Graphene Oxide Nanoparticles Induce Apoptosis in wild-type and CRISPR/Cas9-IGF/IGFBP3 knocked-out Osteosarcoma Cells
Bibliographic record
Abstract
Osteosarcoma affects both adolescents and adults, and some improvement in the survival rate for affected patients has been reached in the last decade. Still, non-specificity and systemic toxicity may limit traditional therapeutic approaches to some extent. The insulin growth factor 1 (IGF1) and its binding protein (IGFBP3) have been implicated in the tumorigenesis. Nanoparticles, such as graphene oxide (GO), can provide an effective treatment for cancer as they can specifically target cancer cells while reducing undesired side effects. This study aimed to evaluate the toxicity of GO on osteosarcoma in vitro using tumor cell lines with and without knocking out the IGF and IGFBP3 genes. Human osteosarcoma cell lines, U2OS and SAOS2, and the normal osteoblast cell line hFOB1.19 were used. The IGF1 and IGFBP3 genes were eliminated using CRISPR/Cas9. Tumor cells were cultured and treated with GO. Apoptosis and reactive oxygen species (ROS) were analyzed by Annexin V-FITC and ROS assays. The nuclear factor erythroid 2-related factor 2 (NRF2), which is a crucial regulator of cellular resistance to oxidants, was investigated by Western blotting. We found a significantly higher rate of apoptosis in the OS than hFOB1.19, especially in U2OS cells in which IGF1 and IGFBP3 were knocked out. ROS increase due to GO exposure was remarkably time and concentration-dependent. Based on the rate of apoptosis, ROS, Nrf-2 decrease, and cytomorphological changes, GO has a significant cytotoxic effect against OS. Targeting the IGF1 and IGFBP3 signaling pathway may strengthen GO-related cytotoxicity with the potential to increase the survival of patients affected by this tumor.
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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".