Pan-cancer analysis of mRNA stability for decoding tumour post-transcriptional programs
Bibliographic record
Abstract
Abstract RNA stability is a crucial and often overlooked determinant of gene expression. Some of the regulators of mRNA stability are long known as key oncogenic or tumour suppressor factors. Nonetheless, the extent to which mRNA stability contributes to transcriptome remodeling in cancer is unknown, and the factors that modulate mRNA stability during cancer development and progression are largely uncharacterized. Here, by decoupling transcriptional and post-transcriptional effects in RNA-seq data of 7760 samples from 18 cancer types, we present a pan-cancer view of the mRNA stability changes that accompany tumour development and progression. We show that thousands of genes are dysregulated at the mRNA stability level, and identify the potential factors that drive these changes, including >80 RNA-binding proteins (RBPs) and microRNAs (miRNAs). Most RBPs and miRNAs have cancer type-specific activities, but a few show recurrent inactivation across multiple cancers, including the RBFOX family of RBPs and miR-29. Analysis of cell lines with phenotypic activation or inhibition of RBFOX1 and miR-29 confirms their role in modulation of genes that are dysregulated across multiple cancers, with functions in calcium signaling, extracellular matrix organization, and stemness. Overall, our study highlights the critical role of mRNA stability in shaping the tumour transcriptome, with recurrent post-transcriptional changes that are ~30% as frequent as transcriptional events. These results provide a resource for systematic interrogation of cancer-associated stability drivers and pathways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".