N-acetylcysteine decreases malignant characteristics of glioblastoma cells by inhibiting Notch2 signaling
Bibliographic record
Abstract
BACKGROUND: Glioblastomas multiforme (GBM) is the most devastating primary intracranial malignancy lacking effective clinical treatments. Notch2 has been established to be a prognostic marker and probably involved in GBM malignant progression. N-acetylcysteine (NAC), a precursor of intracellular glutathione (GSH), has been widely implicated in prevention and therapy of several cancers. However, the role of NAC in GBM remains unclear and the property of NAC independent of its antioxidation is largely unknown. METHODS: The mRNA and protein levels of Notch family and other related factors were detected by RT-PCR and western blot, respectively. In addition, intracellular reactive oxygen species (ROS) was measured by flow cytometry-based DCFH-DA. Moreover, cell viability was assessed by CCK8 and cell cycle was analyzed by flow cytometry-based PI staining. The level of apoptosis was checked by flow cytometry-based Annexin V/PI. Cell migration and invasion were evaluated by wound healing and transwell invasion assays. At last, U87 Xenograft model was established to confirm whether NAC could restrain the growth of tumor. RESULTS: Our data showed that NAC could decrease the protein level of Notch2. Meanwhile, NAC had a decreasing effect on the mRNA and protein levels of its downstream targets Hes1 and Hey1. These effects caused by NAC were independent of cellular GSH and ROS levels. The mechanism of NAC-mediated Notch2 reduction was elucidated by promoting Notch2 degradation through Itch-dependent lysosome pathway. Furthermore, NAC could prevent proliferation, migration, and invasion and might induce apoptosis in GBM cells via targeting Notch2. Significantly, NAC could suppress the growth of tumor in vivo. CONCLUSIONS: NAC could facilitate Notch2 degradation through lysosomal pathway in an antioxidant-independent manner, thus attenuating Notch2 malignant signaling in GBM cells. The remarkable ability of NAC to inhibit cancer cell proliferation and tumor growth may implicate a novel application of NAC on GBM therapy.
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".