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Record W4400728053 · doi:10.1038/s41420-024-02097-x

Dysregulation of tRNA methylation in cancer: Mechanisms and targeting therapeutic strategies

2024· review· en· W4400728053 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCell Death Discovery · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsUniversity of Alberta
FundersHubei UniversityHubei University of TechnologyNational Natural Science Foundation of ChinaUniversity of Alberta
KeywordsTransfer RNAMethylationTranslation (biology)MethyltransferaseReprogrammingBiologyAutophagyCancer researchCancerRNARNA methylationEpigeneticsProtein biosynthesismicroRNAComputational biologyMessenger RNAGeneGenetics

Abstract

fetched live from OpenAlex

tRNA is the RNA type that undergoes the most modifications among known RNA, and in recent years, tRNA methylation has emerged as a crucial process in regulating gene translation. Dysregulation of tRNA abundance occurs in cancer cells, along with increased expression and activity of tRNA methyltransferases to raise the level of tRNA modification and stability. This leads to hijacking of translation and synthesis of multiple proteins associated with tumor proliferation, metastasis, invasion, autophagy, chemotherapy resistance, and metabolic reprogramming. In this review, we provide an overview of current research on tRNA methylation in cancer to clarify its involvement in human malignancies and establish a theoretical framework for future therapeutic interventions targeting tRNA methylation processes.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.954
Threshold uncertainty score0.651

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 head0.035
GPT teacher head0.333
Teacher spread0.298 · 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