The EWSR1/NR4A3 fusion protein of extraskeletal myxoid chondrosarcoma activates the <i>PPARG</i> nuclear receptor gene
Bibliographic record
Abstract
The NR4A3 nuclear receptor is implicated in the development of extraskeletal myxoid chondrosarcoma (EMC), primitive sarcoma unrelated to conventional chondrosarcomas, through a specific fusion with EWSR1 resulting in an aberrant fusion protein that is thought to disrupt the transcriptional regulation of specific target genes. We performed an expression microarray analysis of EMC tumours expressing the EWSR1/NR4A3 fusion protein, comparing their expression profiles to those of other sarcoma types. We thereby identified a set of genes significantly overexpressed in EMC relative to other sarcomas, including PPARG and NDRG2. Western blot or immunohistochemical analyses confirm that PPARG and NDRG2 are expressed in tumours positive for EWSR1/NR4A3. Bioinformatic analysis identified a DNA response element for EWSR1/NR4A3 in the PPARG promoter, and band-shift experiments and transient transfections indicate that EWSR1/NR4A3 can activate transcription through this element. Western blots further show that an isoform of the native NR4A3 receptor lacking the C-terminal domain is very highly expressed in tumours positive for EWSR1/NR4A3, and co-transfections of this isoform along with EWSR1/NR4A3 indicate that it may negatively regulate the activity of the fusion protein on the PPARG promoter. These results suggest that the overall expression of PPARG in EMC may be regulated in part by the balance between EWSR1/NR4A3 and NR4A3, and that PPARG may play a crucial role in the development of these tumours. The specific up-regulation of PPARG by EWSR1/NR4A3 may also have potential therapeutic implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".