Bibliographic record
Abstract
Introduction:We have previously identified using RNA micro-array a set of genes that were upregulated in aggressive renal cell carcinoma RCC cell lines.These genes included a subset belonging to the Fanconi Anemia pathway, a DNA repair pathway.Through preliminary elimination, FANCD2 was identified as the most significant of these genes and was further characterized for its role in RCC. Material and Methods:The expression of FANCD2 RNA was assessed in praffin imbedded and fresh frozen (RCC) samples.Imune Staining of FANCD2 has been carried out for protein expression on a TMA that includes clear cell, non clear cell RCC, and normal adjacent tissue.A knock down in vitro model for FANCD2 was created using 786-0±VHL and RCC4±VHL cell lines.Western blotting for various components of the VHL pathway was carried out comparing knock down to wild type.Proliferation, branching and invasion were compared between knock down to wild type.Similarly an in vivo assay using nude mice is being carried out for tumour formation using the two cell lines.Results: FANCD2 RNA expression is significantly increased in clear cell RCC versus normal tissue (p=0.01) but not in non-clear cell RCC versus normal (p>0.05) in paraffin imbedded samples.The same is true for fresh frozen samples (p=0.04 and p>0.05 respectively).There was no difference between FANCD2 knock down cell lines and Wild type with respect to the expression of VHL pathway components (including EGFR, TGFα and VEGF).TMA immunohistochemistry is in the prossess of being scored.FANCD2 expression does not affect proliferation rate.In contrast, FANCD2 knock down highly increases branching and invasion in VHL deficient cell lines.Conclusion: Our results suggest that FANCD2 may play a protective role in RCC that seems independent of the VHL pathway.A VHL mutation may lead to increased FANCD2 expression to accelerate DNA repair.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.851 | 0.706 |
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".