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Record W4295182896 · doi:10.1038/s41419-022-05128-6

Deubiquitinase OTUD5 modulates mTORC1 signaling to promote bladder cancer progression

2022· article· en· W4295182896 on OpenAlexaff
Tao Hou, Weichao Dan, Tianjie Liu, Bo Liu, Yi Wei, Chenyang Yue, Taotao Que, Bohan Ma, Yuzeshi Lei, Zixi Wang, Jin Zeng, Yizeng Fan, Lei Li

Bibliographic record

VenueCell Death and Disease · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsYork University
FundersNational Natural Science Foundation of China
KeywordsPI3K/AKT/mTOR pathwayDeubiquitinating enzymemTORC1Cancer researchUbiquitin ligaseRegulatorCancerBiologySignal transductionBladder cancerTumor progressionRPTORUbiquitinCell biologyBiochemistryGeneticsGene

Abstract

fetched live from OpenAlex

The mechanistic (formally "mammalian") target of rapamycin (mTOR) pathway serves as a crucial regulator of various biological processes such as cell growth and cancer progression. In bladder cancer, recent discoveries showing the cancer-promoting role of mTOR complex 1 have attracted wide attention. However, the regulation of mTOR signaling in bladder cancer is complicated and the underlying mechanism remains elusive. Here, we report that the deubiquitinating enzyme, ovarian tumor domain-containing protein 5 (OTUD5), can activate the mTOR signaling pathway, promote cancer progression, and show its oncogenic potential in bladder cancer. In our study, we found that OTUD5 deubiquitinated a RING-type E3 ligase, RNF186, and stabilized its function. In addition, the stabilization of RNF186 further led to the degradation of sestrin2, which is an inhibitor of the mTOR signaling pathway. Together, we provide novel insights into the pathogenesis of bladder cancer and first prove that OTUD5 can promote bladder cancer progression through the OTUD5-RNF186-sestrin2-mTOR axis, which may be exploited in the future for the diagnosis and treatment of this malignancy.

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 score0.121
Threshold uncertainty score0.620

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 head0.013
GPT teacher head0.262
Teacher spread0.249 · 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

Citations43
Published2022
Admission routes1
Has abstractyes

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