γ-Glutamyl hydrolase modulation and folate influence chemosensitivity of cancer cells to 5-fluorouracil and methotrexate
Bibliographic record
Abstract
BACKGROUND: γ-Glutamyl hydrolase (GGH) regulates intracellular folate and antifolates for optimal nucleotide biosynthesis and antifolate-induced cytotoxicity, respectively. The modulation of GGH may therefore affect chemosensitivity of cancer cells, and exogenous folate levels may further modify this effect. METHODS: We generated a novel model of GGH modulation in human HCT116 and MDA-MB-435 cancer cells and investigated the effect of GGH modulation on chemosensitivity to 5-fluorouracil (5FU) and methotrexate (MTX) at different folate concentrations in vitro and in vivo. RESULTS: Overexpression of GGH significantly decreased chemosensitivity of MDA-MB-435 cells to 5FU and MTX at all folate concentrations as expected. In contrast, in HCT116 cells this predicted effect was observed only at very high folate concentration, and as the folate concentration decreased this effect became null or paradoxically increased. This in vitro observation was confirmed in vivo. Inhibition of GGH significantly increased chemosensitivity of both cancer cells to 5FU at all folate concentrations. Unexpectedly, GGH inhibition significantly decreased chemosensitivity of both cancer cells to MTX at all folate concentrations. In both GGH modulation systems and cell lines, the magnitude of chemosensitivity effect incrementally increased as folate concentration increased. CONCLUSION: Modulation of GGH affects chemosensitivity of cancer cells to 5FU and MTX, and exogenous folate levels can further modify the effects.
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".