Shikonin inhibits the proliferation and induces the apoptosis of human HepG2 cells
Bibliographic record
Abstract
This study investigated the potential of shikonin as an anticancer agent against liver cancer and an in vitro human hepatoma cancer model system. The HepG2 cell line was the hepatoma cancer model in the present study. The inhibitory effect of shikonin on the growth of HepG2 cells was measured by MTT assay. To explore the underlying mechanism of cell growth inhibition of shikonin, the cell cycle distribution, DNA fragmentation, mitochondrial membrane potential (ΔΨm) disruption, and expression of Bax and Bcl-2 were measured in HepG2 cells. The activity of shikonin in inducing apoptosis was investigated through the detection of Annexin V signal and CD95 expression by flow cytometry and electron microscopy, respectively. Shikonin inhibited the growth of HepG2 cells in a dose-dependent manner. The IC50 value (inhibiting cell growth by 50%) was 4.30 mg/mL. Shikonin inhibited cell growth in a dose-dependent manner and blocked HepG2 cell cycle progression at the S phase. The changes in mitochondrial morphology, dose-dependently decreased in ΔΨm, were observed in different concentrations of the drug treatment group. Western blot analysis showed that cajanol inhibited Bcl-2 expression and induced Bax expression. Furthermore, we show that shikonin increases Annexin V signal and CD95 (Fas/APO) expression, resulting in apoptotic cell death of HepG2 cells. In addition, lump formation of intranuclear chromatin, pyknosis of cell nucleus, deletion of microvillus, vacuolar degeneration of mitochondria, reduction of rough endoplasmic reticulum, and resolution of free ribosome, etc., associated with apoptosis were discovered by electron microscopy in HepG2 cells after 48 h treatment. Shikonin inhibited HepG2 cells, possibly through the pathway of inducing early apoptosis, and was beneficial for restoring the apoptotic sensitivity of HepG2 cells by CD95, and should therefore be considered as a candidate agent for the prevention or treatment of human hepatoma.
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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".