Pediatric rhabdoid tumors of kidney and brain show many differences in gene expression but share dysregulation of cell cycle and epigenetic effector genes
Bibliographic record
Abstract
BACKGROUND: Rhabdoid tumors (RTs) are aggressive tumors of early childhood that occur most often in brain (AT/RTs) or kidney (KRTs). Regardless of location, they are characterized by loss of functional SMARCB1 protein, a component of the SWI/SNF chromatin remodeling complex. The aim of this study was to determine genes and biological process dysregulated in common to both AT/RTs and KRTs. PROCEDURE: Gene expression for AT/RTs was compared to that of other brain tumors and normal brain using microarray data from our lab. Similar analysis was performed for KRTs and other kidney tumors and normal kidney using data from GEO. Dysregulated genes common to both analyses were analyzed for functional significance. RESULTS: Unsupervised hierarchical clustering of RTs identified three major subsets: two comprised of AT/RTs, and one of KRTs. Compared to other tumors, 1,187, 663, and 539 genes were dysregulated in each subset, respectively. Only 14 dysregulated genes were common to all three subsets. Compared to normal tissue, 5,209, 4,275, and 2,841 genes were dysregulated in each subset, with an overlap of 610 dysregulated genes. Among these genes, processes associated with cell proliferation, MYC activation, and epigenetic dysregulation were common to all three RT subsets. CONCLUSIONS: The low overlap of dysregulated genes in AT/RTs and KRTs suggests that factors in addition to SMARCB1 loss play a role in determining subsequent gene expression. Drugs which target cell cycle or epigenetic genes may be useful in all RTs. Additionally, targeted therapies tailored to specific RT subset molecular profiles should be considered.
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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 | 0.001 | 0.001 |
| 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".