Bibliographic record
Abstract
Abstract Epidermal growth factor receptor (EGFR) and its downstream phosphatidylinositol 3-kinase (PI3K) pathway are commonly deregulated in many cancers including head and neck cancer (HNC). Recently, we have shown that the IQ motif-containing GTPase-activating protein 1 (IQGAP1) provides a molecular platform to scaffold all the components from the PI3K-Akt pathway and results in the sequential generation of phosphatidylinositol-3,4,5-triphosphate (PI3,4,5P3). This makes the IQGAP1-PI3K scaffold a promising therapeutic target. In addition to the PI3K-Akt pathway, IQGAP1 also scaffolds the Ras-ERK pathway. To identify an IQGAP1 mutant that specifically loses IQGAP1-PI3K signaling but not other functions, we have focused on the IQ3 motif since this region binds with both the PIPK1α and PI3K enzymes and a short peptide derived from this sequence blocks binding and PI3K signaling. An IQ3 deletion mutant (ΔIQ3) in IQGAP1 was functionally compared with wild-type (WT) IQGAP1. We found that the IQ3 domain specifically mediates the PI3K-Akt pathway but does not regulate the Ras-ERK pathway. The IQ3 deletion mutant lost interactions with PI3K-Akt components but retained its binding of ERK pathway components and cell surface receptors, EGFR and integrins. In addition, the IQ3 deletion mutant lost regulation of cell migration. Consistently, the IQ3 motif derived peptide blocked Akt activation and led to the blockage of invasion mediated by EGFR organized into an integrin complex by syndecan-4. Taken together, this work has defined the IQGAP1 IQ3 motif as a specific target sequence for the scaffolding of the PI3K-AKT pathway.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.812 | 0.689 |
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".