Epigenetics of Triple-Negative Breast Cancer via Natural Compounds
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) is a highly resistant, lethal, and metastatic sub-division of breast carcinoma, characterized by the deficiency of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). In women, TNBC shows a higher aggressive behavior with poor patient prognosis and a higher recurrence rate during reproductive age. TNBC is defined by the presence of epithelial- to-mesenchymal-transition (EMT), which shows a significant role in cancer progression. At the epigenetic level, TNBC is characterized by epigenetic signatures, such as DNA methylation, histone remodeling, and a host of miRNA, MiR-193, LncRNA, HIF- 2α, eEF2K, LIN9/NEK2, IMP3, LISCH7/TGF-β1, GD3s, KLK12, mediated regulation. These modifications either are silenced or activate the necessary genes that are prevalent in TNBC. The review is based on epigenetic mediated mechanistic changes in TNBC. Furthermore, Thymoquinone (TQ), Regorafenib, Fangjihuangqi decoction, Saikosaponin A, and Huaier, etc., are potent antitumor natural compounds extensively reported in the literature. Further, the review emphasizes the role of these natural compounds in TNBC and their possible epigenetic targets, which can be utilized as a potential therapeutic strategy in the treatment of TNBC.
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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) | 0.002 | 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".