A Precision Strategy to Cure Renal Cell Carcinoma by Targeting Transglutaminase 2
Bibliographic record
Abstract
In a recent report, no significance of transglutaminase 2 (TGase 2) was noted in the analyses of expression differences between normal and clear cell renal cell carcinoma (ccRCC), although we found that knock down of TGase 2 induced significant p53-mediated cell death in ccRCC. Generally, to find effective therapeutic targets, we need to identify targets that belong specifically to a cancer phenotype that can be differentiated from a normal phenotype. Here, we offer precise reasons why TGase 2 may be the first therapeutic target for ccRCC, according to several lines of evidence. TGase 2 is negatively regulated by von Hippel-Lindau tumor suppressor protein (pVHL) and positively regulated by hypoxia-inducible factor 1-α (HIF-1α) in renal cell carcinoma (RCC). Therefore, most of ccRCC presents high level expression of TGase 2 because over 90% of ccRCC showed VHL inactivity through mutation and methylation. Cell death, angiogenesis and drug resistance were specifically regulated by TGase 2 through p53 depletion in ccRCC because over 90% of ccRCC express wild type p53, which is a cell death inducer as well as a HIF-1α suppressor. Although there have been no detailed studies of the physiological role of TGase 2 in multi-omics analyses of ccRCC, a life-long study of the physiological roles of TGase 2 led to the discovery of the first target as well as the first therapeutic treatment for ccRCC in the clinical field.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".