Abstract B021: tRNA fragments extracted from patient plasma extra cellular vesicles display potential as biomarkers for high-grade glioma
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".