Targeting Tumor Ubiquitin-Proteasome Pathway with Polyphenols for Chemosensitization
Bibliographic record
Abstract
The development of tumor drug resistance is one of the biggest obstacles on the way to achieve a favorable outcome of chemotherapy. Among various strategies that have been explored to overcome drug resistance, the combination of current chemotherapy with plant polyphenols as a chemosensitizer has emerged as a promising one. Plant polyphenols are a group of phytochemicals characterized by the presence of more than one phenolic group. Mechanistic studies suggest that polyphenols have multiple intracellular targets, one of which is the proteasome complex. The proteasome is a proteolytic enzyme complex responsible for intracellular protein degradation and has been shown to play an important role in tumor growth and the development of drug resistance. Therefore, proteasome inhibition by plant polyphenols could be one of the mechanisms contributing to their chemosensitizing effect. Plant polyphenols that have been identified to possess proteasome-inhibitory activity include (-)-epigallocatechins-3-gallate (EGCG), genistein, luteolin, apigenin, chrysin, quercetin, curcumin and tannic acid. These polyphenols have exhibited an appreciable effect on overcoming resistance to various chemotherapeutic drugs as well as multidrug resistance in a broad spectrum of tumors ranging from carcinoma and sarcoma to hematological malignances. The in vitro and in vivo studies on polyphenols with proteasome-inhibitory activity have built a solid foundation to support the idea that they could serve as a chemosensitizer for the treatment of cancer. In-depth mechanistic studies and identification of optimal regimen are needed in order to eventually translate this laboratory concept into clinical trials to actually benefit current chemotherapy.
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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 | 0.001 |
| 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".