Metabolic features of clear-cell renal cell carcinoma: mechanisms and clinical implications
Bibliographic record
Abstract
Central to the malignant behaviour that endows cancer cells with growth advantage is their unique metabolism. Cancer cells can process nutrient molecules differently from normal cells and use it to overcome stress imposed on them by various therapies. This metabolic conversion is controlled by specific genetic mutations that are associated with activation of oncogenes and loss of tumour suppressor proteins. Understanding these processes is important as it can lead to the discovery of biomarkers that can predict the aggressiveness of the disease and its response to therapy, and even more importantly, to the development of novel therapeutics. A classic tumour in this respect is clear-cell renal cell carcinoma (RCC). In this review, we will begin with a brief summary of normal cellular bioenergetic pathways, which will be followed by a description of the characteristic metabolism of glucose and lipids in clear-cell RCC cells and its clinical implications. Data relating to the potential effect of dietary nutrients on RCC will also be reviewed along with potential therapies targeted at interrupting specific metabolic pathways in clear-cell RCC.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".