The Potential of Natural Products in the Treatment of Triple-negative Breast Cancer
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) is a subtype of breast cancer that lacks receptors for targeted therapy. Consequently, chemotherapy is currently the mainstay of systemic treatment options. However, the enrichment of cancer stem cells (CSC, a subpopulation with stem-cell characteristics and tumor-initiating propensity) promotes chemo-resistance and tumorigenesis, resulting in cancer recurrence and relapse. Furthermore, toxic side effects of chemotherapeutics reduce patient wellbeing. Natural products specifically compounds derived from plants, have the potential to treat TNBC and target CSCs by inhibiting CSC signaling pathways. Literature evidence from six promising compounds was reviewed, including sulforaphane, curcumin, genistein, resveratrol, lycopene, and epigallocatechin-3-gallate. These compounds have been shown to promote cell cycle arrest and apoptosis in TNBC cells. They also could inhibit the epithelial-mesenchymal transition (EMT) that plays an important role in metastasis. In addition, those natural compounds have been found to inhibit pathways important for CSCs, such as NF-κB, PI3K/Akt/mTOR, Notch 1, Wnt/β- catenin, and YAP. Clinical trials conducted on these compounds have shown varying degrees of effectiveness. Epidemiological case-control studies for the compounds commonly consumed in certain human populations have also been summarized. While in vivo and in vitro data are promising, further basic and clinical investigations are required. Likely, natural products in combination with other drugs may hold great potential to improve TNBC treatment efficacy and patient outcomes.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".