Antisense Inhibition of Methylenetetrahydrofolate Reductase Reduces Cancer Cell Survival <i>In vitro</i> and Tumor Growth <i>In vivo</i>
Bibliographic record
Abstract
PURPOSE: Many cancer lines are methionine dependent and decrease proliferation when methionine supply is limited. Methylenetetrahydrofolate reductase (MTHFR) generates the folate derivative for homocysteine remethylation to methionine. We investigated the effect of antisense-mediated inhibition of MTHFR on survival of human cancer cells. EXPERIMENTAL DESIGN: We examined the in vitro and in vivo anticancer effects of a combination of MTHFR antisense and standard cytotoxic drugs. RESULTS: Specific antisense against MTHFR (EX5) showed significant inhibitory effects on growth of human colon, lung, breast, prostate, and neuroblastoma tumor cells in vitro compared with that of the control oligonucleotide. Cytotoxic drugs (5-fluorouracil, cisplatin, or paclitaxel) potentiated the effect of EX5. In vivo, antisense alone or in combination with cytotoxic drugs inhibited the growth of human colon and lung carcinoma xenografts. In comparison with control oligonucleotide, treatment with EX5 inhibited growth of colon tumors and lung tumors by 60% and 45%, respectively. EX5 with 5-fluorouracil decreased growth of colon tumors by an additional 30% compared with EX5 alone, and EX5 with cisplatin decreased growth of lung tumors by an additional 40% compared with cisplatin alone. Growth inhibition by EX5 was associated with decreased amounts of MTHFR protein and with increased amounts of an apoptosis marker. CONCLUSIONS: Our results confirm that MTHFR inhibition decreases tumor growth and suggest that inhibition of MTHFR by antisense or small molecules may be a novel anticancer approach.
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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".