Transcriptomic biomarkers related to cardiac disease in childhood cancer survivors: a case-control study
Bibliographic record
Abstract
BACKGROUND AND AIMS: Cardiotoxic treatments like anthracyclines and heart-directed radiotherapy increase the risk of cardiac diseases (CDs) in childhood cancer survivors (CCSs), but individual differences in CD incidence are not fully understood. This study aims to identify transcriptomic biomarkers associated with CD occurrence after childhood cancer treatment. METHODS: A matched case-control study was conducted on a sample of 330 CCS: 165 cases with CD and 165 CD-free controls. The expression of 8557 genes was investigated to select those associated with CD and heart failure (HF), using three stabilization approaches derived for the conditional logistic regression with Lasso (Percentile lasso, Bolasso, and Sublasso). The intersection of the three selected gene sets formed the final selection. The interactions between cancer treatment doses and selected genes were investigated. RESULTS: One promising gene, NFE2L2, constituted the final selection, and its expression was lower in cases than in controls [CD: odds ratio (OR) .16, 95% confidence interval (CI) .09-.29; HF: OR .11, 95% CI .03-.37]. No interaction between treatment doses and NFE2L2 expression levels was found in our study. Incorporating NFE2L2 gene expression into prognostic models improved discrimination between cases and controls compared with models based solely on clinical and treatment variables [CD: area under the curve (AUC) .85 vs .66; HF: AUC .87 vs .77]. CONCLUSIONS: Using high-dimensional data selection methods has enabled the identification of the gene NFE2L2, associated with CD and HF in CCS. Further research is needed to validate this finding and achieve a better understanding of the biological mechanisms leading to cardiac toxicities and so to develop risk-adapted treatment and surveillance strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".