Dynamic Coupling of MAPK Signaling to the Guanine Nucleotide Exchange Factor GEF-H1
Bibliographic record
Abstract
Abstract: The KRAS gene is nearly ubiquitously subjected to activating mutation in pancreatic adenocarcinomas (PDAC), occurring at a frequency of over 90% in tumors. Mutant KRAS drives sustained signaling through the MAPK pathway to affect frequently disrupted cancer phenotypes including transcription, proliferation and cell survival. Recent research has shown that PDAC tumor growth and survival required a guanine nucleotide exchange factor for RAS homolog family member A (RhoA) called GEF-H1. The GEF-H1 protein, encoded by the ARHGEF2 gene, is a microtubule-associated GEF for RhoA that promotes invasion-migration of PDAC cells via activation of RhoA. Unexpectedly, independent of its RhoGEF activity, GEF-H1 was found to potentiate MAPK signaling by scaffolding protein phosphatase 2A (PP2A) to the kinase suppressor of Ras 1 (KSR-1). In a feedback-dependent manner, enhanced MAPK activity drives expression of ARHGEF2 via regulation of transcription factors ETS and SP, and the RAS responsive element-binding protein 1 (RREB1). RREB1 a negative regulator of ARHGEF2 expression, is downregulated in PDAC cells, which permits sustained expression of GEF-H1 for PDAC tumor survival and subsequent MAPK pathway activation. Given that MAPK targeted therapies show limited clinical efficacy, highlights the need for novel targets. This review describes the unexpected complexity of GEF-H1 function leading to positive feedback that potentiates RAS-MAPK signaling and suggests inhibition of GEF-H1 as a therapeutic strategy for RAS-driven cancers. Keywords: ARHGEF2, GEF-H1, pancreatic, RAS-MAPK, RhoA, RREB1
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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 | 0.001 |
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".