Cidofovir Administered with Radiation Displays an Antiangiogenic Effect Mediated by E6 Inhibition and Subsequent TP53-Dependent VEGF Repression in HPV18+ Cell Lines
Bibliographic record
Abstract
Therapeutic administration of the antiviral agent cidofovir with radiation markedly enhanced the antitumor effect of ionizing radiation in cells of two HPV18+ human cervical carcinoma cell lines. Although this potent radiosensitizing effect was associated with repression of the viral oncoproteins E6/ E7 and restoration of TP53 as shown previously, additional mechanisms may be involved. In the present study, we investigated the antiangiogenic effect of the combination of cidofovir and radiation in cells of two HPV18+ cervical cancer cell lines, HeLa and ME180, and assessed the molecular mechanisms associated with the antiangiogenic effect observed. Cells were exposed to cidofovir (10 microg/ml) and irradiated (1-9 Gy). The angiogenic response was studied in vitro by a matrigel invasion assay. Modulations of E6, TP53 and VEGF mRNA and protein levels were studied by real-time RT-PCR, Western blot analysis and ELISA, respectively. Then a double RNA interference approach was used to analyze the connection between E6/TP53 and VEGF. The combination of cidofovir and radiation had a potent antiangiogenic effect. It induced E6 inhibition, restoration of TP53, and reduction of the proangiogenic phenotype of HPV18+ cells associated with VEGF inhibition. A siRNA strategy showed an anti-VEGF action of the combination mediated directly by E6 inhibition and TP53 restoration, since E6 siRNA inhibited VEGF whereas co-transfection with E6 and TP53 siRNA abrogated the anti-VEGF effect. This study showed that the combination of cidofovir with ionizing radiation has an antiangiogenic effect associated with VEGF inhibition subsequent to E6 inhibition and TP53 restoration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".