Quantitative Analysis of DNA Methylation Profiles in Lung Cancer Identifies Aberrant DNA Methylation of Specific Genes and Its Association with Gender and Cancer Risk Factors
Bibliographic record
Abstract
The global increase in lung cancer burden, together with its poor survival and resistance to classical chemotherapy, underscores the need for identification of critical molecular events involved in lung carcinogenesis. Here, we have applied quantitative profiling of DNA methylation states in a panel of five cancer-associated genes (CDH1, CDKN2A, GSTP1, MTHFR, and RASSF1A) to a large case-control study of lung cancer. Our analyses revealed a high frequency of aberrant hypermethylation of MTHFR, RASSF1A, and CDKN2A in lung tumors as compared with control blood samples, whereas no significant increase in methylation levels of GSTP1 and CDH1 was observed, consistent with the notion that aberrant DNA methylation occurs in a tumor-specific and gene-specific manner. Importantly, we found that tobacco smoking, sex, and alcohol intake had a strong influence on the methylation levels of distinct genes (RASSF1A and MTHFR), whereas folate intake, age, and histologic subtype had no significant influence on methylation states. We observed a strong association between MTHFR hypermethylation in lung cancer and tobacco smoking, whereas methylation levels of CDH1, CDKN2A, GSTP1, and RASSF1A were not associated with smoking, indicating that tobacco smoke targets specific genes for hypermethylation. We also found that methylation levels in RASSF1A, but not the other genes under study, were influenced by sex, with males showing higher levels of methylation. Together, this study identifies aberrant DNA methylation patterns in lung cancer and thus exemplifies the mechanism by which environmental factors may interact with key genes involved in tumor suppression and contribute to 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 | 0.001 | 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".