Y disruption, autosomal hypomethylation and poor male lung cancer survival
Bibliographic record
Abstract
Summary Lung cancer is the most frequent cause of cancer death worldwide 1 . It is male predominant and for reasons that are unknown also associated with significantly worse outcomes in men 2 . Here we compared gene co-expression networks in affected and unaffected pulmonary tissue derived from 126 patients with Stage IA–IV lung cancer. We observed marked degradation of a sex-associated gene co-expression network in tumour tissue. The disturbance was linked to fractional loss of the Y chromosome and was detected in 28% of male tumours in the discovery dataset and 27% of male tumours in a 123 sample replication dataset. Depression of Y chromosome expression was accompanied by extensive autosomal DNA hypomethylation. The male specific H3K4 demethylase, KDM5D , was identified as an apex hub within this co-expression network. Male patients exhibiting relative tumour KDM5D deficiency had an increased risk of death in the discovery dataset (Hazard Ratio [HR] 3.80, 95% CI 1.40 – 10.3, P =0.009) and in an independent sample of 1,100 male lung tumours (HR 1.67, 95% CI 1.4-2.0, P =1.2⨯10 −10 ). Our findings identify tumour-specific weakening of male-specific expression, in particular deficiency of KDM5D , as a common replicable prognostic marker and credible mechanism underlying sex disparity in 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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".