Inhibition of Autophagy Promotes the Elimination of Liver Cancer Stem Cells by CD133 Aptamer-Targeted Delivery of Doxorubicin
Bibliographic record
Abstract
Doxorubicin is the most frequently used chemotherapeutic agent for the treatment of hepatocellular carcinoma. However, one major obstacle to the effective management of liver cancer is the drug resistance derived from the cancer stem cells. Herein, we employed a CD133 aptamer for targeted delivery of doxorubicin into liver cancer stem cells to overcome chemoresistance. Furthermore, we explored the efficacy of autophagy inhibition to sensitize liver cancer stem cells to the treatment of CD133 aptamer-doxorubicin conjugates based on the previous observation that doxorubicin contributes to the survival of liver cancer stem cells by activating autophagy. The kinetics and thermodynamics of aptamer-doxorubicin binding, autophagy induction, cell apoptosis, and self-renewal of liver cancer stem cells were studied using isothermal titration calorimetry, Western blot analysis, annexin V assay, and tumorsphere formation assay. The aptamer-cell binding andintracellular accumulation of doxorubicin were quantified via flow cytometry. CD133 aptamer-guided delivery of doxorubicin resulted in a higher doxorubicin concentration in the liver cancer stem cells. The combinatorial treatment strategy of CD133 aptamer-doxorubicin conjugates and an autophagy inhibitor led to an over 10-fold higher elimination of liver cancer stem cells than that of free doxorubicin in vitro. Future exploration of cancer stem cell-targeted delivery of doxorubicin in conjunction with autophagy inhibition in vivo may well lead to improved outcomes in the treatment of hepatocellular carcinoma.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".