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Record W2040138014 · doi:10.1371/journal.pone.0070047

Regulation of the Tumor Suppressor PTEN through Exosomes: A Diagnostic Potential for Prostate Cancer

2013· article· en· W2040138014 on OpenAlexafffund
Kathleen Gabriel, Alistair J. Ingram, Richard C. Austin, Anil Kapoor, Damu Tang, Fadwa Majeed, Talha Qureshi, Khalid Al‐Nedawi

Bibliographic record

VenuePLoS ONE · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsSt. Joseph's HospitalMcMaster University
FundersProstate Cancer CanadaMcMaster UniversityMovember Foundation
KeywordsPTENProstate cancerMicrovesiclesCancer researchTumor suppressor geneCancerSuppressorDownregulation and upregulationBiologyMedicinemicroRNAInternal medicineApoptosisGeneCarcinogenesisPI3K/AKT/mTOR pathwayGenetics

Abstract

fetched live from OpenAlex

PTEN is a potent tumor-suppressor protein. Aggressive and metastatic prostate cancer (PC) is associated with a reduction or loss of PTEN expression. PTEN reduction often occurs without gene mutations, and its downregulation is not fully understood. Herein, we show that PTEN is incorporated in the cargo of exosomes derived from cancer cells. PTEN is not detected in exosomes derived from normal, noncancerous cells. We found that PTEN can be transferred to other cells through exosomes. In cells that have a reduction or complete loss of PTEN expression, the transferred PTEN is competent to confer tumor-suppression activity to acceptor cells. In PC patients, we show that PTEN is incorporated in the cargo of exosomes that circulate in their blood. Interestingly, normal subjects have no PTEN expression in their blood exosomes. Further, we found that the prostate-specific antigen (PSA) is incorporated in PC patients' and normal subjects' blood exosomes. These data suggest that exosomal PTEN can compensate for PTEN loss in PTEN deficient cells, and may have diagnostic value for prostate 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 score0.018
Threshold uncertainty score0.297

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 head0.015
GPT teacher head0.229
Teacher spread0.214 · 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

Citations128
Published2013
Admission routes2
Has abstractyes

Explore more

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