Patient outcome prediction using multiple biomarkers in human melanoma: A clinicopathological study of 118 cases
Bibliographic record
Abstract
The application of biomarkers in melanoma prognosis has been well recognized. However the ability of a single biomarker to predict melanoma patient outcome is usually limited. We previously examined the expression of ten biomarkers (Bim, BRG1, BRMS1, CTHRC1, ING4, NQO1, NF-κB-p50, PUMA, SNF5 and SOX4) in melanomas. To assess the value of a combined multiple biomarker system in melanoma prognosis, we compared the expression of each biomarker between various stages of melanoma, and determined the best combination of biomarkers for melanoma prognosis. Although the expression of six biomarkers (Bim, BRMS1, ING4, NQO1, PUMA and SOX4) was significantly decreased in AJCC III-IV stages of melanoma compared to AJCC I-II stages, the combined 6-biomarker index score exhibited higher variations than any individual biomarker in the same comparison. Moreover, the 6-biomarker index score was correlated with melanoma thickness, location and subtype, and predicted the outcome of melanoma patients more accurately than the individual biomarkers. Multivariate Cox regression analysis demonstrated that the 6-biomarker index score is an independent prognostic factor for melanoma. In conclusion, our study suggests that a multi-biomarker system test is valuable for improved outcome prediction in melanoma patients and for the development of novel therapeutic strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".