Doxycycline inhibits proliferation and induces apoptosis of both human papillomavirus positive and negative cervical cancer cell lines
Bibliographic record
Abstract
The clinical management of cervical cancer remains a challenge and the development of new treatment strategies merits attention. However, the discovery and development of novel compounds can be a long and labourious process. Drug repositioning may circumvent this process and facilitate the rapid translation of hypothesis-driven science into the clinics. In this work, we show that a FDA-approved antibiotic, doxycycline, effectively targets human papillomavirus (HPV) positive and negative cervical cancer cells in vitro and in vivo. Doxycycline significantly inhibits proliferation of a panel of cervical cancer cell lines. It also induces apoptosis of cervical cancer cells in a time- and dose-dependent manner. In addition, the apoptosis induced by doxycycline is through caspase-dependent pathway. Mechanism studies demonstrate that doxycycline affects oxygen consumption rate, glycolysis, and reduces ATP levels in cervical cancer cells. In HeLa xenograft mouse model, doxycycline significantly inhibits growth of tumour. Our in vitro and in vivo data clearly demonstrate the inhibitory effects of doxycycline on the growth and survival of cervical cancer cells. Our work provides the evidence that doxycycline can be repurposed for the treatment of cervical cancer and targeting energy metabolism may represent a potential therapeutic strategy for cervical 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".