Microarray‐based survey of CpG islands identifies concurrent hyper‐ and hypomethylation patterns in tissues derived from patients with breast cancer
Bibliographic record
Abstract
Maintenance of CpG island methylation in the genome is crucial for cellular homeostasis and this balance is disrupted in cancer. Our rationale was to compare the methylation of CpG islands in tissues (tumor, healthy breast and blood) from patients with breast cancer. We studied 72 genes in 103 samples using microarray hybridization and bisulfite sequencing. We observed tumor specific hyper- or hypomethylation of five genes; COL9A1, MT1A, MT1J, HOXA5 and FLJ45983. A general drop of methylation in COL9A1 was apparent in tumors, when compared with blood and healthy breast tissue. Furthermore, one tumor displayed a complete loss of methylation of all five genes, suggesting overall impairment of methylation. The downstream, evolutionary conserved island of HOXA5 showed hypomethylation in 18 tumors and complete methylation in others. This CpG island also displayed a semimethylated state in the majority of normal breast samples, when compared to complete methylation in blood. Distinct methylation patterns were further seen in MT1J and MT1A, belonging to the metallothionein gene family. The CpG islands of these genes are spaced by 2 kb, which shows selective methylation of two structurally and functionally related genes. The promoters of FLJ45983 and MT1A were methylated above 25% in 18 primary and metastatic tumors. Concurrently, there was also >10% methylation of healthy breast tissue in 11 and 5 samples, respectively. This suggests that the methylation process for the latter two genes takes place already in normal breast cells. Our results also point to a considerable heterogeneity of epigenetic disturbance in breast cancer. This article contains Supplementary Material available at http://www.interscience.wiley.com/jpages/1045-2257/suppmat.
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".