Global Gene Expression Analysis of Reactive Stroma in Prostate Cancer
Bibliographic record
Abstract
PURPOSE: Marked reactive stroma formation, designated as grade 3 reactive stroma, is associated with poor outcome in clinically localized prostate cancer. To understand the biological processes and signaling mechanisms underlying the formation of such reactive stroma, we carried out microarray gene expression analysis of laser-captured reactive stroma and matched normal stroma. EXPERIMENTAL DESIGN: Seventeen cases of reactive stroma grade 3 cancer were used to laser-capture tumor and normal stroma. Expression analysis was carried out using Agilent 44K arrays. Up-regulation of selected genes was confirmed by quantitative reverse transcription-PCR. Expression data was analyzed to identify significantly up- and down-regulated genes, and gene ontology analysis was used to define pathways altered in reactive stroma. RESULTS: A total of 544 unique genes were significantly higher in the reactive stroma and 606 unique genes were lower. Gene ontology analysis revealed significant alterations in a number of novel processes in prostate cancer reactive stroma, including neurogenesis, axonogenesis, and the DNA damage/repair pathways, as well as evidence of increases in stem cells in prostate cancer reactive stroma. CONCLUSIONS: Formation of reactive stroma in prostate cancer is a dynamic process characterized by significant alterations in growth factor and signal transduction pathways and formation of new structures, including nerves and axons.
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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 | 0.000 |
| 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".