Quantitative analysis of macrophage inhibitory cytokine-1 (MIC-1) gene expression in human prostatic tissues
Why this work is in the frame
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Bibliographic record
Abstract
Macrophage inhibitory cytokine-1 (MIC-1) gene is a member of transforming growth factor-beta superfamily and was reported to be highly overexpressed in human prostate cancer using microarray technology. The aim of this study was to evaluate the quantitative expression of MIC-1 in malignant and benign prostate tissues and to associate expression levels with clinicopathological parameters of prostate cancer. Matched (paired) prostatic tissue samples from the cancerous and noncancerous parts of the same prostates were obtained from 66 patients who underwent radical prostatectomy. Quantitative RT-PCR was performed using SYBR Green I on the Roche LightCycler system. Macrophage inhibitory cytokine-1 gene overexpression in cancerous tissues was observed in 88% of cases, compared to noncancerous tissues (P<0.001). The expression level of MIC-1 in cancerous tissues was significantly higher than in noncancerous tissue (P<0.001). Higher expression of MIC-1 gene was significantly associated with higher Gleason score (P=0.004). The expression of the MIC-1 gene in prostate cancer is significantly higher than in noncancerous tissues, especially in more aggressive forms of the disease (Gleason score>5). This is in contrast to prostate-specific antigen that is downregulated in higher-grade tumours. The upregulation of MIC-1 in prostate cancer and in advanced and more aggressive prostatic tumours suggests that MIC-1 protein should be evaluated as a potential diagnostic and prognostic biomarker.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it