Detection of circular RNAs and their potential as biomarkers predictive of drug response
Bibliographic record
Abstract
ABSTRACT The introduction of high-throughput sequencing technologies has allowed for comprehensive RNA species detection, both coding and non-coding, which opened new avenues for the discovery of predictive and prognostic biomarkers. However the consistency of the detection of different RNA species depends on the RNA selection protocol used for RNA-sequencing. While preliminary reports indicated that non-coding RNAs, in particular circular RNAs, constitute a rich source of biomarkers predictive of drug response, the reproducibility of this novel class of biomarkers has not been rigorously investigated. To address this issue, we assessed the inter- lab consistency of circular RNA expression in cell lines profiled in large pharmacogenomic datasets. We found that circular RNA expression quantified from rRNA-depleted RNA-seq data is stable and yields robust prognostic markers in cancer. On the other hand, quantification of the expression of circular RNA from poly(A)-selected RNA-seq data yields highly inconsistent results, calling into question results from previous studies reporting their potential as predictive biomarkers in cancer. We have also identified median expression of transcripts and transcript length as potential factors influencing the consistency of RNA detection. Our study provides a framework to quantitatively assess the stability of coding and non-coding RNA expression through the analysis of biological replicates within and across independent studies.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".