Clear Cell Renal Cell Carcinoma with Biallelic Inactivation of CDKN2A/B on 9p21 have Distinct Gene Expression Signature and are Associated with Poor Prognosis
Bibliographic record
Abstract
Abstract Purpose The clinical implications of biallelic inactivation of CDKN2A/B in clear cell renal cell carcinoma (ccRCC) and relevant dysregulated biological pathways and gene signatures were investigated. Materials and Methods Data were obtained from the TCGA data set and validated using Project GENIE and previously published dataset. CDKN2A/B allelic status was classified into 3 groups, including biallelic CDKN2A/B inactivation (homozygous deletion or combined heterozygous deletion and mutation), monoallelic CDKN2A/B loss (heterozygous deletion or mutation) and absent CDKN2A/B allelic loss. Univariate and multivariate cancer-specific survival and disease-free survival analyses were performed. Integrated analyses of copy number, gene expression (mRNA and miRNA), protein expression and methylation changes were conducted. Results Of 440 patients with ccRCC 17 (3.9%) had biallelic CDKN2A/B inactivation and 116 (26.4%) had monoallelic CDKN2A/B loss. CDKN2A/B allelic inactivation was associated with late tumor stage, high histological grade, presence of metastasis and greater tumor size. Patients with biallelic deletion of CDKN2A/B showed significantly worse cancer-specific survival and disease-free survival (p<0.0001). Significant co-occurrence of MTAP homozygous deletion was observed in CDKN2A/B biallic inactivated tumors (46.7%; p<0.001). Significant underexpression of CDKN2A/B was observed in biallelic inactivated tumors at the mRNA and protein levels. miR-21 was the most highly expressed miRNA in biallelic inactivated tumors. Biallelic inactivated tumors were significantly enriched for genes related to activation of ATR in response to replication stress and miR-21 target genes. Conclusions CDKN2A/B biallelic inactivation may be a prognostic marker for ccRCC and is associated with distinct dysregulation of gene expression signatures.
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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 | 0.001 | 0.001 |
| 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".