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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it