A Pathologic Link between Wilms Tumor Suppressor Gene, WT1, and IFI16
Bibliographic record
Abstract
The Wilms tumor gene (WT1) is mutated or deleted in patients with heredofamilial syndromes associated with the development of Wilms tumors, but is infrequently mutated in sporadic Wilms tumors. By comparing the microarray profiles of syndromic versus sporadic Wilms tumors and WT1-inducible Saos-2 osteosarcoma cells, we identified interferon-inducible protein 16 (IFI16), a transcriptional modulator, as a differentially expressed gene and a candidate WT1 target gene. WT1 induction in Saos-2 osteosarcoma cells led to strong induction of IFI16 expression and its promoter activity was responsive to the WT1 protein. Immunohistochemical analysis showed that IFI16 and WT1 colocalized in WT1-replete Wilms tumors, but not in normal human midgestation fetal kidneys, suggesting that the ability of WT1 to regulate IFI16 in tumors represented an aberrant pathologic relationship. In addition, endogenous IFI16 and WT1 interacted in vivo in two Wilms tumor cell lines. Furthermore, IFI16 augmented the transcriptional activity of WT1 on both synthetic and physiological promoters. Strikingly, short hairpin RNA (shRNA)-mediated knockdown of either IFI16 or WT1 led to decreased growth of Wilms tumor cells. These data suggest that IFI16 and WT1, in certain cellular context including sporadic Wilms tumors, may support cell survival.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".