Targeting Senescence in Oncology: An Emerging Therapeutic Avenue for Cancer
Bibliographic record
Abstract
Since cancer is often linked to the aging process, the importance of cellular senescence in cancer has come under the spotlight. While senescence in cancer cells can serve as a natural barrier against cancer due to its proliferation arrest, its secretory phenotypes and alterations in the surface proteome can paradoxically promote or suppress tumor progression. Senescent cancer-associated fibroblasts, endothelial cells, and immune cells can also contribute to cancer promotion. During therapeutic interventions for cancer, not only their therapeutic effects, but also therapy-induced senescence may have an impact on cancer outcomes. Senotherapeutics, therapy targeting senescent cells, have been reported as novel cancer therapy in recent studies, and the combination of senescence induction and senotherapeutics has been increasingly recognized. Although some clinical trials of senotherapeutic drugs for cancer with or without senescence-inducible therapy are ongoing, there is as yet no satisfactory clinical application. With further research into targeting senescence in oncology, it is expected that senotherapeutics, particularly in combination with senescence-inducing therapy, will become a novel therapeutic strategy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".