Expression of human telomerase reverse transcriptase, Survivin, DD3 and PCGEM1 messenger RNA in archival prostate carcinoma tissue.
Bibliographic record
Abstract
INTRODUCTION: The wide spectrum of biological behavior displayed by prostate cancer (PCa) warrants investigation of potential PCa-specific biomarkers that could identify more aggressive tumor types and therefore provide prognostic value. Upregulation of expression of human telomerase reverse transcriptase (hTERT), Survivin, DD3 and PCGEM1 mRNAs in PCa lesions has recently been described. The purpose of this study was to evaluate the clinical value of detection of over-expression of these biomarkers in the diagnosis and prognosis of PCa. MATERIAL AND METHODS: Archival formalin-fixed, paraffin-embedded (FFPE) prostatectomy tissue from 26 patients with PCa (Gleason score 3-9, mean 7) and 14 patients with benign prostatic hyperplasia (BPH) were analyzed by reverse transcription polymerase chain reaction (RT-PCR) for semiquantitative transcript levels of hTERT, Survivin, DD3 and PCGEM1. In addition, 25 matched normal (MN) tissue samples were examined. The expression of biomarker mRNA relative to b2-microglobulin mRNA was determined using AlphaImager 2200 data analysis software. RESULTS: The biomarkers had sensitivities ranging from 91% to 100%. Clinical specificities evaluated with the BPH tissue were the following: hTERT mRNA (93%), DD3 mRNA (57%), Survivin (29%) and PCGEM1 (14%). Biomarker expressions were up to 13.5-fold higher in PCa tissue as compared to MN tissue. None of the tumor biomarkers showed a positive correlation with pathological stage and Gleason score. CONCLUSIONS: The results of this study indicate potential utility of the hTERT mRNA and DD3 mRNA as diagnostic but not prognostic biomarkers for 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 | 0.001 | 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".