Alternative RNA splicing in stem cells and cancer stem cells: Importance of transcript-based expression analysis
Bibliographic record
Abstract
Alternative ribonucleic acid (RNA) splicing can lead to the assembly of different protein isoforms with distinctive functions.The outcome of alternative splicing (AS) can result in a complete loss of function or the acquisition of new functions.There is a gap in knowledge of abnormal RNA splice variants promoting cancer stem cells (CSCs), and their prospective contribution in cancer progression.AS directly regulates the self-renewal features of stem cells (SCs) and stem-like cancer cells.Notably, octamer-binding transcription factor 4A spliced variant of octamerbinding transcription factor 4 contributes to maintaining stemness properties in both SCs and CSCs.The epithelial to mesenchymal transition pathway regulates the AS events in CSCs to maintain stemness.The alternative spliced variants of CSCs markers, including cluster of differentiation 44, aldehyde dehydrogenase, and doublecortin-like kinase, α6β1 integrin, have pivotal roles in increasing selfrenewal properties and maintaining the pluripotency of CSCs.Various splicing analysis tools are considered in this study.LeafCutter software can be considered as the best tool for differential splicing analysis and identification of the type of splicing events.Additionally, LeafCutter can be used for efficient mapping splicing quantitative trait loci.Altogether, the accumulating evidence re-enforces Ebrahimie E et al.Alternative RNA splicing in stem cells WJSC https://www.wjgnet.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".