Repurposing Antimalarials for Oral Cancer: Selective Efficacy of Hydroxychloroquine on Gingival Squamous Cell Carcinoma
Bibliographic record
Abstract
Oral cancer, the most common head and neck malignancy, has a high recurrence rate and poor prognosis largely owing to chemotherapy resistance. The adverse effects of conventional therapies have prompted investigations into safer and more effective alternative therapies. Chloroquine (CQ) and hydroxychloroquine (HCQ) have shown potential owing to their roles in autophagy modulation and immune regulation. This study clarifies the selective efficacy of hydroxychloroquine (HCQ) and chloroquine (CQ) in oral squamous cell carcinoma models, emphasizing distinct responses in gingival (Ca9-22) and tongue (SCC-9) carcinoma cells. Non-oncogenic oral epithelial cells (GMSM-K) and oral carcinoma cell lines from the tongue (SCC-9, Cal-27) and gingiva (Ca9-22) were used. Cell viability, cytotoxicity, and colony formation were assessed via MTT, LDH, and crystal violet assays. Flow cytometry was used to measure apoptosis, autophagy, oxidative stress, mitochondrial membrane potential, and DNA damage. The transcriptomic profiles of apoptosis and autophagy-related genes were assessed by qPCR arrays. Bioinformatics analysis allowed estimation of the main gene interaction networks. Pre-screening showed that GMSM-K and Cal-27 cells were non-responsive or exhibited non-specific toxicity at high doses; therefore, subsequent analyses focused on Ca9-22 (GC) and SCC-9 (TC). HCQ significantly reduced viability and colony formation in Ca9-22 cells while moderately affecting SCC-9 cells. Autophagy inhibition was accompanied by compensatory up-regulation of autophagy-related genes, consistent with feedback activation of TFEB and FOXO3a pathways. Gene expression profiling and flow-cytometry analyses revealed cell-type-specific differences in apoptosis, mitochondrial potential, and DNA damage, suggesting HCQ's selective anti-tumor potential in gingival carcinoma. These findings highlight HCQ as a repurposed adjuvant therapy that modulates autophagy and apoptosis to enhance chemosensitivity in 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".