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Record W4367834560 · doi:10.1038/s41389-023-00467-1

Retinoic acid receptor β modulates mechanosensing and invasion in pancreatic cancer cells via myosin light chain 2

2023· article· en· W4367834560 on OpenAlexaff
Carlos Matellan, Dariusz Lachowski, Ernesto Cortés, Kai Ning Chiam, Aleksandar Krstić, Stephen D. Thorpe, Armando E. del Río Hernández

Bibliographic record

VenueOncogenesis · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsTrinity College
FundersUK Research and Innovation“la Caixa” Foundation
KeywordsPancreatic cancerStromal cellCancer researchDownregulation and upregulationRetinoic acidCancer cellBiologyCancerMyosinCell biologyRetinoic acid receptorChemistryEndocrinologyInternal medicineMedicineBiochemistry

Abstract

fetched live from OpenAlex

Pancreatic ductal adenocarcinoma (PDAC) is the most common and lethal form of pancreatic cancer, characterised by stromal remodelling, elevated matrix stiffness and high metastatic rate. Retinoids, compounds derived from vitamin A, have a history of clinical use in cancer for their anti-proliferative and differentiation effects, and more recently have been explored as anti-stromal therapies in PDAC for their ability to induce mechanical quiescence in cancer associated fibroblasts. Here, we demonstrate that retinoic acid receptor β (RAR-β) transcriptionally represses myosin light chain 2 (MLC-2) expression in pancreatic cancer cells. As a key regulatory component of the contractile actomyosin machinery, MLC-2 downregulation results in decreased cytoskeletal stiffness and traction force generation, impaired response to mechanical stimuli via mechanosensing and reduced ability to invade through the basement membrane. This work highlights the potential of retinoids to target the mechanical drivers of pancreatic cancer.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.608

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 head0.011
GPT teacher head0.239
Teacher spread0.228 · 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 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

Citations19
Published2023
Admission routes1
Has abstractyes

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