Exploring the potential of small RNA cargo of extracellular vesicles as a next-generation biomarker for B lymphoblastic leukemia
Bibliographic record
Abstract
Extracellular vesicles (EV) are novel promising sources of biomarkers in human diseases. Despite the promise that EVs hold, there are currently lots of unknowns, which limit our understanding. Therefore, the work presented in this thesis explored questions about EV s in pediatric B cell acute lymphoblastic leukemia (B-ALL) and compared the effect of collection tube treatment on EVs from selected human biofluids. For B-ALL, we wanted to identify a miRNA signature of pediatric B-ALL, which could be explored in EVs. We performed a reanalysis of rniRNA raw data and metanalysis of published rniRNAs data; to identify differentially expressed miRNAs (DEmiRs). Neither the reanalysis nor the metanalyses revealed a consensus miRNA signature. However, we identified promising miRNAs which could be fmther investigated. Next, we compared EVs from blood plasma of pediatric B-ALL patients and non-cancer donors (NCD) for size, concentration, and small RNA profile differences. We found that B-ALL plasma contains more EVs than NCD plasma. Furthermore, RNA-seq analysis of small RNAs revealed a signature of differentially packaged and exclusively packaged RNAs (including miRNAs, lncRNAs, mRNAs, and tRNAs) that distinguish NCD from B-ALL. Next, we investigated the small RNA cargo packaging patterns in EVs from pediatric B-ALL primary lymphoblasts and immo1talized cell lines. We found that RNA biotypes packaged from cells into EVs include mature and precursor miRNAs and fragments of mRNA, lncRNA and tRNA. We also found that pediatric B-ALL primary lymphoblasts and immo1talized cell lines package multiple RNA biotypes, using putative motifs shared by the selectively packaged RNAs. Finally, we compared EV characteristics (size, shape, concentration) and selected EV miRNA levels between non-Streck (EDTA for plasma) and Streck treated collection tubes for urine and blood sample collection. We found that neither treatment affected EV size and concentration in plasma or urine. However, Streck treatment led to reduced RNA cargo in EVs, compared to non-Streck. Taken together, this work provides new insights into EVs in pediatric B-ALL, and blood and urine sample collection for EV profiling. This is knowledge that can facilitate next generation research for EV based biomarkers and monitoring disease burden.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".