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Abstract B021: tRNA fragments extracted from patient plasma extra cellular vesicles display potential as biomarkers for high-grade glioma

2024· article· en· W4404305779 on OpenAlexaff
Thomson Phinney, Ali H. Alwadei, Gabriel Wajnberg, Jae Ho Han, Simi Chacko, Kathleen M. Attwood, Jeremy Roy, Adriennne Weeks

Bibliographic record

VenueClinical Cancer Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsBeatrice Hunter Cancer Research InstituteAtlantic Cancer Research InstituteDalhousie University
Fundersnot available
KeywordsGliomaVesicleCancer researchChemistryMedicinePathologyBiologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract Transfer RNA (tRNA) are non-coding RNAs that bring amino acids to ribosomes during translation. Recent studies have shown that cleaved fragments derived from tRNA (tRFs) serve regulatory roles in epigenetics, signaling, transcription, and translation. tRFs can be released from cells and can be found in extracellular vesicles to perform some aspects of paracrine signaling. Utilizing high throughput small RNA sequencing, we have identified over 750 uniquely or multi-mapped (6.9% of total sRNA mapped reads) tRFs in extracellular vesicles of high-grade glioma (IDH-WT) patient plasma. By performing differential expression analysis, we identified 6 upregulated tRFs and 36 downregulated tRFs between high-grade glioma plasma EVs and non-cancer controls (n = 14, FDR<0.05). Differentially expressed tRFs (all 42) were further analyzed using bioinformatic techniques (RNAhybrid and IntaRNA) to predict potential gene targets. By performing gene ontology analysis (ShinyGO) we found that 70 of 75 genes related to glioma formation were identified as potential gene targets, indicating a possible role of tRFs in the regulation and formation of gliomas. A number of differentially expressed tRFs were predicted to target cancer-related genes including Metastasis Associated Lung Adenocarcinoma Transcript 1 (MALAT-1). This analysis demonstrates that tRFs may serve as important biomarkers and regulators with therapeutic potential in the future Citation Format: Thomson R Phinney, Ali Alwadei, Gabriel Gabriel Wajnberg, Jae Han, Simi Chacko, Kathleen Attwood, Jeremy Roy, Adriennne Weeks. tRNA fragments extracted from patient plasma extra cellular vesicles display potential as biomarkers for high-grade glioma [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B021.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.083
GPT teacher head0.431
Teacher spread0.348 · 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 designObservational
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
Published2024
Admission routes1
Has abstractyes

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