Prognostic implications of tenascin C in peripheral blood and primary tumours at the time of uveal melanoma diagnosis
Bibliographic record
Abstract
OBJECTIVE: To examine the prognostic implication of tenascin C (TNC) in posterior uveal melanoma (UM). DESIGN: Retrospective cohort study. PARTICIPANTS: A total of 162 patients diagnosed with posterior UM. METHODS: A peripheral blood sample was obtained from 82 patients at the time of UM diagnosis between 1996 and 1999. Samples were kept frozen at -80°C until the concentration of TNC was measured in 2021. Primary tumour TNC RNA sequencing data were collected from another 80 patients (The Cancer Genome Atlas cohort). Patients were separated based on median TNC values. Cumulative incidences of metastatic death (UM mortality) from competing risks data were calculated as well as Cox regression hazard ratios. RESULTS: Patients with high and low TNC levels had tumours of similar size and American Joint Committee on Cancer stage at Bonferroni-corrected significance levels. The exception was a significantly smaller tumour diameter in patients with high serum TNC levels (p = 0.003). In competing risks analysis, patients with high serum TNC levels (≥7 ng/mL) had a higher UM mortality rate (44% vs 17% at 20 years; p = 0.008). Similarly, patients with higher primary tumour TNC RNA levels (≥1 transcripts per million) had higher UM mortality (83% vs 27% at 5 years; p = 0.003). In multivariate Cox regressions, TNC levels in peripheral blood and primary tumours were predictors of metastatic death independent of American Joint Committee on Cancer stage. CONCLUSIONS: TNC is a prognostic biomarker in UM. At the time of primary tumour diagnosis, it is measured in higher levels in both peripheral blood and tumour tissue from patients who will eventually suffer from metastatic death.
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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 | 0.002 |
| 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".