<i>AIB1 </i>Genomic Amplification Predicts Poor Clinical Outcomes in Female Glioma Patients
Bibliographic record
Abstract
Amplified in breast cancer 1 (AIB1) gene, a coactivator for steroid receptor, is frequently amplified in diverse cancers and is considered as an oncogene in tumorigenesis.However, the prognostic significance of AIB1 amplification in gliomas remains totally unclear.In this study, 115 gliomas and 16 benign meningiomas as control subjects were enrolled, and the copy number of AIB1 was analyzed in these samples.In addition, we explored potential correlation of AIB1 amplification with clinicopathological characteristics and clinical outcomes of glioma patients.Our data showed that glioma samples exhibited a significantly higher AIB1 copy number than control subjects as determined by quantitative polymerase chain reaction (qPCR) approach.Moreover, univariate analysis showed that AIB1 amplification (≥3.5 copies) was strongly correlated with cancer-related death (P =0.03).Interestingly, our data revealed a significant association of AIB1 amplification with WHO grade (P =0.03), tumor recurrence (P =0.03) and survival status (P =0.03) in female patients but not in male patients.Multivariate analysis further demonstrated that AIB1 amplification was independent factor for cancer-related death in female patients.Importantly, AIB1 amplification was closely relevant to worse survival in female patients (P =0.001), but not in male patients (P =1.00).In addition, the patients with AIB1 amplification were resistant to radiotherapy.Altogether, our data demonstrate that AIB1 amplification is a common genetic event in glioma tumorigenesis, and suggest that AIB1 amplification is not only a prognostic factor for poor clinical outcomes in glioma patients, but also a predictor of radiotherapy resistance in gliomas.
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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 | 0.001 |
| 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) | 0.002 | 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".