Probing the mechanism of action (MOA) of Solanum nigrum Linn against breast cancer using network pharmacology and molecular docking
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Solanum nigrum Linn is a medicinal herb widely used in traditional Chinese medicine to treat ailments such as fever, inflammation and cancer. Although quite a few compounds have been isolated and characterized, its anticancer mechanism remains elusive. Thus, in this study, we used network pharmacology and molecular docking strategies to identify the major active ingredients in S. nigrum and reveal its putative mechanism against human breast cancer. Six compounds, quercetin, cholesterol, 3-epi-beta-sitosterol, diosgenin, medioresinol and solanocapsine, were identified to be the major active ingredients. Target identification and analysis showed that they regulate 80 breast cancer-related targets. Furthermore, network analysis showed that the six active ingredients regulate multiple pathways including ErbB signaling pathway and estrogen signaling pathway and genes AKT1 (AKT serine/threonine kinase 1), ESR1 (estrogen receptor 1), EGFR (epidermal growth factor receptor), SRC (proto-oncogene tyrosine-protein kinase Src), AR (androgen receptor) and MMP9 (matrix metalloproteinase 9) are crucial genes involved in the regulations. Molecular docking implied that quercetin could form good binding with AKT1, EGFR, SRC and MMP9. Our current study suggests that the anticancer function of S. nigrum is likely via synergistic/additive effects of multiple active ingredients’ regulations of different signaling pathways. Further studies are warranted to establish the standard for S. nuigrum to be used as a CAM (complementary and alternative medicine) in breast cancer treatment and dissect its potential interactions with chemotherapy drugs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it