MétaCan
Menu
Back to cohort
Record W4404575006 · doi:10.1038/s41417-024-00850-z

Reversible downregulation of MYC in a spheroid model of metastatic epithelial ovarian cancer

2024· article· en· W4404575006 on OpenAlexafffund
Adrian Buensuceso, Matthew J. Borrelli, Yudith Ramos Valdés, Trevor G. Shepherd

Bibliographic record

VenueCancer Gene Therapy · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsOccupational Cancer Research CentreWestern University
FundersCanadian Institutes of Health ResearchGovernment of CanadaLondon Health Sciences Foundation
KeywordsDownregulation and upregulationSpheroidCancer researchBiologyOvarian cancerTranscription factorCell biologyCellGene expressionGeneCancerCell cultureGenetics

Abstract

fetched live from OpenAlex

Upon detachment from the primary tumour, epithelial ovarian cancer cells can form multicellular aggregates, also referred to as spheroids, that have the capacity to establish metastases at distant sites. These structures exhibit numerous adaptations that may facilitate metastatic transit and promote tumorigenic potential. One such adaptation is the acquisition of dormancy, characterized by decreased proliferation and molecular features of quiescence. One of the most frequently dysregulated genes in cancer is MYC, which encodes a transcription factor that promotes cell proliferation. In this study, we demonstrate that MYC protein abundance and associated gene expression is significantly decreased in EOC spheroids compared to adherent cells. This downregulation occurs rapidly upon cell detachment and is proteasome-dependent. Moreover, MYC protein abundance and associated gene expression is restored upon spheroid reattachment to an adherent culture surface. Overall, our findings suggest that suppression of MYC activity is a common feature of EOC spheroids and may contribute to the reversible acquisition of dormancy.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score1.000

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)0.0010.000

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 head0.054
GPT teacher head0.330
Teacher spread0.276 · 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.

Study designBench or experimental
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

Citations5
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueCancer Gene TherapySame topicCancer Cells and MetastasisFrench-language works237,207