Inhibition of Grb2-mediated activation of MAPK signal transduction suppresses NOR1/CB1954-induced cytotoxicity in the HepG2 cell line
Bibliographic record
Abstract
The nitroreductase oxidored‑nitro domain containing protein 1 (NOR1) gene may be involved in the chemical carcinogenesis of hepatic cancer and nasopharyngeal carcinoma (NPC). We have previously demonstrated that NOR1 overexpression is capable of converting the monofunctional alkylating agent 5‑(aziridin‑1‑yl)‑2,4‑dinitrobenzamide (CB1954) into a toxic form by reducing the 4‑nitro group of CB1954. Toxic CB1954 is able to enhance cell killing in the NPC cell line CNE1; however, the underlying mechanisms remain unknown. Using cDNA microarrays and quantitative real‑time PCR, we previously discovered that NOR1 increases the expression of growth factor receptor‑bound protein 2 (Grb2) mRNA by 4.8‑fold in the human hepatocellular carcinoma cell line HepG2. In the present study, we revealed that NOR1 increased Grb2 protein expression by 3‑fold in HepG2 cells. Additionally, we demonstrated that NOR1 enhanced CB1954‑induced cell killing in HepG2 cells, and cell cytotoxicity was inhibited with the tyrosine kinase inhibitor genistein, or by stable transfection of Grb2 small hairpin RNA (shRNA) pU6+27‑shGrb2 to silence the expression of Grb2. Western blot analysis revealed that Grb2 downregulation may reduce the activity of the mitogen‑activated protein kinase (MAPK). Inhibiting the activation of MAPK using the methyl ethyl ketone (MEK) inhibtor PD98059 suppressed CB1954‑induced cell killing. These results suggested that the NOR1 gene enhances CB1954‑mediated cell cytotoxicity through the upregulation of Grb2 expression and the activation of MAPK signal transduction in the HepG2 cell line.
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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 | 0.001 |
| 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".