Patient-matched tumours, plasma, and cell lines reveal tumour microenvironment- and cell culture-specific microRNAs
Bibliographic record
Abstract
MicroRNAs (miRNAs) are small non-coding RNA molecules that are present in all cell types and bodily fluids and are commonly dysregulated in cancer. miRNAs in cancer have been studied by measuring levels in cell lines, tumour tissues, and in circulation; however, no study has specifically investigated miRNA expression in patient-matched samples across all three sample types. Canine osteosarcoma is a well-established spontaneously occurring model of human osteosarcoma for which matched samples are available. We analysed a panel of miRNAs by real-time quantitative PCR and compared across patients and sample types. While some miRNAs are highly expressed in all three sample types, tumour tissue and cell lines had the most in common. There were several miRNAs that were highly expressed in plasma and tumour tissue but not in cell lines and likely represent miRNAs produced in the tumour microenvironment. Two highly expressed miRNAs were exclusive to plasma and are known to be expressed in circulating cells. This study highlights the importance of considering sample type when studying miRNAs in cancer and demonstrates the power of using patient-matched samples.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".