Autophagy-driven regulation of cisplatin response in human cancers: Exploring molecular and cell death dynamics
Bibliographic record
Abstract
Despite the challenges posed by drug resistance and side effects, chemotherapy remains a pivotal strategy in cancer treatment. A key issue in this context is macroautophagy (commonly known as autophagy), a dysregulated cell death mechanism often observed during chemotherapy. Autophagy plays a cytoprotective role by maintaining cellular homeostasis and recycling organelles, and emerging evidence points to its significant role in promoting cancer progression. Cisplatin, a DNA-intercalating agent known for inducing cell death and cell cycle arrest, often encounters resistance in chemotherapy treatments. Recent studies have shown that autophagy can contribute to cisplatin resistance or insensitivity in tumor cells through various mechanisms. This resistance can be mediated by protective autophagy, which suppresses apoptosis. Additionally, autophagy-related changes in tumor cell metastasis, particularly the induction of Epithelial-Mesenchymal Transition (EMT), can also lead to cisplatin resistance. Nevertheless, pharmacological strategies targeting the regulation of autophagy and apoptosis offer promising avenues to enhance cisplatin sensitivity in cancer therapy. Notably, numerous non-coding RNAs have been identified as regulators of autophagy in the context of cisplatin chemotherapy. Thus, therapeutic targeting of autophagy or its associated pathways holds potential for restoring cisplatin sensitivity, highlighting an important direction for future clinical research.
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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 | 0.001 |
| 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".