Common reduction of the Raf kinase inhibitory protein in clear cell renal cell carcinoma
Bibliographic record
Abstract
// Brianne Hill 1 , 2 , 3 , Jason De Melo 1 , 2 , 3 , Judy Yan 1 , 2 , 3 , Anil Kapoor 3 , 4 , Lizhi He 1 , 2 , 3 , 5 , Jean-Claude Cutz 6 , Xingchang Feng 1 , 3 , 7 , Nazihah Bakhtyar 1 , 2 , 3 , Damu Tang 1 , 2 , 3 1 Division of Nephrology, Department of Medicine, McMaster University, Hamilton, Ontario, Canada, Hamilton, Ontario, Canada 2 Father Sean O’Sullivan Research Institute, Hamilton, Ontario, Canada 3 The Hamilton Center for Kidney Research, St. Joseph’s Hospital, Hamilton, Ontario, Canada 4 Division of Urology, Department of Surgery, McMaster University, Hamilton, Ontario, Canada 5 Department of Biological Chemistry and Molecular Pharmacology (BCMP), Harvard Medical School, Boston, MA, USA 6 Department of Pathology, McMaster University, Hamilton, Ontario, Canada. 7 College of Veterinary Medicine, Northeast Agricultural University, Harbin, China 150030 Correspondence: Damu Tang e-mail: damut@mcmaster.ca Running title: RKIP suppresses ccRCC tumorigenesis Key words: RKIP, Raf, ERK, ccRCC, and kidney cancer Received: October 30, 2013 Accepted: April 21, 2014 Published: April 24, 2014 ABSTRACT Despite the recent progress in our understanding of clear cell renal cell carcinomas (ccRCCs), the etiology of ccRCC remains unclear. We reported here a prevailing reduction of the raf kinase inhibitory protein (RKIP) in ccRCC. In our examination of more than 600 ccRCC patients by western blot and immunohistochemistry, RKIP was significantly reduced in 80% of tumors. Inhibition of RKIP transcription in ccRCC occurs to greater levels than VHL transcription based on the quantification analysis of their transcripts in six large datasets of DNA microarray available in Oncomine™ with the median rank of suppression being 582 and 2343 for RKIP and VHL, respectively. Collectively, the magnitude of RKIP reduction and the levels of its downregulation match those of VHL. Furthermore, RKIP displays tumor suppressing activity in ccRCC. While modulation of RKIP expression did not affect the proliferation of A498 and 786-0 ccRCC cells and neither their ability to form xenograft tumors in NOD/SCID mice, ectopic expression or knockdown of RKIP inhibited or enhanced A498 and 786-0 ccRCC cell invasion, respectively. This was associated with robust changes in vimentin expression, a marker of EMT. Taken together, we demonstrate here that downregulation of RKIP occurs frequently at a rate that reaches that of VHL, suggesting RKIP being a critical tumor suppressor for ccRCC. This is consistent with RKIP being a tumor suppressor for other cancers.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".