A 3-Protein Expression Signature of Neuroblastoma for Outcome Prediction
Bibliographic record
Abstract
Neuroblastoma (NB) is the most common extracranial solid tumor in children with contrasting outcomes. Precise risk assessment contributes to prognosis prediction, which is critical for treatment strategy decisions. In this study, we developed a 3-protein predictor model, including the neural stem cell marker Msi1, neural differentiation marker ID1, and proliferation marker proliferating cell nuclear antigen (PCNA), to improve clinical risk assessment of patients with NB. Kaplan-Meier analysis in the microarray data (GSE16476) revealed that low expression of ID1 and high expression of Msi1 and PCNA were associated with poor prognosis in NB patients. Combined application of these 3 markers to constitute a signature further stratified NB patients into different risk subgroups can help obtain more accurate prediction performance. Survival prognostic power of age and Msi1_ID1_PCNA signature by receiver operating characteristics analysis showed that this signature predicted more effectively and sensitively compared with classic risk stratification system, compensating for the deficiency of the prediction function of the age. Furthermore, we validated the expressions of these 3 proteins in neuroblastic tumor spectrum tissues by immunohistochemistry revealed that Msi1 and PCNA exhibited increased expression in NB compared with intermedial ganglioneuroblastoma and benign ganglioneuroma, whereas ID1 levels were reduced in NB. In conclusion, we established a robust risk assessment predictor model based on simple immunohistochemistry for therapeutic decisions of NB patients.
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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 | 0.001 |
| 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".