High Expressions of Neuronatin Isoforms in Favorable Neuroblastoma
Bibliographic record
Abstract
Neuroblastoma is a malignant solid tumor of children, which derives from the embryonal sympathoadrenal linage. Clinical cases can vary widely from a favorable to an unfavorable prognosis according to the presence of genetic aberrations, such as MYCN amplification. Our cDNA microarray analysis which compared the gene expressions between favorable and unfavorable neuroblastomas showed a high expression of the neuronatin (Nnat) gene in favorable neuroblastomas. Nnat is highly conserved in mammalian species, and its expression appears in nervous systems from the hindbrain to the peripherals during the prenatal periods. The Nnat mRNA expression, investigated in 63 of neuroblastoma samples by quantitative reverse-transcription polymerase chain reaction, was found to be significantly higher in the favorable prognosis groups than in the unfavorable groups. Nnat is an imprinted gene, and its expression in IMR32 neuroblastoma cell line was up-regulated by treatment with a demethylating agent. High expressions of Nnat isoforms induced in an IMR32 neuroblastoma cell line changed the cell morphology to the extension of the neural processes, which thus indicated the occurrence of cell differentiation. In conclusion, the high expressions of Nnat were found to be associated with good prognoses in neuroblastoma, which might indicate tumor differentiation, and its suppressions in unfavorable tumors are considered to be under epigenetic control.
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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 | 0.000 | 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".