Cdc42 and Rac1 activity is reduced in human pheochromocytoma and correlates with FARP1 and ARHGEF1 expression
Bibliographic record
Abstract
Among small GTPases from the Rho family, Cdc42, RAC, and Rho are well known to mediate a large variety of cellular processes linked with cancer biology through their ability to cycle between an inactive (GDP-bound) and an active (GTP-bound) state. Guanine nucleotide exchange factors (GEFs) stimulate the exchange of GDP for GTP to generate the activated form, whereas the GTPase-activating proteins (GAPs) catalyze GTP hydrolysis, leading to the inactivated form. Modulation of Rho GTPase activity following altered expression of RHO-GEFs and/or RHO-GAPs has already been reported in various human tumors. However, nothing is known about the Rho GTPase activity or the expression of their regulators in human pheochromocytomas, a neuroendocrine tumor (NET) arising from chromaffin cells of the adrenal medulla. In this study, we demonstrate, through an ELISA-based activity assay, that Rac1 and Cdc42 activities decrease in human pheochromocytomas (PCCs) compared with the matched adjacent non-tumor tissue. Furthermore, through quantitative mass spectrometry (MS) approaches, we show that the expression of two RHO-GEF proteins, namely ARHGEF1 and FARP1, is significantly reduced in tumors compared with matched non-tumor tissue, whereas ARHGAP36 expression is increased. Moreover, siRNA-based knockdown of ARHGEF1 and FARP1 in PC12 cells leads to a significant inhibition of Rac1 and Cdc42 activities, respectively. Finally, a principal component analysis (PCA) of our dataset was able to discriminate PCC from non-tumor tissue and indicates a close correlation between Cdc42/Rac1 activity and FARP1/ARHGEF1 expression. Altogether, our findings reveal for the first time the importance of modulation of Rho GTPase activities and expression of their regulators in human PCCs.
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".