Lipid Rafts and Nonrafts Mediate Tumor Necrosis Factor–Related Apoptosis-Inducing Ligand–Induced Apoptotic and Nonapoptotic Signals in Non–Small Cell Lung Carcinoma Cells
Bibliographic record
Abstract
Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is capable of inducing apoptosis in non-small cell lung carcinoma (NSCLC). However, many of the human NSCLC cell lines are resistant to TRAIL, and TRAIL treatment of the resistant cells leads to the activation of nuclear factor-kappaB (NF-kappaB) and extracellular signal-regulated kinase 1/2 (ERK1/2). TRAIL can induce apoptosis in TRAIL-sensitive NSCLC cells through the induction of death-inducing signaling complex (DISC) assembly in lipid rafts of plasma membrane. In the DISC, caspase-8 is cleaved and initiates TRAIL-induced apoptosis. In contrast, TRAIL-DISC assembly in the nonraft phase of the plasma membrane leads to the inhibition of caspase-8 cleavage and NF-kappaB and ERK1/2 activation in TRAIL-resistant NSCLC cells. Receptor-interacting protein (RIP) and cellular Fas-associated death domain-like interleukin-1beta-converting enzyme-inhibitory protein (c-FLIP) mediates the DISC assembly in nonrafts and selective knockdown of either RIP or c-FLIP with interfering RNA redistributes the DISC from nonrafts to lipid rafts, thereby switching the DISC signals from NF-kappaB and ERK1/2 activation to caspase-8-initiated apoptosis. Chemotherapeutic agents inhibit c-FLIP expression, thereby enhancing the DISC assembly in lipid rafts for caspase-8-initiated apoptosis. These studies indicate that RIP and c-FLIP-mediated assembly of the DISC in nonrafts is a critical upstream event in TRAIL resistance and thus targeting of either RIP or c-FLIP may lead to the development of novel therapeutic strategies that can overcome TRAIL resistance in human NSCLC.
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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) | 0.002 | 0.001 |
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".