miR-26b enhances the sensitivity of hepatocellular carcinoma to Doxorubicin via USP9X-dependent degradation of p53 and regulation of autophagy
Why this work is in the frame
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Bibliographic record
Abstract
Multi-drug resistance is a major challenge to hepatocellular carcinoma (HCC) treatment, and the over-expression or deletion of microRNA (miRNA) expression is closely related to the drug-resistant properties of various cell lines. However, the underlying molecular mechanisms remain unclear. CCK-8, EdU, flow cytometry, and transmission electron microscopy were performed to determine cell viability, proliferation, apoptosis, autophagic flow, and nanoparticle characterization, respectively. In this study, the results showed that the expression of miR-26b was downregulated following doxorubicin treatment in human HCC tissues. An miR-26b mimic enhanced HCC cell doxorubicin sensitivity, except in the absence of p53 in Hep3B cells. Delivery of the proteasome inhibitor, MG132, reversed the inhibitory effect of miR-26b on the level of p53 following doxorubicin treatment. Tenovin-1 (an MDM2 inhibitor) protected p53 from ubiquitination-mediated degradation only in HepG2 cells with wild type p53. Tenovin-1 pretreatment enhanced HCC cell resistance to doxorubicin when transfected with an miR-26b mimic. Moreover, the miR-26b mimic inhibited doxorubicin-induced autophagy and the autophagy inducer, rapamycin, eliminated the differences in the drug sensitivity effect of miR-26b. In vivo, treatment with sp94dr/miR-26b mimic nanoparticles plus doxorubicin inhibited tumor growth. Our current data indicate that miR-26b enhances HCC cell sensitivity to doxorubicin through diminishing USP9X-mediated p53 de-ubiquitination caused by DNA damaging drugs and autophagy regulation. This miRNA-mediated pathway that modulates HCC will help develop novel therapeutic strategies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it