miR-10b is a prognostic marker in clear cell renal cell carcinoma
Bibliographic record
Abstract
AIMS: Clear cell renal cell carcinoma (ccRCC) is the most common adult kidney cancer. It is an aggressive tumour with unpredictable outcome. The currently used clinical parameters are not always accurate for predicting disease behaviour. miR-10b is dysregulated in different malignancies including RCC. METHODS: We assessed the clinical utility of miR-10b as a prognostic marker in 250 patients with primary ccRCC. We examined the correlation between miR-10b and clinicopathological parameters. We compared miR-10b expression among different RCC subtypes and normal kidney tissue. RESULTS: We observed a stepwise decrease of miR-10b expression from normal kidney to primary ccRCC and a further decrease from primary to metastatic RCC. miR-10b expression was significantly lower in stages III/IV compared with stages I/II (p=0.038). Using a binary cut-off, miR-10b-positive patients had significantly longer disease-free survival (HR=0.47, CI 0.28 to 0.79, p=0.004). In the subgroup of patients with tumour size >4 cm, higher miR-10b expression was associated with significant longer disease-free and overall survival (p=0.001 and p=0.036, respectively). miR-10b was significantly downregulated in ccRCC compared with normal kidney (p<0.0001), and oncocytoma (p=0.031). It was also downregulated in chromophobe RCC. In addition, we identified a number of miR-10b-predicted targets and pathways that are involved in tumourigenesis. CONCLUSIONS: Our data point to miR-10b as a promising prognostic marker in ccRCC with potential therapeutic applications.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".