Genome-Wide Analysis in Human Colorectal Cancer Cells Reveals Ischemia-Mediated Expression of Motility Genes via DNA Hypomethylation
Bibliographic record
Abstract
DNA hypomethylation is an important epigenetic modification found to occur in many different cancer types, leading to the upregulation of previously silenced genes and loss of genomic stability. We previously demonstrated that hypoxia and hypoglycaemia (ischemia), two common micro-environmental changes in solid tumours, decrease DNA methylation through the downregulation of DNMTs in human colorectal cancer cells. Here, we utilized a genome-wide cross-platform approach to identify genes hypomethylated and upregulated by ischemia. Following exposure to hypoxia or hypoglycaemia, methylated DNA from human colorectal cancer cells (HCT116) was immunoprecipitated and analysed with an Affymetrix promoter array. Additionally, RNA was isolated and analysed in parallel with an Affymetrix expression array. Ingenuity pathway analysis software revealed that a significant proportion of the genes hypomethylated and upregulated were involved in cellular movement, including PLAUR and CYR61. A Matrigel invasion assay revealed that indeed HCT116 cells grown in hypoxic or hypoglycaemic conditions have increased mobility capabilities. Confirmation of upregulated expression of cellular movement genes was performed with qPCR. The correlation between ischemia and metastasis is well established in cancer progression, but the molecular mechanisms responsible for this common observation have not been clearly identified. Our novel data suggests that hypoxia and hypoglycaemia may be driving changes in DNA methylation through downregulation of DNMTs. This is the first report to our knowledge that provides an explanation for the increased metastatic potential seen in ischemic cells; i.e. that ischemia could be driving DNA hypomethylation and increasing expression of cellular movement genes.
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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 | 0.001 | 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) | 0.001 | 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".