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Record W1975029652 · doi:10.1074/jbc.m310357200

RNA Interference-mediated Silencing of the S100A10 Gene Attenuates Plasmin Generation and Invasiveness of Colo 222 Colorectal Cancer Cells

2004· article· en· W1975029652 on OpenAlexafffund
Libo Zhang, Darin K. Fogg, David M. Waisman

Bibliographic record

VenueJournal of Biological Chemistry · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicS100 Proteins and Annexins
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsAnnexin A2RNA interferenceGene silencingPlasminCell biologyAnnexinCancer cellBiologySmall interfering RNAMolecular biologyCancer researchChemistryTransfectionCellCancerRNAGeneBiochemistryGenetics

Abstract

fetched live from OpenAlex

S100A10 is a key plasminogen receptor of the extracellular cell surface that is overexpressed in many cancer cells. Typically, S100A10 is thought to be anchored to the plasma membrane via the phospholipid-binding sites of its binding partner, annexin A2. Here, using the potent and highly sequence-specific mechanism of RNA interference (RNAi), we have stably silenced the expression of the S100A10 gene in colorectal (CCL-222) cancer cells. We show that siRNA expression mediated by the pSUPER vector causes efficient, stable, and specific down-regulation of S100A10 gene expression. The siRNA-mediated down-regulation of S100A10 gene expression resulted in a major decrease in the appearance of extracellular S100A10 protein and correlated with a 45% loss of plasminogen binding, a 65% loss in cellular plasmin generation and a complete loss in plasminogen-dependent cellular invasiveness. We also observed that the CCL-222 cells do not express annexin A2 on their extracellular surface. Thus, the data show that annexin A2 is not required by S100A10 for its association with the plasma membrane, for its colocalization with uPAR, or for its binding and activation of plasminogen.

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 score0.001
Threshold uncertainty score0.287

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 head0.020
GPT teacher head0.237
Teacher spread0.217 · 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

Citations100
Published2004
Admission routes2
Has abstractyes

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