Modulation of signature cancer-related genes in oral cancer cells (Ca9-22) by anethole treatment: Insights into therapeutic potential
Bibliographic record
Abstract
To explore an alternative strategy to chemotherapy to combat oral cancer, natural products and their derivates constitute one promising approach. In the last previous study, we have demonstrated the potential anti-tumor properties of anethole; an aromatic compound abundantly present in nature that serves as a major active ingredient found in plants like anise and fennel. In the current study, we aimed to investigate how this molecule inhibits oral cancer cell proliferation and induces apoptosis. This will be carried out by a transcriptomic study of its effects on the expression profile of cell cycle and apoptosis regulation genes in gingival cancer cells. cell cycle. Ca9-22 cells were treated with 10 μM of anethole (IC50) and cell proliferation was evaluated by MTT assay. The percentage of cells in different stages of the cell cycle was measured by flow cytometry. Cytotoxicity was evaluated by LDH assay and apoptosis was investigated by Pi/Annexin V assay following 24-hour treatment. Furthermore, we employed PCR array analysis to investigate alterations in the expression levels of oncogenes and tumor suppressor genes associated with cell cycle regulation and apoptosis. Finally, Gene-gene interactions were examined using the Gene MANIA database. Our findings demonstrate that anethole significantly attenuated the proliferation of Ca9-22 cells, leading to disturbances in cell cycle progression and eliciting cellular toxicity and apoptosis. By a double normalizing with two housekeeping genes (Actin and GAPDH), we show that, treatment with 10 μM of anethole alters (more than two-fold) the expression of 13 genes involved in the control of the cell cycle (8 were up regulated and 5 were down regulated) and 7 genes involved in the regulation of apoptosis (4 were up regulated and 3 downregulated by anethole). Finally, each group of genes modulated by anethole forms a network of connections between them or with other genes. Our study suggests that anethole holds promise as a potential alternative treatment for oral cancer by its ability to modify numerous oncogenes and tumor suppressor genes implicated in the cell cycle regulation and induction of apoptosis in oral cancer cells. These findings underscore the significance of further research into the potential therapeutic application of anethole as an alternative drug for managing oral cancer.
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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".