Induction of 1C aldoketoreductases and other drug dose-dependent genes upon acquisition of anthracycline resistance
Bibliographic record
Abstract
OBJECTIVES: Recent studies suggest that tumor cells overexpressing aldoketoreductases (AKRs) exhibit increased resistance to DNA damaging agents such as anthracyclines. AKRs may induce resistance to the anthracycline doxorubicin by catalyzing its conversion to the less toxic 13-hydroxy metabolite doxorubicinol. However, it has not been established whether during selection for anthracycline resistance, AKR overexpression in tumor cells can be correlated with the onset or magnitude of drug resistance and with appreciable conversion of anthracyclines to 13-hydroxy metabolites. METHODS AND FINDINGS: Through microarray and quantitative polymerase chain reaction studies involving rigid selection criteria and both correlative discriminate statistics and time-course models, we have identified several genes whose expression can be correlated with the onset and/or magnitude of anthracycline resistance, including AKR1C2 and AKR1C3. Also associated with the onset or magnitude of anthracycline resistance were genes involved in drug transport (ABCB1, ABCC1), cell signaling and transcription (RDC1, CXCR4), cell proliferation or apoptosis (BMP7, CAV1), protection from reactive oxygen species (AKR1C2, AKR1C3, FTL, FTH, TXNRD1, MT2A), and structural or immune system proteins (IFI30, STMN1). As expected, doxorubicin-resistant and epirubicin-resistant cells exhibited higher levels of doxorubicinol than wild-type cells, although at insufficient levels to account for significant drug resistance. Nevertheless, an inhibitor of Akr1c2 (5beta-cholanic acid) almost completely restored sensitivity to doxorubicin in ABCB1-deficient doxorubicin-resistant cells, while having no effect on ABCB1-expressing epirubicin-resistant cells. CONCLUSION: Taken together, we show for the first time that a variety of genes (particularly redox genes such as AKR1C2 and AKR1C3) can be temporally and causally correlated with the acquisition of anthracycline resistance in breast tumor cells.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".