Antitumour effects of metformin and curcumin in human papillomavirus positive and negative head and neck cancer cells
Bibliographic record
Abstract
The incidence of oropharyngeal squamous cell carcinoma (OPSCC) has significantly increased in recent decades due to human papillomavirus (HPV)-mediated oncogenesis. Unfortunately, a growing number of HPV-positive (+) OPSCC survivors are living with the irreversible side effects of treatment. The novel, well-tolerated chemotherapeutics with improved side effect profiles are, therefore, in high demand. Metformin is one such drug, widely used as a first-line oral agent in the treatment of type 2 diabetes mellitus. Curcumin is another well-tolerated agent quickly gaining attention for its medicinal properties. Both metformin and curcumin have been shown to display anticancer properties. This study aimed to determine the antitumor effects of these agents, individually and combined, in HPV+ and HPV-negative (-) head and neck squamous cell carcinoma (HNSCC) cell lines. This was achieved by assessing the efficacy of varying drug concentrations on the overall cell viability, proliferation, and expression of common HNSCC biomarkers. The results from protein and RNA expression data are highly variable, as expected, with multiple pathways being affected in cancer. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assays and immunofluorescence microscopy suggest that both agents are capable of slowing proliferation and inducing apoptosis. We conclude that curcumin and metformin display effective antitumor effects in both HPV+ and HPV- HNSCC cell lines. The curcumin effects appear more pronounced in the HPV- cell lines. Metformin appears to be more effective at reducing the overall cell numbers in HPV+ cell lines. Metformin and curcumin combined did not appear to have synergistic effects on the proliferation or apoptosis of the treated cell lines.
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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".