Anticancer Potential of Piperine in Human Oral Cancer Cell Lines—An In-Vitro Study
Bibliographic record
Abstract
Introduction: Cancer is a complex disease characterized by abnormal and uncontrolled cell growth. Oral cavity cancer is the result of a multifaceted process involving three stages: Initiation, Promotion, and Development. Piperine, a compound traditionally used as a savory condiment and flavor enhancer, as well as in traditional medicine, possesses a wide array of pharmacological and biochemical properties. These include antioxidant, anti-carcinogenic, antibacterial, antifungal, anti-inflammatory, immunomodulatory, and hepatoprotective actions. This study aims to assess the potential of Piperine as an anti-tumor agent in human oral cancer cell lines. Materials and Methods: We sourced trypsin-EDTA, fetal bovine serum (FBS), antibiotics-antimycotics, Dulbecco’s modified Eagle’s medium (DMEM), and phosphate-buffered saline (PBS) from Gibco, Canada. The oral cancer cell line was provided by the National Centre for Cell Science (NCCS), Pune, India, and was cultured according to established guidelines. In brief, the oral cancer cells were cultured in DMEM supplemented with 10% FBS at 37°C in a 5% CO2 environment. Data analysis was conducted using CFX Manager Version 2.1, employing the comparative CT approach, and fold changes were determined using the 2 delta CT method as outlined by Schmittgen and Livak (2008) (Bio Rad, USA). Results: Our results demonstrate that exposure to various concentrations (25μM, 50μM, and 100μM) of Piperine significantly reduced the viability of oral cancer cells (p<0.05). Piperine induced a dose-dependent increase in ROS production and nuclear condensation, both of which were statistically significant (p<0.05). Furthermore, Piperine down-regulated Bcl2 expression while upregulating Bax and p53 expression in a concentration-dependent manner, thereby promoting cell death through the loss of metalloprotease activity and activation of caspase-3. Conclusion: Based on the outcomes of this study, the Piperine extract exhibited a profound ability to suppress cancer activity by promoting apoptosis. These findings underscore the potential of Piperine extract as a formidable anti-cancer agent against human oral cancer cells, positioning it as a promising contender for the development of anticancer pharmaceuticals and treatments for 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".