Micro<scp>RNA</scp>‐194 is a Marker for Good Prognosis in Clear Cell Renal Cell Carcinoma
Bibliographic record
Abstract
Clear cell renal cell carcinoma (ccRCC) is the most prevalent adult kidney cancer. Prognostic markers are needed to guide patient management toward aggressive versus more conservative approaches, especially for small tumors ≤4 cm. miR-194 was reported to be downregulated in several cancers and is involved in epithelial to mesenchymal transition. We evaluated miR-194 as a prognostic marker in ccRCC. In a cohort of 234 patients with primary ccRCC, we correlated miR-194 expression level with multiple clinicopathological features including disease-free and overall survival, tumor size, clinical stage, and histological grade. Our results shows a stepwise decrease in miR-194 expression from normal kidney to primary ccRCC (P = 0.0032) and a subsequent decrease from primary to metastatic lesions. Additionally, patients with higher miR-194 expression has significantly longer disease-free survival (P = 0.041) and overall survival (P = 0.031) compared to those with lower expression. In multivariate analysis, miR-194-positive tumors retain significance in disease-free survival and overall survival, suggesting miR-194 is an independent marker for good prognosis in ccRCC. Moreover, miR-194 is a marker for good prognosis for patients with small renal masses (P = 0.014). These findings were validated on an independent data set from The Cancer Genome Atlas. We also compared miR-194 expression between RCC subtypes. ccRCC had the highest levels, whereas chromophobe RCC and oncocytoma had comparable lower levels. Target prediction coupled with pathway analysis show that miR-194 is predicted to target key molecules and pathways involved in RCC progression. miR-194 represents a prognostic biomarker in ccRCC.
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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 | 0.001 |
| 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) | 0.002 | 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".