Aberrant Methylation of <i>RASSF1A</i> in Plasma DNA Before Breast Cancer Diagnosis in the Breast Cancer Family Registry
Bibliographic record
Abstract
In addition to classic genetic mechanisms such as deletions and mutations, growth regulatory genes can be inactivated via methylation of cytosine-residues in their promoter regions. Hypermethylation of promoter CpG islands is now recognized as an important and early event in carcinogenesis. Detection of methylated DNA in serum or plasma has been suggested to be a marker for early cancer development. We examined methylation changes in RASSF1A, a growth regulatory gene in plasma DNA from blood collected before diagnosis from women with breast cancer and from controls. Samples were from two sets of subjects, 28 women with breast cancer and 10 of their unaffected siblings, and 33 women with breast cancer and 29 age- and ethnicity-matched population-based controls. Using methylation specific PCR, we found 11 of 61 (18%) cases were positive for methylation of RASSF1A in their plasma DNA collected before diagnosis. Two of 10 healthy high-risk sibling controls (20%) had plasma DNA positive for RASSF1A methylation in their plasma DNA compared with 0 of 29 (0%) population-based controls. Tumor tissue was available for 12 cases and all were positive for RASSF1A methylation. These results, if replicated, suggest that aberrant promoter hypermethylation in serum/plasma DNA may be common among high-risk women and may be present years before cancer 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".