Y disruption, autosomal hypomethylation and poor male lung cancer survival
Bibliographic record
Abstract
Abstract Lung cancer is the most frequent cause of cancer death worldwide. It affects more men than women, and men generally have worse survival outcomes. We compared gene co-expression networks in affected and unaffected lung tissue from 126 consecutive patients with Stage IA–IV lung cancer undergoing surgery with curative intent. We observed marked degradation of a sex-associated transcription network in tumour tissue. This disturbance, detected in 27.7% of male tumours in the discovery dataset and 27.3% of male tumours in a further 123-sample replication dataset, was coincident with partial losses of the Y chromosome and extensive autosomal DNA hypomethylation. Central to this network was the epigenetic modifier and regulator of sexually dimorphic gene expression, KDM5D . After accounting for prognostic and epidemiological covariates including stage and histology, male patients with tumour KDM5D deficiency showed a significantly increased risk of death (Hazard Ratio [HR] 3.80, 95% CI 1.40–10.3, P = 0.009). KDM5D deficiency was confirmed as a negative prognostic indicator in a further 1100 male lung tumours (HR 1.67, 95% CI 1.4–2.0, P = 1.2 × 10 –10 ). Our findings identify tumour deficiency of KDM5D as a prognostic marker and credible mechanism underlying sex disparity in lung cancer.
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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".