Nix Protein Positively Regulates NF-κB Activation in Gliomas
Bibliographic record
Abstract
Previous reports indicate that the NIX/BNIP3L gene acts as a pro-apoptotic factor by interacting with BCL2 and BCL-XL, playing an important role in hypoxia-dependent cell death and acting as a tumor suppressor. However, many studies also showed that NIX is linked to a protective role and cell survival in cancer cells. Nuclear factor-κB (NF-κB) can attenuate apoptosis in human cancers in response to chemotherapeutic agents and ionizing radiation. We observed an absence of i-κBα (NF-κB activation inhibitor) expression, but a greater expression of Nix and p-NF-κB proteins in the Nix-wt U251 cells, which was not observed in the Nix-kn cells under hypoxic conditions. Using electrophoretic mobility shift assay (EMSA) and luciferase detection, the activation of NF-κB was detected only in the Nix-wt U251 cells with hypoxia. These data imply that Nix protein might play a role in the positive regulation of the NF-κB pathway. Moreover, 46 cases of glioma also showed high levels of Nix protein expression, which was always accompanied by high p-NF-κB expression. Patients with Nix (+) showed less tissue apoptosis behavior in glioblastoma (GBM), unlike that observed in the Nix-negative patients (-). The same apoptotic tendency was also identified in anaplastic astrocytoma (AA) groups, but not in astrocytoma (AS). On analyzing the Kaplan-Meier curve, better tumor-free survival was observed only in cases of astrocytoma, and not in AA and GBM. Thus, our study indicates that Nix protein might have multiple functions in regulating glioma behaviors. In the low-grade gliomas (astrocytoma) with low expression of NF-κB, the cell death-inducing function that occurs through a Bax mechanism might predominate and act as a tumor suppressor. While in the malignant gliomas (AA and GBM), with higher expression of the NIX gene and with activity of the NF-κB pathway, the oncogene function of Nix was predominant.
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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".