Wilms’ tumor gene 1: lessons from the interface between kidney development and cancer
Bibliographic record
Abstract
In 1990, mutations of the Wilms’ tumor-1 gene ( WT1), encoding a transcription factor in the embryonic kidney, were found in 10–15% of Wilms’ tumors; germline WT1 mutations were associated with hereditary syndromes involving glomerular and reproductive tract dysplasia. For more than three decades, these discoveries prompted investigators to explore the embryonic role of WT1 and the mechanisms by which loss of WT1 leads to malignant transformation. Here, we discuss how alternative splicing of WT1 generates isoforms that act in a context-specific manner to activate or repress target gene transcription. WT1 also regulates posttranscriptional regulation, alters the epigenetic landscape, and activates miRNA expression. WT1 functions at multiple stages of kidney development, including the transition from resting stem cells to committed nephron progenitor, which it primes to respond to WNT9b signals from the ureteric bud. WT1 then drives nephrogenesis by activating WNT4 expression and directing the development of glomerular podocytes. We review the WT1 mutations that account for Denys–Drash syndrome, Frasier syndrome, and WAGR syndrome. Although the WT1 story began with Wilms’ tumors, an understanding of the pathways that link aberrant kidney development to malignant transformation still has some important gaps. Loss of WT1 in nephrogenic rests may leave these premalignant clones with inadequate DNA repair enzymes and may disturb the epigenetic landscape. Yet none of these observations provide a complete picture of Wilms’ tumor pathogenesis. It appears that the WT1 odyssey is unfinished and still holds a great deal of untilled ground to be explored.
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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 | 0.002 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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".