Inhibition of nuclear factor-κB by dehydroxymethylepoxyquinomicin induces schedule-dependent chemosensitivity to anticancer drugs and enhances chemoinduced apoptosis in osteosarcoma cells
Bibliographic record
Abstract
Osteosarcoma (OS) is the most common primary malignant bone tumor, usually developing in children and adolescents, and is highly invasive and metastatic, potentially developing chemoresistance. Thus, novel effective treatment regimens are urgently needed. This study was the first to investigate the anticancer effects of dehydroxymethylepoxyquinomicin (DHMEQ), a highly specific nuclear factor-κB (NF-κB) inhibitor, on the OS cell lines HOS and MG-63. We demonstrate that NF-κB blockade by DHMEQ inhibits proliferation, decreases the mitotic index, and triggers apoptosis of OS cells. We examined the effects of combination treatment with DHMEQ and cisplatin, doxorubicin, or methotrexate, drugs commonly used in OS treatment. Using the median effect method of Chou and Talalay, we evaluated the combination indices for simultaneous and sequential treatment schedules. In all cases, combination with a chemotherapeutic drug produced a synergistic effect, even at low single-agent cytotoxic levels. When cells were treated with DHMEQ and cisplatin, a more synergistic effect was obtained using simultaneous treatment. For the doxorubicin and methotrexate combination, a more synergistic effect was achieved with sequential treatment using DHMEQ before chemotherapy. These synergistic effects were accompanied by enhancement of chemoinduced apoptosis. Interestingly, the highest apoptotic effect was reached with sequential exposure in both cell lines, independent of the chemotherapeutic agent used. Likewise, DHMEQ decreased cell invasion and migration, crucial steps for tumor progression. Our data suggest that combining DHMEQ with chemotherapeutic drugs might be useful for planning new therapeutic strategies for OS treatment, mainly in resistant and metastatic cases.
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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".