BTF4/BTNA3.2 and GCS as Candidate mRNA Prognostic Markers in Epithelial Ovarian Cancer
Bibliographic record
Abstract
This study aims to identify reliable prognosis markers to predict patient outcome at surgery in high-grade serous epithelial ovarian cancer by a real-time quantitative PCR (RT-q-PCR)-based test. Seventeen tissue samples from serous epithelial ovarian cancer patients were screened by DNA microarray to identify genes differentially expressed between tumors from patients who relapsed within 18 months and tumors from patients showing no relapse or relapsed after 24 months after initial diagnosis. RNA expression of a subset of genes was validated by RT-q-PCR in the initial set of 17 samples. From these results, a refined list was selected and tested in independent samples from 41 serous. Expression was associated with time to relapse and clinical variables. Microarray analysis identified a profile of 34 differentially expressed genes. RT-q-PCR validated the expression profile of a subset of seven genes in the initial set of patients. Differential gene expression was also validated in an independent set of patients. Low BTF4 or GCS expression was strongly associated with poor outcome in Kaplan-Meier analysis (P < 0.05, log-rank test) and Cox univariate as well as in multivariate analyses with a higher hazard ratio than clinical variables, such as residual disease, age, stage, and grade.
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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) | 0.001 | 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".