Functional transcriptomic annotation and protein–protein interaction analysis identify <scp>EZH</scp>2 and <scp>UBE</scp>2C as key upregulated proteins in ovarian cancer
Bibliographic record
Abstract
Although early stage ovarian cancer is in most cases a curable disease, some patients relapse even with appropriate adjuvant treatment. Therefore, the identification of patient and tumor characteristics to better stratify risk and guide rational drug development is desirable. Using transcriptomic functional annotation followed by protein-protein interacting (PPI) network analyses, we identified functions that were upregulated and associated with detrimental outcome in patients with early stage ovarian cancer. Some of the identified functions included cell cycle, cell division, signal transduction/protein modification, cellular response to extracellular stimuli or transcription regulation, among others. Genes within these functions included AURKA, AURKB, CDK1, BIRC5, or CHEK1 among others. Of note, the histone-lysine N-methyltransferase (EZH2) and the ubiquitin-conjugating enzyme E2C (UBE2C) genes were found to be upregulated and amplified in 10% and 6% of tumors, respectively. Of note, EZH2 and UBE2C were identified as principal interacting proteins of druggable networks. In conclusion, we describe a set of genes overexpressed in ovarian cancer with potential for therapeutic intervention including EZH2 and UBE2C.
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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".