Garlic-derived compound S-allylmercaptocysteine inhibits hepatocarcinogenesis through targeting LRP6/Wnt pathway
Bibliographic record
Abstract
Whether and how garlic-derived S-allylmercaptocysteine (SAMC) inhibits hepatocellular carcinoma (HCC) is largely unknown. In the current study, the role of low-density lipoprotein receptor (LDLR)-related protein 6 (LRP6) in HCC progression and the anti-HCC mechanism of SAMC was examined in clinical sample, cell model and xenograft/orthotopic mouse models. We demonstrated that SAMC inhibited cell proliferation and tumorigenesis, while induced apoptosis of human HCC cells without influencing normal hepatocytes. SAMC directly interacted with Wnt-pathway co-receptor LRP6 on the cell membrane. LRP6 was frequently over-expressed in the tumor tissue of human HCC patients (66.7% of 48 patients) and its over-expression only correlated with the over-expression of β-catenin, but not with age, gender, tumor size, stage and metastasis. Deficiency or over-expression of LRP6 in hepatoma cells could partly mimic or counteract the anti-tumor properties of SAMC, respectively. In vivo administration of SAMC significantly suppressed the growth of Huh-7 xenograft/orthotopic HCC tumor without causing undesirable side effects. In addition, stable down-regulation of LRP6 in Huh-7 facilitated the anti-HCC effects of SAMC. In conclusion, LRP6 can be a potential therapeutic target of HCC. SAMC is a promising specific anti-tumor agent for treating HCC subtypes with Wnt activation at the hepatoma cell surface.
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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) | 0.001 | 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".