Global Hypomethylation of Genomic DNA in Cancer-Associated Myofibroblasts
Bibliographic record
Abstract
Global hypomethylation has long been recognized as a feature of the malignant epithelial component in human carcinomas. Here we show evidence for this same type of epigenetic alteration in cancer-associated stromal myofibroblasts. We used methylation-sensitive SNP array analysis (MSNP) to profile DNA methylation in early-passage cultures of stromal myofibroblasts isolated from human gastric cancers. The MSNP data indicated widespread hypomethylation in these cells, with rare focal gains of methylation, conclusions that were independently validated by bisulfite sequencing and by a methylation-sensitive cytosine incorporation assay. Immunohistochemistry with anti-5-methylcytosine (anti-5-methyl-C) in a series of gastrectomy specimens showed frequent loss of methylation in nuclei of both the malignant epithelial cells and alpha-smooth muscle actin (ASMA)-positive stromal myofibroblasts of both intestinal-type and diffuse carcinomas. We confirmed this phenomenon and established its onset at the stage of noninvasive dysplastic lesions by immunohistochemistry for anti-5-methyl-C in a transgenic mouse model of multistage gastric carcinogenesis. These findings indicate similar general classes of epigenetic alterations in carcinoma cells and their accompanying reactive stromal cells and add to accumulating evidence for biological differences between normal and cancer-associated myofibroblasts.
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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 | 0.000 |
| 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) | 0.001 | 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".