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Record W3083569853 · doi:10.1158/1538-7445.am2020-276

Abstract 276: Mir-139 regulates autophagy in prostate cancer cells through Beclin-1 and mTOR signaling proteins

2020· article· en· W3083569853 on OpenAlexaff
Tania Benatar, Robert K. Nam, Christopher Sherman, Yutaka Amemiya, Arun Seth

Bibliographic record

VenueCancer Research · 2020
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsLNCaPDU145Prostate cancerCancer researchAutophagyPI3K/AKT/mTOR pathwayDownregulation and upregulationmicroRNACancer cellCancerBiologyMedicineCell biologySignal transductionInternal medicineApoptosisBiochemistryGene

Abstract

fetched live from OpenAlex

Abstract Background: Prostate cancer represents the second leading cause of cancer death in men and develops as a result of the accumulation of genetic and epigenetic alterations. We previously identified a panel of five miRNAs associated with biochemical recurrence and metastasis following prostatectomy from prostate cancer patients using NGS-based whole miRNome sequencing and qPCR-based validation analysis. We have shown that expression of miR-139, one of the downregulated miRNAs from this panel, was associated with improved prognosis in patients with localized prostate cancer. Furthermore, miR-139 overexpression inhibited prostate cancer cell growth through signaling pathways mediated in part through downregulation of AXL and IGF1R. Here, we further investigated the molecular mechanisms by which miR-139 could inhibit prostate cancer cell growth. 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 and Discussion: We found that treatment of prostate cancer cells with miR-139 resulted in phenotypic changes characteristic of autophagic cells. Conversion of the microtubule-associated protein light chain 3 (LC3-I to LC3-II) was used to monitor autophagy. We observed that the treatment of PC3 and LNCaP cells with miR-139 increased the conversion of LC3-I to LC3-II protein that was specifically inhibited by addition of miR-139 antagomir. The upregulation of LC3 II was further confirmed by confocal microscopy of PC3 cells treated with miR-139. Mammalian target of rapamycin (mTOR) and Beclin1 are two important autophagy-related molecules playing significant roles in different stages. mTOR negatively regulates autophagy machinery. Treatment of cells with miR-139 inhibited both activation and expression of mTOR. We also observed increased levels of phospho-Beclin 1 in PC3 cells treated with mir-139. Beclin 1 is a scaffold protein that assembles components for promoting or inhibiting autophagy and its phosphorylation controls autophagy. The cargo adaptor protein, p62/SQSTM1, interacts with autophagic substrates, delivering them to the autophagosome for degradation, and is degraded during autophagy. We found that during early treatment of cells with miR-139, p62 expression is inhibited, while at later stages, p62 expression accumulates, suggesting that autophagic flux may be blocked. These results suggest that miR-139 is regulating autophagy in prostate cancer cells at least in part through the mTOR and Beclin-1 proteins. Citation Format: Tania C. Benatar, Robert Nam, Christopher Sherman, Yutaka Amemiya, Arun Seth. Mir-139 regulates autophagy in prostate cancer cells through Beclin-1 and mTOR signaling proteins [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 276.

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 score0.003
Threshold uncertainty score0.009

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.093
GPT teacher head0.412
Teacher spread0.320 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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