P.100 Plasma extracellular vesicle sampling from high grade gliomas demonstrates a small RNA signature indicative of disease and identifies lncRNA RPPH1 as a novel biomarker
Bibliographic record
Abstract
Background: High grade gliomas (HGGs) and cells of the tumour microenvironment secrete extracellular vesicles (EVs) into the plasma that contain genetic and protein cargo which function in paracrine signalling. Isolation of these EVs and their cargo could lead to an important tool that can inform on diagnosis and disease-course of HGGs. Methods: EVs were isolated using Vn96 capture from plasma obtained longitudinally from HGG patients. sRNA was enriched from the EVs, followed by next-generation sequencing, multidimensional scaling, differential expression, and in silico functional enrichment analyses. Results: Over 750 differentially expressed sRNA were identified between HGG and controls. Pathway analysis revealed miRNA highly enriched in both EV and HGG pathways demonstrating the validity of results in capturing a signal from HGG. Other sRNA included several novel HGG plasma-EV biomarkers including lncRNA RPPH1 , RNY4, and RNY5 . Furthermore, in paired longitudinal patient sampling, RPPH1 informed on surgical resection (decreased on resection) and importantly increased again with clinically defined progression. TCGA analysis demonstrated increased expression of RPPH1 in HGG tissue and additionally, higher expression of RPPH1 was associated with a worse disease-specific prognosis. Conclusions: The present study supports the role of plasma-EV sRNA sampling (and particularly RPPH1 ) as part of a multi-pronged approach to HGG disease course surveillance.
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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) | 0.004 | 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".