Hepatocellular carcinoma repression by TNFα‐mediated synergistic lethal effect of mitosis defect‐induced senescence and cell death sensitization
Bibliographic record
Abstract
UNLABELLED: Hepatocellular carcinoma (HCC) is a cancer lacking effective therapies. Several measures have been proposed to treat HCCs, such as senescence induction, mitotic inhibition, and cell death promotion. However, data from other cancers suggest that single use of these approaches may not be effective. Here, by genetic targeting of Survivin, an inhibitor of apoptosis protein (IAP) that plays dual roles in mitosis and cell survival, we identified a tumor necrosis factor alpha (TNFα)-mediated synergistic lethal effect between senescence and apoptosis sensitization in malignant HCCs. Survivin deficiency results in mitosis defect-associated senescence in HCC cells, which triggers local inflammation and increased TNFα. Survivin inactivation also sensitizes HCC cells to TNFα-triggered cell death, which leads to marked HCC regression. Based on these findings, we designed a combination treatment using mitosis inhibitor and proapoptosis compounds. This treatment recapitulates the therapeutic effect of Survivin deletion and effectively eliminates HCCs, thus representing a potential strategy for HCC therapy. CONCLUSION: Survivin ablation dramatically suppresses human and mouse HCCs by triggering senescence-associated TNFα and sensitizing HCC cells to TNFα-induced cell death. Combined use of mitotic inhibitor and second mitochondrial-derived activator of caspases mimetic can induce senescence-associated TNFα and enhance TNFα-induced cell death and synergistically eliminate HCC. (Hepatology 2016;64:1105-1120).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".