Acquisition of Doxorubicin Resistance Induces Breast Cancer Cell Migration and Epithelial-Mesenchymal Transition that are Reversed by Shikonin-Metformin Synergy
Bibliographic record
Abstract
Background: Drug resistance is a major challenge in cancer chemotherapy. Methods: By adopting an appropriately timed strategy, we generated MCF-7 cell sublines resistant to serial doses of doxorubicin (DOX). Our higher-dose sublines showed more stability in resistance and were, therefore, subjected to further analyses. We tested the consistency of drug resistance by comparing sublines with control groups for growth and migration capacities. Molecular analyses monitored expression changes, CD44/CD24 ratios, and DOX binding to key molecules. The reverting impact of shikonin (SHKN) and metformin (MTFN) on DOX resistance was examined. Results. The resistant sublines grew parallel to or even faster than WT MCF-7 cells and showed a larger and more rounded morphology. The consistency of their drug resistance and invasive potential was demonstrated over time using serial doses of DOX. Real-time PCR revealed upregulation of genes involved in cell growth and survival, drug resistance, migration/invasion, and epithelial-mesenchymal transition and, conversely, downregulation of pro-apoptotic, anti-chemoresistance, and tumor suppressor genes. SHKN-MTFN co-treated resistant cells showed significantly lower CD44/CD24 ratios, less aggressiveness, and reduced survival and migration rates but enhanced apoptosis. SHKN’s affinity to CYP1A and TOP2A demonstrated the importance of these interactions and the compounds’ capacity to compete with DOX. Conclusion: Acquisition of DOX resistance increases tumorigenic properties of cancer cells, whereas synergy between selective anti-tumorigenic compounds re-sensitizes resistant cells by reverting cellular pathways that favor or follow resistance. Our findings suggest that this reversal is supported by competing reactions that deprive DOX of binding to its target molecules.
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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".