Anti-Breast Cancer Effects of Thymoquinone-Chemotherapeutic Combinations: A Systematic Review of the Latest <i>In Vitro</i> and <i>In Vivo</i> Studies
Bibliographic record
Abstract
Background: Breast cancer is a leading malignancy among women globally, with chemotherapy as a cornerstone of treatment. However, the side effects and toxicity associated with chemotherapy necessitate the exploration of adjunctive therapies to improve efficacy and reduce adverse effects. Thymoquinone (TQ) has shown potential anti-cancer properties. This systematic review aimed to evaluate the effectiveness of TQ in combination with chemotherapeutic agents in treating breast cancer. Methods: This study thoroughly reviewed and synthesized existing research following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. The selected databases, including PubMed, ProQuest, ScienceDirect, Epistemonikos, and Google Scholar, were searched over the past 10 years. Eligibility criteria were based on the PICOS framework, focusing on experimental studies involving TQ-chemotherapy combinations. Data extraction and quality assessment were performed using SYRCLE and SCIRAP tools. This review included 18 in vitro and six in vivo studies. Results: Findings revealed that TQ enhances the efficacy of chemotherapeutic agents by inducing apoptosis, enhancing autophagy, inhibiting tumor growth, and regulating cancer cell signaling pathways as well as multiple phases of the cell cycle. Additionally, TQ reduced chemotherapy-related toxicity, such as heart, blood, liver, and kidney damage, and also improved patient tolerance. Nanoparticle-based delivery systems further amplified these synergistic effects. Conclusions: The TQ-chemotherapy combination shows significant potential as a therapy for breast cancer, enhancing treatment efficacy while mitigating side effects. Future clinical studies are needed to establish its safety and therapeutic applicability.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".