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Record W4295222695 · doi:10.7150/ijbs.73790

Targeting Ferroptosis by Ubiquitin System Enzymes: A Potential Therapeutic Strategy in Cancer

2022· review· en· W4295222695 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Biological Sciences · 2022
Typereview
Languageen
FieldMedicine
TopicFerroptosis and cancer prognosis
Canadian institutionsSKiN Health
Fundersnot available
KeywordsDeubiquitinating enzymeUbiquitinCancer cellProgrammed cell deathCancer researchCancerBiologyGPX4Cell biologyEnzymeBiochemistryApoptosisGeneGeneticsGlutathione

Abstract

fetched live from OpenAlex

Ferroptosis is a novel type of regulated cell death driven by the excessive accumulation of iron-dependent lipid peroxidation. Therapy-resistant tumor cells, particularly those in the mesenchymal-like state and prone to metastasis, are highly susceptible to ferroptosis, suggesting that induction of ferroptosis in tumor cells is a promising strategy for cancer therapy. Although ferroptosis is regulated at various levels, ubiquitination is key to post-translational regulation of ferroptotic cell death. E3 ubiquitin ligases (E3s) and deubiquitinating enzymes (DUBs) are the most remarkable ubiquitin system enzymes, whose dysregulation accounts for the progression of multiple cancers. E3s are involved in the attachment of ubiquitin to substrates for their degradation, and this process is reversed by DUBs. Accumulating evidence has highlighted the important role of ubiquitin system enzymes in regulating the sensitivity of ferroptosis. Herein, we will portray the regulatory networks of ferroptosis mediated by E3s or DUBs and discuss opportunities and challenges for incorporating this regulation into cancer therapy.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.147
GPT teacher head0.411
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it