miR-221/222 Are Involved in Response to Sunitinib Treatment in Metastatic Renal Cell Carcinoma
Bibliographic record
Abstract
Sunitinib is a multitargeting tyrosine kinase inhibitor used for metastatic renal cancer. There are no biomarkers that can predict sunitinib response. Such markers are needed to avoid administration of costly medication with side effects to patients who would not benefit from it. We compared global miRNA expression between patients with a short (≤12 months) versus prolonged (>12 months) progression-free survival (PFS) under sunitinib as first-line therapy for metastatic renal cell carcinoma. We identified a number of differentially expressed miRNAs and developed miRNA statistical models that can accurately distinguish between the two groups. We validated our models in the discovery set and an independent set of 57 patients. Target prediction and pathway analysis showed that these miRNAs are involved in vascular endothelial growth factor (VEGF), TGFβ, and mammalian target of rapamycin (mTOR)-mediated signaling and cell-cell communication. We tested the effect of these miRNAs on cellular proliferation and angiogenesis. We validated the negative correlation between miR-221 and its target, VEGFR2.miR-221 overexpression was associated with a poor PFS while its target, VEGFR2 was associated with longer survival. Gain of function experiments showed that miR-221 and miR-222 decreased angiogenesis and cellular proliferation in human umbilical vein endothelial cells (HUVEC) while increasing cellular proliferation in ACHN cells. miRNAs represent potential predictive markers for sunitinib response.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".