Decitabine impact on the endocytosis regulator RhoA, the folate carriers RFC1 and FOLR1, and the glucose transporter GLUT4 in human tumors
Bibliographic record
Abstract
BACKGROUND: In 31 solid tumor patients treated with the demethylating agent decitabine, we performed tumor biopsies before and after the first cycle of decitabine and used immunohistochemistry (IHC) to assess whether decitabine increased expression of various membrane transporters. Resistance to chemotherapy may arise due to promoter methylation/downregulation of expression of transporters required for drug uptake, and decitabine can reverse resistance in vitro. The endocytosis regulator RhoA, the folate carriers FOLR1 and RFC1, and the glucose transporter GLUT4 were assessed. RESULTS: Pre-decitabine RhoA was higher in patients who had received their last therapy >3 months previously than in patients with more recent prior therapy (P = 0.02), and varied inversely with global DNA methylation as assessed by LINE1 methylation (r = -0.58, P = 0.006). Tumor RhoA scores increased with decitabine (P = 0.03), and RFC1 also increased in patients with pre-decitabine scores ≤150 (P = 0.004). Change in LINE1 methylation with decitabine did not correlate significantly with change in IHC scores for any transporter assessed. We also assessed methylation of the RFC1 gene (alias SLC19A1). SLC19A1 methylation correlated with tumor LINE1 methylation (r = 0.45, P = 0.02). There was a small (statistically insignificant) decrease in SLC19A1 methylation with decitabine, and there was a trend towards change in SLC19A1 methylation with decitabine correlating with change in LINE1 methylation (r = 0.47, P <0.15). While SLC19A1 methylation did not correlate with RFC1 scores, there was a trend towards an inverse correlation between change in SLC19A1 methylation and change in RFC1 expression (r = -0.45, P = 0.19). CONCLUSIONS: In conclusion, after decitabine administration, there was increased expression of some (but not other) transporters that may play a role in chemotherapy uptake. Larger patient numbers will be needed to define the extent to which this increased expression is associated with changes in DNA methylation.
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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".