DNA methylation signatures of breast cancer in peripheral T-cells
Bibliographic record
Abstract
BACKGROUND: Immune surveillance acts as a defense mechanism in cancer, and its disruption is involved in cancer progression. DNA methylation reflects the phenotypic identity of cells and recent data suggested that DNA methylation profiles of T cells and peripheral blood mononuclear cells (PBMC) are altered in cancer progression. METHODS: We enrolled 19 females with stage 1 and 2, nine with stage 3 and 4 and 9 age matched healthy women. T cells were isolated from peripheral blood and extracted DNA was subjected to Illumina 450 K DNA methylation array analysis. Raw data was analyzed by BMIQ, ChAMP and ComBat followed by validation of identified genes by pyrosequencing. RESULTS: Analysis of data revealed ~ 10,000 sites that correlated with breast cancer progression and established a list of 89 CG sites that were highly correlated (p < 0.01, r > 0.7, r < - 0.7) with breast cancer progression. The vast majority of these sites were hypomethylated and enriched in genes with functions in the immune system. CONCLUSIONS: The study points to the possibility of using DNA methylation signatures as a noninvasive method for early detection of breast cancer and its progression which need to be tested in clinical studies.
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 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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".