AGRO100 inhibits activation of nuclear factor-κB (NF-κB) by forming a complex with NF-κB essential modulator (NEMO) and nucleolin
Bibliographic record
Abstract
AGRO100, also known as AS1411, is an experimental anticancer drug that recently entered human clinical trials. It is a member of a novel class of antiproliferative agents known as G-rich oligonucleotides (GRO), which are non-antisense, guanosine-rich phosphodiester oligodeoxynucleotides that form stable G-quadruplex structures. The biological activity of GROs results from their binding to specific cellular proteins as aptamers. One important target protein of GROs has been previously identified as nucleolin, a multifunctional protein expressed at high levels by cancer cells. Here, we report that AGRO100 also associates with nuclear factor-kappaB (NF-kappaB) essential modulator (NEMO), which is a regulatory subunit of the inhibitor of kappaB (IkappaB) kinase (IKK) complex, and also called IKKgamma. In the classic NF-kappaB pathway, the IKK complex is required for phosphorylation of IkappaBalpha and subsequent activation of the transcription factor NF-kappaB. We found that treatment of cancer cells with AGRO100 inhibits IKK activity and reduces phosphorylation of IkappaBalpha in response to tumor necrosis factor-alpha stimulation. Using a reporter gene assay, we showed that AGRO100 blocks both tumor necrosis factor-alpha-induced and constitutive NF-kappaB activity in human cancer cell lines derived from cervical, prostate, breast, and lung carcinomas. In addition, we showed that, in AGRO100-treated cancer cells, NEMO is coprecipitated by nucleolin, indicating that both proteins are present in the same complex. Our studies suggest that abrogation of NF-kappaB activity may contribute to the anticancer effects of AGRO100 and that nucleolin may play a previously unknown role in regulating the NF-kappaB pathway.
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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".