MétaCan
Menu
Back to cohort
Record W3009077651 · doi:10.1186/s12943-020-01157-x

The epigenetic regulators and metabolic changes in ferroptosis-associated cancer progression

2020· review· en· W3009077651 on OpenAlexaff
Yuqing Wu, Si-Wei Zhang, Xiaoxiao Gong, Samantha Tam, Desheng Xiao, Shuang Liu, Yongguang Tao

Bibliographic record

VenueMolecular Cancer · 2020
Typereview
Languageen
FieldMedicine
TopicFerroptosis and cancer prognosis
Canadian institutionsQueen's University
FundersOverseas Expertise Introduction Project for Discipline InnovationNational Key Research and Development Program of ChinaHigher Education Discipline Innovation ProjectNational Natural Science Foundation of China
KeywordsBiologyEpigeneticsProgrammed cell deathCell biologyEndoplasmic reticulumCarcinogenesisMitochondrionAutophagyOrganelleLysosomeOxidative stressCancerSignal transductionReprogrammingUnfolded protein responseCancer researchCellApoptosisGeneticsBiochemistryGene

Abstract

fetched live from OpenAlex

Ferroptosis, a novel form of regulated cell death, is different from other types of cell death in morphology, genetics and biochemistry. Increasing evidence indicates that ferroptosis has significant implications on cell death linked to cardiomyopathy, tumorigenesis, and cerebral hemorrhage to name a few. Here we summarize current literature on ferroptosis, including organelle dysfunction, signaling transduction pathways, metabolic reprogramming and epigenetic regulators in cancer progression. With regard to organelles, mitochondria-induced cysteine starvation, endoplasmic reticulum-related oxidative stress, lysosome dysfunction and golgi stress-related lipid peroxidation all contribute to induction of ferroptosis. Understanding the underlying mechanism in ferroptosis could provide insight into the treatment of various intractable diseases including cancers.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.033
GPT teacher head0.355
Teacher spread0.322 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations378
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueMolecular CancerSame topicFerroptosis and cancer prognosisFrench-language works237,207