Early-stage lung adenocarcinoma affects DNA methylation and gene expression in adjacent tissues
Bibliographic record
Abstract
The impact of early-stage tumors on gene expression in adjacent tissues remains uncertain, despite the known influence of the tumor microenvironment on tumor progression. Here, we systematically analyze early-stage lung adenocarcinoma (LUAD) and surrounding tissues across multiple distinct regions, from the tumor core to distant tissues. DNA methylation profiling in a 12-patient cohort reveals two distinct patterns of methylation changes. Steep changes occurring at the tumor boundary and shallow changes showing a gradual shift over increasing distance to the tumor. Approximately 17,000 CpG sites demonstrate shallow changing trends without clear boundaries, potentially affecting 2655 genes. In half of the patients, tissues within 10 mm beyond the tumor show methylation patterns similar to tumors. We test mRNA expression of key genes affected by these methylation patterns and observe that the protein expression pattern of WNT7B demonstrates no steep changes at the tumor boundary, supporting their regulatory role. Adding a 59-patient four-year-prognosis cohort allowed us to rigorously assess the clinical relevance of these methylation change trends. These shallow changes reflect tumor characteristics and have the potential for prognostic prediction in patients, warranting further investigation.
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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 | 0.000 |
| 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) | 0.001 | 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".