Induction of DNA Hypomethylation by Tumor Hypoxia
Bibliographic record
Abstract
In cancer, the extensive methylation found in the bulk of chromatin is reduced, while the normally unmethylated CpG islands become hypermethylated. Regions of solid tumors are transiently and/or chronically exposed to ischemia (hypoxia) and reperfusion, conditions known to contribute to cancer progression. We hypothesized that hypoxic microenvironment may influence local epigenetic alterations, leading to inappropriate silencing and re-awakening of genes involved in cancer. We cultured human colorectal and melanoma cancer cell lines under severe hypoxic conditions, and examined their levels of global methylation using HPLC to quantify 5-methylcytosine (5-mC), and found that hypoxia induced losses of global methylation. This was more extensive in normal human fibroblasts than cancer cell lines. Cell lines from metastatic colorectal carcinoma or malignant melanoma were found to be markedly more hypomethylated than cell lines from their respective primary lesions, but they did not show further reduction of 5-mC levels under hypoxic conditions. To explore these epigenetic changes in vivo, we established xenografts of the same cancer cells in immune deficient mice. We used Hypoxyprobe to assess the magnitude of tissue hypoxia, and immunostaining for 5-mC to evaluate DNA methylation status in cells from different regions of tumors. We found an inverse relationship between the presence of extensive tumor hypoxia and the incidence of methylation, and a reduction of 5-mC in xenografts compared to the levels seen in the same cancer cell lines in vitro, verifying that methylation patterns are also modulated by hypoxia in vivo. This suggests that epigenetic events in solid tumors may be modulated by microenvironmental conditions such as hypoxia.
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 | 0.000 |
| 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".