Long-SAGE analysis was used to compare a transplantable human metastatic prostate cancer subline, PCa1-met, with a nonmetastatic counterpart, PCa2. Both sublines were developed from a patient's prostate cancer specimen via subrenal capsule grafting and subsequent orthotopic implantation into SCID mice. Among various differentially expressed genes identified, ASAP1, an 8q24 gene encoding an ADP-ribosylation factor GTPase-activating protein not previously associated with prostate cancer, was up-regulated in the metastatic subline as confirmed by quantitative real-time PCR. Immunohistochemistry of xenograft sections showed that cytoplasmic ASAP1 protein staining was absent or weak in benign tissue, significantly stronger in nonmetastatic PCa2 tissue, and strongest in PCa1-met tissue. In clinical specimens, ASAP1 protein staining was elevated in 80% of primary prostate cancers and substantially higher in metastatic lesions compared with benign prostate tissue. Moreover, additional ASAP1 gene copies were detected in 58% of the primary prostate cancer specimens. Small interfering RNA-induced reduction of ASAP1 protein expression markedly suppressed in vitro PC-3 cell migration (approximately 50%) and Matrigel invasion (approximately 67%). This study suggests that the ASAP1 gene plays a role in prostate cancer metastasis and may represent a therapeutic target and/or biomarker for metastatic disease.
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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) | 0.001 | 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".