Dysregulation of kallikrein-related peptidases in renal cell carcinoma: potential targets of miRNAs
Bibliographic record
Abstract
Renal cell carcinoma (RCC) accounts for 3% of all adult malignancies and currently no diagnostic marker exists. Kallikrein-related peptidases (KLKs) have been implicated in numerous cancers including ovarian, prostate, and breast carcinoma. KLKs 5, 6, 10, and 11 have decreased expression in RCC when compared to normal kidney tissue. Our bioinformatic analysis indicated that the KLK 1, 6, and 7 genes have decreased expression in RCC. We experimentally verified these results and found that decreased expression of KLKs 1 and 3 were significantly associated with the clear cell RCC subtype (p<0.001). An analysis of miRNAs differentially expressed in RCC showed that 61 of the 117 miRNAs that were reported to be dysregulated in RCC were predicted to target KLKs. We experimentally validated two targets using two independent approaches. Transfection of miR-224 into HEK-293 cells resulted in decreased KLK1 protein levels. A luciferase assay demonstrated that hsa-let-7f can target KLK10 in the RCC cell line ACHN. Our results, showing differential expression of KLKs in RCC, suggest that KLKs could be novel diagnostic markers for RCC and that their dysregulation could be under miRNA control. The observation that KLKs could represent targets for miRNAs suggests a post-transcriptional regulatory mechanism with possible future 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".