Expression of p33ING1 mRNA and chemosensitivity in brain tumor cells.
Bibliographic record
Abstract
BACKGROUND: Mutations and down-regulation of tumor suppressor genes can contribute to both tumorigenesis and chemotherapy resistance. The tumor suppressor p33ING1 has growth-inhibitory and pro-apoptotic effects recruiting p53 and it plays a role in DNA repair through interaction with PCNA. We questioned whether p33ING1 mRNA expression correlates with the chemosensitivity of brain tumor cells. MATERIALS AND METHODS: Various malignant brain tumor cell lines were examined for their sensitivity to cisplatin, doxorubicin, etoposide and the antimitotic agents vincristine and paclitaxel by MTT-cytotoxicity assays. p33ING1 mRNA expression was determined by RT-PCR. RESULTS: We found that, unlike other tumor types, ING1 levels were higher in glioma cell lines than in normal control cells. Medulloblastoma cells revealed the lowest ING1 expression of the lines tested. Comparing all cell lines, p33ING1 gene expression significantly (p = 0.028) correlated with resistance to vincristine (r2 = 0.87). CONCLUSION: Our results suggest that p33ING1 mRNA levels may be used to predict the chemosensitivity of brain tumor cells to vincristine.
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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".