Editorial: Hallmark of cancer: Resisting cell death
Bibliographic record
Abstract
Hallmark of cancer: Resisting cell deathIn 2000, Douglas Hanahan and Robert Weinberg proposed 6 physiological features as the hallmarks of cancer (1), which evolved into 14 hallmarks with the rapid advances in cancer research over the past two decades (2).Among the long list of hallmarks, conceivably being further updated in the future, evading programmed cell death constitutes one of the founder mechanisms whereby tumors can establish successfully (1, 2).Whereas apoptosis was regarded as the primary form of programmed cell death two decades ago, it is currently well acknowledged that cell death can be executed through a plethora of programmed mechanisms.The latest recommendations by the Nomenclature Committee on Cell Death in 2018 proposed 13 distinct forms of programmed cell death, including classical forms of autonomous cell death, such as apoptosis, necroptosis, autosis, ferroptosis and netosis, and the emerging concept of noncell-autonomous death, such as entosis, which is mediated by the formation of cell-in-cell structures (CICs) (3).Novel mechanisms of cell death, such as cuproptosis (4), are continually being identified.Given the pivotal role of resisting cell death in the development and progression of tumors, almost every form of cell death has been, more or less, implicated in human cancers.This Research Topic collected a set of research articles and comprehensive reviews related to the representative forms of cell death that are currently undergoing extensive investigation in multiple human cancers to promote the diagnostic and therapeutic applications of targeting cell death in the clinic. FerroptosisResearch on ferroptosis, a novel form of programmed cell death driven by irondependent lipid peroxidation, has grown exponentially over the past years.Ferroptosis is regulated by multiple cellular pathways, including redox homeostasis, iron handling, Frontiers in Oncology frontiersin.org01
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.017 | 0.018 |
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".