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Record W3212250957 · doi:10.7150/ijbs.64167

Dysregulation of Transglutaminase type 2 through GATA3 defines aggressiveness and Doxorubicin sensitivity in breast cancer

2021· article· en· W3212250957 on OpenAlexaff
Gianluca Aguiari, Francesca Crudele, Cristian Taccioli, Linda Minotti, Fabio Corrà, Jeffrey W. Keillor, Silvia Grassilli, Carlo Cervellati, Stefano Volinia, Carlo M. Bergamini, Nicoletta Bianchi

Bibliographic record

VenueInternational Journal of Biological Sciences · 2021
Typearticle
Languageen
FieldMedicine
TopicBlood properties and coagulation
Canadian institutionsUniversity of Ottawa
FundersUniversità degli Studi di Padova
KeywordsCancer researchBreast cancerBiologyTranscription factorGene silencingDoxorubicinTissue transglutaminaseGATA3Downregulation and upregulationCancerCell biologyGeneChemotherapyEnzymeGeneticsBiochemistry

Abstract

fetched live from OpenAlex

The role of transglutaminase type 2 in cell physiology is related to protein transamidation and signal transduction (affecting extracellular, intracellular and nuclear processes) aided by the expression of truncated isoforms and of two lncRNAs with regulatory functions. In breast cancer TG2 is associated with disease progression supporting motility, epithelial-mesenchymal transition, invasion and drug resistance. The aim of his work is to clarify these issues by emphasizing the interconnections among TGM2 variants and transcription factors associated with an aggressive phenotype, in which the truncated TGH isoform correlates with malignancy. TGM2 transcripts are upregulated by several drugs in MCF-7, but only Doxorubicin is effective in MDA-MB-231 cells. These differences reflect the expression of GATA3, as demonstrated by silencing, suggesting a link between this transcription factor and gene dysregulation. Of note, NC9, an irreversible inhibitor of enzymatic TG2 activities, emerges to control NF-B and apoptosis in breast cancer cell lines, showing potential for combination therapies with Doxorubicin.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.111

Codex and Gemma teacher scores by category

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

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 head0.061
GPT teacher head0.343
Teacher spread0.282 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations12
Published2021
Admission routes1
Has abstractyes

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