Targeting signal transducer and activator of transcription 3 with G-quartet oligonucleotides: a potential novel therapy for head and neck cancer
Bibliographic record
Abstract
Signal transducer and activator of transcription 3 (Stat3) is a critical mediator of oncogenic signaling activated frequently in many types of human cancer where it contributes to tumor cell growth and resistance to apoptosis. Stat3 has been proposed as a promising target for anticancer drug discovery. Recently, we developed a series of G-quartet oligodeoxynucleotides (GQ-ODN) as novel and potent Stat3 inhibitors, which significantly suppressed the growth of prostate and breast tumors in nude mice. In the present study, we showed that GQ-ODN specifically inhibited DNA-binding activity of Stat3 as opposed to Stat1. Computer-based docking analysis revealed that GQ-ODN predominantly interacts with the SH2 domains of Stat3 homodimers to destabilize dimer formation and disrupt DNA-binding activity. We employed five regimens in the treatment of nude mice with tumors of head and neck squamous cell carcinoma (HNSCC): placebo, paclitaxel, GQ-ODN T40214, GQ-ODN T40231, and T40214 plus paclitaxel. The mean size of HNSCC tumors over 21 days only increased by 1.7-fold in T40214-treated mice and actually decreased by 35% in T40214 plus paclitaxel-treated mice whereas the mean size of HNSCC tumors increased 9.4-fold in placebo-treated mice in the same period. These findings show that GQ-ODN has potent activity against HNSCC tumor xenografts alone and in combination with paclitaxel.
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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".