Recurrent co‐alteration of HDGF and SETDB1 on chromosome 1q drives cutaneous melanoma progression and poor prognosis
Bibliographic record
Abstract
Abstract A progressive increase in copy number variation (CNV) characterizes the natural history of cutaneous melanoma progression toward later disease stages, but our understanding of genetic drivers underlying chromosomal arm‐level CNVs remains limited. To identify candidate progression drivers, we mined the TCGA SKCM dataset and identified HDGF as a recurrently amplified gene whose high mRNA expression correlates with poor patient survival. Using melanocyte‐specific overexpression in the zebrafish BRAFV600E‐driven MiniCoopR melanoma model, we show that HDGF accelerates melanoma development in vivo. Transcriptional analysis of HDGF compared to control EGFP tumors showed the activation of endothelial/angiogenic pathways. We validated this observation using an endothelial kdrl:mCherry reporter line which showed HDGF to increases tumor vasculature. HDGF is frequently co‐altered with the established melanoma driver SETDB1. Both genes are located on chromosome 1q, and their co‐amplification is observed in up to 13% of metastatic melanoma. TCGA patients with both genes amplified and/or overexpressed have a worse melanoma specific survival. We tested co‐expression of HDGF and SETDB1 in the MiniCoopR model, which resulted in faster and more aggressive melanoma development than either gene individually. Our work identifies the co‐amplification of HDGF and SETDB1 as a functional driver of melanoma progression and poor patient prognosis.
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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 | 0.000 |
| 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) | 0.003 | 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".