PKCα–GSK3β–NF-κB signaling pathway and the possible involvement of TRIM21 in TRAIL-induced apoptosis
Bibliographic record
Abstract
Tumor necrosis factor related apoptosis-inducing ligand (TRAIL) is a highly promising therapeutic agent for cancer treatment, owing to its ability to selectively target tumor cells for cell death while having little effect on most normal cells. However, recent research has found that many cancers, including non-small cell lung cancer (NSCLC), display resistance to TRAIL. Therefore, it is important to elucidate the molecular mechanisms governing the resistance of tumor cells to TRAIL treatment. In this study, we show that GSK3β antagonized TRAIL-induced apoptosis in H1299 NSCLC cells, and determined that the PKCα isozyme is an upstream regulator of GSK3β that phosphorylates and inactivates GSK3β, thereby sensitizing cancer cells to TRAIL-induced apoptosis. Furthermore, we demonstrated that the anti-apoptotic effect of GSK3β is mediated by the NF-κB pathway, whereas the tripartite motif 21 (TRIM21) was able to inhibit the activation of NF-κB by GSK3β, and leads to the promotion of cell apoptosis. Taken together, our study further delineated the underpinning mechanism of resistance to TRAIL-induced apoptosis in H1299 cells, and provided new clues for sensitizing NSCLC cells to TRAIL therapy.
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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".