VHL Gene Restoration Supports RCC Reprogramming to iPSCs but Does Not Ensure Line Stability
Bibliographic record
Abstract
BACKGROUND: Modeling precancerous stages holds the promise to understand early transformation events, thereby offering the potential for personalized, targeted treatment. Because cancer hijacks developmental pathways, precancerous stages could potentially be modeled by reprogramming cancer cells to an induced pluripotent stem cell state and subsequently differentiating them to the target organs using organoid models. METHODS: We attempted reprogramming of patient-derived clear cell renal cell carcinoma (ccRCC) cell lines and adjacent normal renal epithelial cell lines using lentivirus or episomal reprogramming vectors. RESULTS: The cancer cells failed to reprogram while the adjacent normal cells reprogrammed with high efficiency. The von Hippel-Lindau factor (VHL) gene was re-expressed in ccRCC cells in an attempt to restore the wild-type phenotype and restore reprogramming. The VHL gene is the major tumor suppressor in ccRCC pathogenesis and a conductor of oxidative-glycolytic glucose metabolism. While its re-expression did restore the epithelial phenotype and oxidative regulation of ccRCC cells, they still failed to stably reprogram. With an optimized reprogramming workflow, VHL-corrected ccRCC cells generate NANOG+ cells; however, they remained dependent on the ectopic expression of the reprogramming factors. CONCLUSIONS: We concluded that while VHL expression is necessary for cellular reprogramming of ccRCC cells, other genetic lesions in the ccRCC cells could be preventing the stabilization of the pluripotent state.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".