Prostatic Stem Cell Marker Identified by cDNA Microarray in Mouse
Bibliographic record
Abstract
PURPOSE: Identifying prostatic stem cells is important to elucidate the mechanisms by which the prostate develops and control prostate cancer. We recently reported that the proximal region of the mouse prostate contains a population of stem cells. However, to our knowledge the specific marker of stem cells in the proximal region remains unknown. MATERIALS AND METHODS: We performed cDNA microarray analysis of cells obtained from the proximal region and from the remaining regions in dorsal prostates to identify several candidate stem cell markers. After we focused on 1 candidate among them we confirmed the expression of this candidate gene by reverse transcriptase-polymerase chain reaction analysis and immunohistochemistry. We also investigated the relation between positive cells for this marker and those for telomerase reverse transcriptase. Finally, we investigated the functional potential of prominin positive cells in 3-dimensional culture. RESULTS: Seven of 4,800 genes analyzed showed proximal/remaining ratios greater than 20. Of these genes we focused on prominin because it is a cell surface marker widely used to identify and isolate stem cells from various organs. We found a prominin positive cell population enriched in the basal cell layer in the proximal region, and the coincidence of prominin and telomerase reverse transcriptase immunostaining. We also found that prominin positive cells gave rise to numerous and large-branched ducts, whereas prominin negative cells formed far fewer such structures in 3-dimensional culture. CONCLUSIONS: A small population of prominin positive cells in the mouse prostate basal layer of the proximal region represents a stem cell population.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".