Agnostic Cys34‐albumin adductomics and DNA methylation: Implication of N‐acetylcysteine in lung carcinogenesis years before diagnosis
Bibliographic record
Abstract
Although smoking and oxidative stress are known contributors to lung carcinogenesis, their mechanisms of action remain poorly understood. To shed light into these mechanisms, we applied a novel approach using Cys34‐adductomics in a lung cancer nested case–control study (n = 212). Adductomics profiles were integrated with DNA‐methylation data at established smoking‐related CpG sites measured in the same individuals. Our analysis identified 42 Cys34‐albumin adducts, of which 2 were significantly differentially abundant in cases and controls: adduct of N‐acetylcysteine (NAC, p = 4.15 × 10−3) and of cysteinyl‐glycine (p = 7.89 × 10−3). Blood levels of the former were found associated to the methylation levels at 11 smoking‐related CpG sites. We detect, for the first time in prospective blood samples, and irrespective of time to diagnosis, decreased levels of NAC adduct in lung cancer cases. Altogether, our results highlight the potential role of these adducts in the oxidative stress response contributing to lung carcinogenesis years before diagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".