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Record W3112438754 · doi:10.21873/anticanres.14689

Mir-139 Regulates Autophagy in Prostate Cancer Cells Through Beclin-1 and mTOR Signaling Proteins

2020· article· en· W3112438754 on OpenAlexaff
Robert K. Nam, Tania Benatar, Yutaka Amemiya, Christopher Sherman, Arun Seth

Bibliographic record

VenueAnticancer Research · 2020
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsHealth Sciences CentreSunnybrook HospitalUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsDU145AutophagyLNCaPPI3K/AKT/mTOR pathwayProstate cancerCancer researchCell biologyDownregulation and upregulationCancer cellTransfectionChemistryApoptosisBiologyCancerSignal transductionCell cultureBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND/AIM: We previously identified a panel of five miRNAs (including miR-139) associated with biochemical recurrence and metastasis in prostate cancer patients. MATERIALS AND METHODS: We examined miR-139 transfected PC3, DU145 and LNCaP cells by morphology as well as by cell-based assays, confocal microscopy and immunoblotting. RESULTS: We found that treatment of prostate cancer cells with miR-139 resulted in phenotypic changes characteristic of autophagic cells. MiR-139 increased the autophagy-related conversion of the microtubule-associated protein light chain 3 (LC3-I to LC3-II) that was specifically inhibited by the miR-139 antagomir. The upregulation of LC3 II was further confirmed by confocal microscopy. miR-139 regulated activation of both mTOR and Beclin1 the two important autophagy-related molecules. We found that upon miR-139 treatment, the cargo adaptor protein p62 which is degraded during autophagy, accumulates. CONCLUSION: These results suggest that miR-139 is inducing autophagic flux blockade leading to apoptosis in prostate cancer cells through the mTOR and Beclin-1 proteins.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003

Distilled classifier scores by category (both heads)

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 head0.098
GPT teacher head0.416
Teacher spread0.318 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2020
Admission routes1
Has abstractyes

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