Ubiquitin-conjugating enzyme E2T (UBE2T) and denticleless protein homolog (DTL) are linked to poor outcome in breast and lung cancers
Bibliographic record
Abstract
Protein ubiquitination and degradation represent druggable vulnerabilities of cancer cells. We used gene expression and functional annotation analyses to identify genes in the ubiquitin pathway which are differentially expressed between normal breast and basal-like tumors. With this approach we identified 16 ubiquitin related genes overexpressed in basal-like breast cancers compared with normal breast. We then explored the association between these genes and outcomes using the KMPlotter online tool. Two genes, the ubiquitin-conjugating enzyme E2T (UBE2T) and the denticleless protein homolog (DTL) were overexpressed and linked with detrimental outcome in basal-like and luminal breast cancer patients. Furthermore, we found that UBE2T and DTL were amplified in around 12% of breast tumors based on data contained at cBioportal. In non-small cell lung adenocarcinomas, UBE2T and DTL were also amplified in around 7% of cases and linked with disease recurrence after surgical resection. No significant molecular alterations or a clear trend for clinical outcome was observed for these genes in ovarian serous cystadenocarcinoma, esophagus-stomach cancer or non-small squamous cell carcinoma. Our data suggest that UBE2T and DTL may have a role in the pathophysiology of breast and lung tumors, opening avenues for future clinical evaluation of agents targeting those proteins or their pathways.
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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".